File size: 185,598 Bytes
23aa310
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
# coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The Trainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task.
"""

import contextlib
import functools
import glob
import inspect
import math
import os
import random
import re
import shutil
import sys
import time
import warnings
from collections.abc import Mapping
from distutils.util import strtobool
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

from tqdm.auto import tqdm


# Integrations must be imported before ML frameworks:
# isort: off
from transformers.integrations import (
    default_hp_search_backend,
    get_reporting_integration_callbacks,
    hp_params,
    is_fairscale_available,
    is_optuna_available,
    is_ray_tune_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)

# isort: on

import numpy as np
import torch
import torch.distributed as dist
from huggingface_hub import Repository, create_repo
from packaging import version
from torch import nn
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler

from transformers import __version__
from transformers.configuration_utils import PretrainedConfig
from transformers.data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
from transformers.debug_utils import DebugOption, DebugUnderflowOverflow
from transformers.deepspeed import deepspeed_init, is_deepspeed_zero3_enabled
from transformers.dependency_versions_check import dep_version_check
from transformers.modelcard import TrainingSummary
from transformers.modeling_utils import PreTrainedModel, load_sharded_checkpoint, unwrap_model
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.optimization import Adafactor, get_scheduler
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_10, is_torch_less_than_1_11
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_callback import (
    CallbackHandler,
    DefaultFlowCallback,
    PrinterCallback,
    ProgressCallback,
    TrainerCallback,
    TrainerControl,
    TrainerState,
)
from transformers.trainer_pt_utils import (
    DistributedLengthGroupedSampler,
    DistributedSamplerWithLoop,
    DistributedTensorGatherer,
    IterableDatasetShard,
    LabelSmoother,
    LengthGroupedSampler,
    SequentialDistributedSampler,
    ShardSampler,
    distributed_broadcast_scalars,
    distributed_concat,
    find_batch_size,
    get_module_class_from_name,
    get_parameter_names,
    nested_concat,
    nested_detach,
    nested_numpify,
    nested_truncate,
    nested_xla_mesh_reduce,
    reissue_pt_warnings,
)
from transformers.trainer_utils import (
    PREFIX_CHECKPOINT_DIR,
    BestRun,
    EvalLoopOutput,
    EvalPrediction,
    FSDPOption,
    HPSearchBackend,
    HubStrategy,
    IntervalStrategy,
    PredictionOutput,
    RemoveColumnsCollator,
    ShardedDDPOption,
    TrainerMemoryTracker,
    TrainOutput,
    default_compute_objective,
    default_hp_space,
    denumpify_detensorize,
    enable_full_determinism,
    find_executable_batch_size,
    get_last_checkpoint,
    has_length,
    number_of_arguments,
    seed_worker,
    set_seed,
    speed_metrics,
)
from transformers.training_args import OptimizerNames, ParallelMode, TrainingArguments
from transformers.utils import (
    CONFIG_NAME,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    can_return_loss,
    find_labels,
    get_full_repo_name,
    is_accelerate_available,
    is_apex_available,
    is_datasets_available,
    is_in_notebook,
    is_ipex_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_torch_compile_available,
    is_torch_neuroncore_available,
    is_torch_tpu_available,
    logging,
)
from transformers.utils.generic import ContextManagers


_is_native_cpu_amp_available = is_torch_greater_or_equal_than_1_10

DEFAULT_CALLBACKS = [DefaultFlowCallback]
DEFAULT_PROGRESS_CALLBACK = ProgressCallback

if is_in_notebook():
    from transformers.utils.notebook import NotebookProgressCallback

    DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback

if is_apex_available():
    from apex import amp

if is_datasets_available():
    import datasets

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met
    import torch_xla.distributed.parallel_loader as pl

if is_fairscale_available():
    dep_version_check("fairscale")
    import fairscale
    from fairscale.nn.data_parallel import FullyShardedDataParallel as FullyShardedDDP
    from fairscale.nn.data_parallel import ShardedDataParallel as ShardedDDP
    from fairscale.nn.wrap import auto_wrap
    from fairscale.optim import OSS
    from fairscale.optim.grad_scaler import ShardedGradScaler


if is_sagemaker_mp_enabled():
    import smdistributed.modelparallel.torch as smp
    from smdistributed.modelparallel import __version__ as SMP_VERSION

    IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse("1.10")

    from transformers.trainer_pt_utils import smp_forward_backward, smp_forward_only, smp_gather, smp_nested_concat
else:
    IS_SAGEMAKER_MP_POST_1_10 = False


skip_first_batches = None
if is_accelerate_available():
    from accelerate import __version__ as accelerate_version

    if version.parse(accelerate_version) >= version.parse("0.16"):
        from accelerate import skip_first_batches


if TYPE_CHECKING:
    import optuna

logger = logging.get_logger(__name__)


# Name of the files used for checkpointing
TRAINING_ARGS_NAME = "training_args.bin"
TRAINER_STATE_NAME = "trainer_state.json"
OPTIMIZER_NAME = "optimizer.pt"
SCHEDULER_NAME = "scheduler.pt"
SCALER_NAME = "scaler.pt"


class Trainer:
    """
    Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.

    Args:
        model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):
            The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.

            <Tip>

            [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use
            your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers
            models.

            </Tip>

        args ([`TrainingArguments`], *optional*):
            The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the
            `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.
        data_collator (`DataCollator`, *optional*):
            The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will
            default to [`default_data_collator`] if no `tokenizer` is provided, an instance of
            [`DataCollatorWithPadding`] otherwise.
        train_dataset (`torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`, *optional*):
            The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the
            `model.forward()` method are automatically removed.

            Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a
            distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a
            `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will
            manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally
            sets the seed of the RNGs used.
        eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*):
             The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the
             `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each
             dataset prepending the dictionary key to the metric name.
        tokenizer ([`PreTrainedTokenizerBase`], *optional*):
            The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the
            maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
            interrupted training or reuse the fine-tuned model.
        model_init (`Callable[[], PreTrainedModel]`, *optional*):
            A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start
            from a new instance of the model as given by this function.

            The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
            be able to choose different architectures according to hyper parameters (such as layer count, sizes of
            inner layers, dropout probabilities etc).
        compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):
            The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return
            a dictionary string to metric values.
        callbacks (List of [`TrainerCallback`], *optional*):
            A list of callbacks to customize the training loop. Will add those to the list of default callbacks
            detailed in [here](callback).

            If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
        optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*): A tuple
            containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model
            and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
            A function that preprocess the logits right before caching them at each evaluation step. Must take two
            tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
            by this function will be reflected in the predictions received by `compute_metrics`.

            Note that the labels (second parameter) will be `None` if the dataset does not have them.

    Important attributes:

        - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]
          subclass.
        - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the
          original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`,
          the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner
          model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`.
        - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from
          data parallelism, this means some of the model layers are split on different GPUs).
        - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set
          to `False` if model parallel or deepspeed is used, or if the default
          `TrainingArguments.place_model_on_device` is overridden to return `False` .
        - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while
          in `train`)

    """

    from transformers.trainer_pt_utils import _get_learning_rate, log_metrics, metrics_format, save_metrics, save_state

    def __init__(
        self,
        model: Union[PreTrainedModel, nn.Module] = None,
        args: TrainingArguments = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
        tokenizer: Optional[PreTrainedTokenizerBase] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
        callbacks: Optional[List[TrainerCallback]] = None,
        optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        save_prefixencoder: bool = False,
    ):
        self.save_prefixencoder = save_prefixencoder
        if args is None:
            output_dir = "tmp_trainer"
            logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.")
            args = TrainingArguments(output_dir=output_dir)
        self.args = args
        # Seed must be set before instantiating the model when using model
        enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
        self.hp_name = None
        self.deepspeed = None
        self.is_in_train = False

        # memory metrics - must set up as early as possible
        self._memory_tracker = TrainerMemoryTracker(self.args.skip_memory_metrics)
        self._memory_tracker.start()

        # set the correct log level depending on the node
        log_level = args.get_process_log_level()
        logging.set_verbosity(log_level)

        # force device and distributed setup init explicitly
        args._setup_devices

        if model is None:
            if model_init is not None:
                self.model_init = model_init
                model = self.call_model_init()
            else:
                raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument")
        else:
            if model_init is not None:
                warnings.warn(
                    "`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will"
                    " overwrite your model when calling the `train` method. This will become a fatal error in the next"
                    " release.",
                    FutureWarning,
                )
            self.model_init = model_init

        if model.__class__.__name__ in MODEL_MAPPING_NAMES:
            raise ValueError(
                f"The model you have picked ({model.__class__.__name__}) cannot be used as is for training: it only "
                "computes hidden states and does not accept any labels. You should choose a model with a head "
                "suitable for your task like any of the `AutoModelForXxx` listed at "
                "https://huggingface.co/docs/transformers/model_doc/auto."
            )

        if hasattr(model, "is_parallelizable") and model.is_parallelizable and model.model_parallel:
            self.is_model_parallel = True
        else:
            self.is_model_parallel = False

        # At this stage the model is already loaded
        if getattr(model, "is_loaded_in_8bit", False):
            if getattr(model, "_is_int8_training_enabled", False):
                logger.info(
                    "The model is loaded in 8-bit precision. To train this model you need to add additional modules"
                    " inside the model such as adapters using `peft` library and freeze the model weights. Please"
                    " check "
                    " the examples in https://github.com/huggingface/peft for more details."
                )
            else:
                raise ValueError(
                    "The model you want to train is loaded in 8-bit precision.  if you want to fine-tune an 8-bit"
                    " model, please make sure that you have installed `bitsandbytes>=0.37.0`. "
                )

        # Setup Sharded DDP training
        self.sharded_ddp = None
        if len(args.sharded_ddp) > 0:
            if args.deepspeed:
                raise ValueError(
                    "Using --sharded_ddp xxx together with --deepspeed is not possible, deactivate one of those flags."
                )
            if len(args.fsdp) > 0:
                raise ValueError(
                    "Using --sharded_ddp xxx together with --fsdp is not possible, deactivate one of those flags."
                )

            if args.local_rank == -1:
                raise ValueError("Using sharded DDP only works in distributed training.")
            elif not is_fairscale_available():
                raise ImportError("Sharded DDP training requires fairscale: `pip install fairscale`.")
            elif ShardedDDPOption.SIMPLE not in args.sharded_ddp and FullyShardedDDP is None:
                raise ImportError(
                    "Sharded DDP in a mode other than simple training requires fairscale version >= 0.3, found "
                    f"{fairscale.__version__}. Upgrade your fairscale library: `pip install --upgrade fairscale`."
                )
            elif ShardedDDPOption.SIMPLE in args.sharded_ddp:
                self.sharded_ddp = ShardedDDPOption.SIMPLE
            elif ShardedDDPOption.ZERO_DP_2 in args.sharded_ddp:
                self.sharded_ddp = ShardedDDPOption.ZERO_DP_2
            elif ShardedDDPOption.ZERO_DP_3 in args.sharded_ddp:
                self.sharded_ddp = ShardedDDPOption.ZERO_DP_3

        self.fsdp = None
        if len(args.fsdp) > 0:
            if args.deepspeed:
                raise ValueError(
                    "Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags."
                )
            if not args.fsdp_config["xla"] and args.local_rank == -1:
                raise ValueError("Using fsdp only works in distributed training.")

            # dep_version_check("torch>=1.12.0")
            # Would have to update setup.py with torch>=1.12.0
            # which isn't ideally given that it will force people not using FSDP to also use torch>=1.12.0
            # below is the current alternative.
            if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.12.0"):
                raise ValueError("FSDP requires PyTorch >= 1.12.0")

            from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch, ShardingStrategy

            if FSDPOption.FULL_SHARD in args.fsdp:
                self.fsdp = ShardingStrategy.FULL_SHARD
            elif FSDPOption.SHARD_GRAD_OP in args.fsdp:
                self.fsdp = ShardingStrategy.SHARD_GRAD_OP
            elif FSDPOption.NO_SHARD in args.fsdp:
                self.fsdp = ShardingStrategy.NO_SHARD

            self.backward_prefetch = BackwardPrefetch.BACKWARD_PRE
            if "backward_prefetch" in self.args.fsdp_config and "backward_pos" not in self.backward_prefetch:
                self.backward_prefetch = BackwardPrefetch.BACKWARD_POST

            self.forword_prefetch = False
            if self.args.fsdp_config.get("forword_prefect", False):
                self.forword_prefetch = True

            self.limit_all_gathers = False
            if self.args.fsdp_config.get("limit_all_gathers", False):
                self.limit_all_gathers = True

        # one place to sort out whether to place the model on device or not
        # postpone switching model to cuda when:
        # 1. MP - since we are trying to fit a much bigger than 1 gpu model
        # 2. fp16-enabled DeepSpeed loads the model in half the size and it doesn't need .to() anyway,
        #    and we only use deepspeed for training at the moment
        # 3. full bf16 or fp16 eval - since the model needs to be cast to the right dtype first
        # 4. Sharded DDP - same as MP
        # 5. FSDP - same as MP
        self.place_model_on_device = args.place_model_on_device
        if (
            self.is_model_parallel
            or args.deepspeed
            or ((args.fp16_full_eval or args.bf16_full_eval) and not args.do_train)
            or (self.sharded_ddp in [ShardedDDPOption.ZERO_DP_2, ShardedDDPOption.ZERO_DP_3])
            or (self.fsdp is not None)
        ):
            self.place_model_on_device = False

        default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer)
        self.data_collator = data_collator if data_collator is not None else default_collator
        self.train_dataset = train_dataset
        self.eval_dataset = eval_dataset
        self.tokenizer = tokenizer

        if self.place_model_on_device and not getattr(model, "is_loaded_in_8bit", False):
            self._move_model_to_device(model, args.device)

        # Force n_gpu to 1 to avoid DataParallel as MP will manage the GPUs
        if self.is_model_parallel:
            self.args._n_gpu = 1

        # later use `self.model is self.model_wrapped` to check if it's wrapped or not
        self.model_wrapped = model
        self.model = model

        self.compute_metrics = compute_metrics
        self.preprocess_logits_for_metrics = preprocess_logits_for_metrics
        self.optimizer, self.lr_scheduler = optimizers
        if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None):
            raise RuntimeError(
                "Passing a `model_init` is incompatible with providing the `optimizers` argument. "
                "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
            )
        if is_torch_tpu_available() and self.optimizer is not None:
            for param in self.model.parameters():
                model_device = param.device
                break
            for param_group in self.optimizer.param_groups:
                if len(param_group["params"]) > 0:
                    optimizer_device = param_group["params"][0].device
                    break
            if model_device != optimizer_device:
                raise ValueError(
                    "The model and the optimizer parameters are not on the same device, which probably means you"
                    " created an optimizer around your model **before** putting on the device and passing it to the"
                    " `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and"
                    " `model.to(xm.xla_device())` is performed before the optimizer creation in your script."
                )
        if ((self.sharded_ddp is not None) or args.deepspeed or (self.fsdp is not None)) and (
            self.optimizer is not None or self.lr_scheduler is not None
        ):
            raise RuntimeError(
                "Passing `optimizers` is not allowed if Fairscale, Deepspeed or PyTorch FSDP is enabled."
                "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
            )
        default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
        callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
        self.callback_handler = CallbackHandler(
            callbacks, self.model, self.tokenizer, self.optimizer, self.lr_scheduler
        )
        self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)

        # Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
        self._loggers_initialized = False

        # Create clone of distant repo and output directory if needed
        if self.args.push_to_hub:
            self.init_git_repo(at_init=True)
            # In case of pull, we need to make sure every process has the latest.
            if is_torch_tpu_available():
                xm.rendezvous("init git repo")
            elif args.local_rank != -1:
                dist.barrier()

        if self.args.should_save:
            os.makedirs(self.args.output_dir, exist_ok=True)

        if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
            raise ValueError("The `data_collator` should be a simple callable (function, class with `__call__`).")

        if args.max_steps > 0:
            logger.info("max_steps is given, it will override any value given in num_train_epochs")

        if train_dataset is not None and not has_length(train_dataset) and args.max_steps <= 0:
            raise ValueError("train_dataset does not implement __len__, max_steps has to be specified")

        if (
            train_dataset is not None
            and isinstance(train_dataset, torch.utils.data.IterableDataset)
            and args.group_by_length
        ):
            raise ValueError("the `--group_by_length` option is only available for `Dataset`, not `IterableDataset")

        self._signature_columns = None

        # Mixed precision setup
        self.use_apex = False
        self.use_cuda_amp = False
        self.use_cpu_amp = False

        # Mixed precision setup for SageMaker Model Parallel
        if is_sagemaker_mp_enabled():
            # BF16 + model parallelism in SageMaker: currently not supported, raise an error
            if args.bf16:
                raise ValueError("SageMaker Model Parallelism does not support BF16 yet. Please use FP16 instead ")

            if IS_SAGEMAKER_MP_POST_1_10:
                # When there's mismatch between SMP config and trainer argument, use SMP config as truth
                if args.fp16 != smp.state.cfg.fp16:
                    logger.warning(
                        f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16},"
                        f"but FP16 provided in trainer argument is {args.fp16},"
                        f"setting to {smp.state.cfg.fp16}"
                    )
                    args.fp16 = smp.state.cfg.fp16
            else:
                # smp < 1.10 does not support fp16 in trainer.
                if hasattr(smp.state.cfg, "fp16"):
                    logger.warning(
                        f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16}, "
                        "but SageMaker Model Parallelism < 1.10 does not support FP16 in trainer."
                    )

        if args.fp16 or args.bf16:
            if args.half_precision_backend == "auto":
                if args.device == torch.device("cpu"):
                    if args.fp16:
                        raise ValueError("Tried to use `fp16` but it is not supported on cpu")
                    elif _is_native_cpu_amp_available:
                        args.half_precision_backend = "cpu_amp"
                    else:
                        raise ValueError("Tried to use cpu amp but native cpu amp is not available")
                else:
                    args.half_precision_backend = "cuda_amp"

            logger.info(f"Using {args.half_precision_backend} half precision backend")

        self.do_grad_scaling = False
        if (args.fp16 or args.bf16) and not (args.deepspeed or is_sagemaker_mp_enabled() or is_torch_tpu_available()):
            # deepspeed and SageMaker Model Parallel manage their own half precision
            if args.half_precision_backend == "cuda_amp":
                self.use_cuda_amp = True
                self.amp_dtype = torch.float16 if args.fp16 else torch.bfloat16
                #  bf16 does not need grad scaling
                self.do_grad_scaling = self.amp_dtype == torch.float16
                if self.do_grad_scaling:
                    if self.sharded_ddp is not None:
                        self.scaler = ShardedGradScaler()
                    elif self.fsdp is not None:
                        from torch.distributed.fsdp.sharded_grad_scaler import (
                            ShardedGradScaler as FSDPShardedGradScaler,
                        )

                        self.scaler = FSDPShardedGradScaler()
                    elif is_torch_tpu_available():
                        from torch_xla.amp import GradScaler

                        self.scaler = GradScaler()
                    else:
                        self.scaler = torch.cuda.amp.GradScaler()
            elif args.half_precision_backend == "cpu_amp":
                self.use_cpu_amp = True
                self.amp_dtype = torch.bfloat16
            else:
                if not is_apex_available():
                    raise ImportError(
                        "Using FP16 with APEX but APEX is not installed, please refer to"
                        " https://www.github.com/nvidia/apex."
                    )
                self.use_apex = True

        # FP16 + model parallelism in SageMaker: gradient clipping does not work for now so we raise a helpful error.
        if (
            is_sagemaker_mp_enabled()
            and self.use_cuda_amp
            and args.max_grad_norm is not None
            and args.max_grad_norm > 0
        ):
            raise ValueError(
                "SageMaker Model Parallelism in mixed precision mode does not support gradient clipping yet. Pass "
                "along 'max_grad_norm': 0 in your hyperparameters."
            )

        # Label smoothing
        if self.args.label_smoothing_factor != 0:
            self.label_smoother = LabelSmoother(epsilon=self.args.label_smoothing_factor)
        else:
            self.label_smoother = None

        self.state = TrainerState(
            is_local_process_zero=self.is_local_process_zero(),
            is_world_process_zero=self.is_world_process_zero(),
        )

        self.control = TrainerControl()
        # Internal variable to count flos in each process, will be accumulated in `self.state.total_flos` then
        # returned to 0 every time flos need to be logged
        self.current_flos = 0
        self.hp_search_backend = None
        self.use_tune_checkpoints = False
        default_label_names = find_labels(self.model.__class__)
        self.label_names = default_label_names if self.args.label_names is None else self.args.label_names
        self.can_return_loss = can_return_loss(self.model.__class__)
        self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)

        # Internal variables to keep track of the original batch size
        self._train_batch_size = args.train_batch_size

        # very last
        self._memory_tracker.stop_and_update_metrics()

        # torch.compile
        if args.torch_compile and not is_torch_compile_available():
            raise RuntimeError("Using torch.compile requires PyTorch 2.0 or higher.")

    def add_callback(self, callback):
        """
        Add a callback to the current list of [`~transformer.TrainerCallback`].

        Args:
           callback (`type` or [`~transformer.TrainerCallback`]):
               A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the
               first case, will instantiate a member of that class.
        """
        self.callback_handler.add_callback(callback)

    def pop_callback(self, callback):
        """
        Remove a callback from the current list of [`~transformer.TrainerCallback`] and returns it.

        If the callback is not found, returns `None` (and no error is raised).

        Args:
           callback (`type` or [`~transformer.TrainerCallback`]):
               A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the
               first case, will pop the first member of that class found in the list of callbacks.

        Returns:
            [`~transformer.TrainerCallback`]: The callback removed, if found.
        """
        return self.callback_handler.pop_callback(callback)

    def remove_callback(self, callback):
        """
        Remove a callback from the current list of [`~transformer.TrainerCallback`].

        Args:
           callback (`type` or [`~transformer.TrainerCallback`]):
               A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the
               first case, will remove the first member of that class found in the list of callbacks.
        """
        self.callback_handler.remove_callback(callback)

    def _move_model_to_device(self, model, device):
        model = model.to(device)
        # Moving a model to an XLA device disconnects the tied weights, so we have to retie them.
        if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, "tie_weights"):
            model.tie_weights()

    def _set_signature_columns_if_needed(self):
        if self._signature_columns is None:
            # Inspect model forward signature to keep only the arguments it accepts.
            signature = inspect.signature(self.model.forward)
            self._signature_columns = list(signature.parameters.keys())
            # Labels may be named label or label_ids, the default data collator handles that.
            self._signature_columns += list(set(["label", "label_ids"] + self.label_names))

    def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
        if not self.args.remove_unused_columns:
            return dataset
        self._set_signature_columns_if_needed()
        signature_columns = self._signature_columns

        ignored_columns = list(set(dataset.column_names) - set(signature_columns))
        if len(ignored_columns) > 0:
            dset_description = "" if description is None else f"in the {description} set"
            logger.info(
                f"The following columns {dset_description} don't have a corresponding argument in "
                f"`{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}."
                f" If {', '.join(ignored_columns)} are not expected by `{self.model.__class__.__name__}.forward`, "
                " you can safely ignore this message."
            )

        columns = [k for k in signature_columns if k in dataset.column_names]

        if version.parse(datasets.__version__) < version.parse("1.4.0"):
            dataset.set_format(
                type=dataset.format["type"], columns=columns, format_kwargs=dataset.format["format_kwargs"]
            )
            return dataset
        else:
            return dataset.remove_columns(ignored_columns)

    def _get_collator_with_removed_columns(
        self, data_collator: Callable, description: Optional[str] = None
    ) -> Callable:
        """Wrap the data collator in a callable removing unused columns."""
        if not self.args.remove_unused_columns:
            return data_collator
        self._set_signature_columns_if_needed()
        signature_columns = self._signature_columns

        remove_columns_collator = RemoveColumnsCollator(
            data_collator=data_collator,
            signature_columns=signature_columns,
            logger=logger,
            description=description,
            model_name=self.model.__class__.__name__,
        )
        return remove_columns_collator

    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
        if self.train_dataset is None or not has_length(self.train_dataset):
            return None

        generator = None
        if self.args.world_size <= 1:
            generator = torch.Generator()
            # for backwards compatibility, we generate a seed here (which is sampled from a generator seeded with
            # `args.seed`) if data_seed isn't provided.
            # Further on in this method, we default to `args.seed` instead.
            if self.args.data_seed is None:
                seed = int(torch.empty((), dtype=torch.int64).random_().item())
            else:
                seed = self.args.data_seed
            generator.manual_seed(seed)

        seed = self.args.data_seed if self.args.data_seed is not None else self.args.seed

        # Build the sampler.
        if self.args.group_by_length:
            if is_datasets_available() and isinstance(self.train_dataset, datasets.Dataset):
                lengths = (
                    self.train_dataset[self.args.length_column_name]
                    if self.args.length_column_name in self.train_dataset.column_names
                    else None
                )
            else:
                lengths = None
            model_input_name = self.tokenizer.model_input_names[0] if self.tokenizer is not None else None
            if self.args.world_size <= 1:
                return LengthGroupedSampler(
                    self.args.train_batch_size * self.args.gradient_accumulation_steps,
                    dataset=self.train_dataset,
                    lengths=lengths,
                    model_input_name=model_input_name,
                    generator=generator,
                )
            else:
                return DistributedLengthGroupedSampler(
                    self.args.train_batch_size * self.args.gradient_accumulation_steps,
                    dataset=self.train_dataset,
                    num_replicas=self.args.world_size,
                    rank=self.args.process_index,
                    lengths=lengths,
                    model_input_name=model_input_name,
                    seed=seed,
                )

        else:
            if self.args.world_size <= 1:
                return RandomSampler(self.train_dataset, generator=generator)
            elif (
                self.args.parallel_mode in [ParallelMode.TPU, ParallelMode.SAGEMAKER_MODEL_PARALLEL]
                and not self.args.dataloader_drop_last
            ):
                # Use a loop for TPUs when drop_last is False to have all batches have the same size.
                return DistributedSamplerWithLoop(
                    self.train_dataset,
                    batch_size=self.args.per_device_train_batch_size,
                    num_replicas=self.args.world_size,
                    rank=self.args.process_index,
                    seed=seed,
                )
            else:
                return DistributedSampler(
                    self.train_dataset,
                    num_replicas=self.args.world_size,
                    rank=self.args.process_index,
                    seed=seed,
                )

    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training [`~torch.utils.data.DataLoader`].

        Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
        training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.
        """
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")

        train_dataset = self.train_dataset
        data_collator = self.data_collator
        if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
            train_dataset = self._remove_unused_columns(train_dataset, description="training")
        else:
            data_collator = self._get_collator_with_removed_columns(data_collator, description="training")

        if isinstance(train_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                train_dataset = IterableDatasetShard(
                    train_dataset,
                    batch_size=self._train_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )

            return DataLoader(
                train_dataset,
                batch_size=self._train_batch_size,
                collate_fn=data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        train_sampler = self._get_train_sampler()

        return DataLoader(
            train_dataset,
            batch_size=self._train_batch_size,
            sampler=train_sampler,
            collate_fn=data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
            worker_init_fn=seed_worker,
        )

    def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.Sampler]:
        # Deprecated code
        if self.args.use_legacy_prediction_loop:
            if is_torch_tpu_available():
                return SequentialDistributedSampler(
                    eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()
                )
            elif is_sagemaker_mp_enabled():
                return SequentialDistributedSampler(
                    eval_dataset,
                    num_replicas=smp.dp_size(),
                    rank=smp.dp_rank(),
                    batch_size=self.args.per_device_eval_batch_size,
                )
            elif self.args.local_rank != -1:
                return SequentialDistributedSampler(eval_dataset)
            else:
                return SequentialSampler(eval_dataset)

        if self.args.world_size <= 1:
            return SequentialSampler(eval_dataset)
        else:
            return ShardSampler(
                eval_dataset,
                batch_size=self.args.per_device_eval_batch_size,
                num_processes=self.args.world_size,
                process_index=self.args.process_index,
            )

    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
        """
        Returns the evaluation [`~torch.utils.data.DataLoader`].

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            eval_dataset (`torch.utils.data.Dataset`, *optional*):
                If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
                by the `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        if eval_dataset is None and self.eval_dataset is None:
            raise ValueError("Trainer: evaluation requires an eval_dataset.")
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        data_collator = self.data_collator

        if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
            eval_dataset = self._remove_unused_columns(eval_dataset, description="evaluation")
        else:
            data_collator = self._get_collator_with_removed_columns(data_collator, description="evaluation")

        if isinstance(eval_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                eval_dataset = IterableDatasetShard(
                    eval_dataset,
                    batch_size=self.args.per_device_eval_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )
            return DataLoader(
                eval_dataset,
                batch_size=self.args.eval_batch_size,
                collate_fn=data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        eval_sampler = self._get_eval_sampler(eval_dataset)

        return DataLoader(
            eval_dataset,
            sampler=eval_sampler,
            batch_size=self.args.eval_batch_size,
            collate_fn=data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
        )

    def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
        """
        Returns the test [`~torch.utils.data.DataLoader`].

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            test_dataset (`torch.utils.data.Dataset`, *optional*):
                The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the
                `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        data_collator = self.data_collator

        if is_datasets_available() and isinstance(test_dataset, datasets.Dataset):
            test_dataset = self._remove_unused_columns(test_dataset, description="test")
        else:
            data_collator = self._get_collator_with_removed_columns(data_collator, description="test")

        if isinstance(test_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                test_dataset = IterableDatasetShard(
                    test_dataset,
                    batch_size=self.args.eval_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )
            return DataLoader(
                test_dataset,
                batch_size=self.args.eval_batch_size,
                collate_fn=data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        test_sampler = self._get_eval_sampler(test_dataset)

        # We use the same batch_size as for eval.
        return DataLoader(
            test_dataset,
            sampler=test_sampler,
            batch_size=self.args.eval_batch_size,
            collate_fn=data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
        )

    def create_optimizer_and_scheduler(self, num_training_steps: int):
        """
        Setup the optimizer and the learning rate scheduler.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or
        `create_scheduler`) in a subclass.
        """
        self.create_optimizer()
        if IS_SAGEMAKER_MP_POST_1_10 and smp.state.cfg.fp16:
            # If smp >= 1.10 and fp16 is enabled, we unwrap the optimizer
            optimizer = self.optimizer.optimizer
        else:
            optimizer = self.optimizer
        self.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)

    def create_optimizer(self):
        """
        Setup the optimizer.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method in a subclass.
        """
        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model

        if self.optimizer is None:
            decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
            decay_parameters = [name for name in decay_parameters if "bias" not in name]
            optimizer_grouped_parameters = [
                {
                    "params": [
                        p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [
                        p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": 0.0,
                },
            ]

            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)

            if self.sharded_ddp == ShardedDDPOption.SIMPLE:
                self.optimizer = OSS(
                    params=optimizer_grouped_parameters,
                    optim=optimizer_cls,
                    **optimizer_kwargs,
                )
            else:
                self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
                if optimizer_cls.__name__ == "Adam8bit":
                    import bitsandbytes

                    manager = bitsandbytes.optim.GlobalOptimManager.get_instance()

                    skipped = 0
                    for module in opt_model.modules():
                        if isinstance(module, nn.Embedding):
                            skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
                            print(f"skipped {module}: {skipped/2**20}M params")
                            manager.register_module_override(module, "weight", {"optim_bits": 32})
                            logger.debug(f"bitsandbytes: will optimize {module} in fp32")
                    print(f"skipped: {skipped/2**20}M params")

        if is_sagemaker_mp_enabled():
            self.optimizer = smp.DistributedOptimizer(self.optimizer)

        return self.optimizer

    @staticmethod
    def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any, Any]:
        """
        Returns the optimizer class and optimizer parameters based on the training arguments.

        Args:
            args (`transformers.training_args.TrainingArguments`):
                The training arguments for the training session.

        """

        # parse args.optim_args
        optim_args = {}
        if args.optim_args:
            for mapping in args.optim_args.replace(" ", "").split(","):
                key, value = mapping.split("=")
                optim_args[key] = value

        optimizer_kwargs = {"lr": args.learning_rate}

        adam_kwargs = {
            "betas": (args.adam_beta1, args.adam_beta2),
            "eps": args.adam_epsilon,
        }
        if args.optim == OptimizerNames.ADAFACTOR:
            optimizer_cls = Adafactor
            optimizer_kwargs.update({"scale_parameter": False, "relative_step": False})
        elif args.optim == OptimizerNames.ADAMW_HF:
            from transformers.optimization import AdamW

            optimizer_cls = AdamW
            optimizer_kwargs.update(adam_kwargs)
        elif args.optim in [OptimizerNames.ADAMW_TORCH, OptimizerNames.ADAMW_TORCH_FUSED]:
            from torch.optim import AdamW

            optimizer_cls = AdamW
            optimizer_kwargs.update(adam_kwargs)
            if args.optim == OptimizerNames.ADAMW_TORCH_FUSED:
                optimizer_kwargs.update({"fused": True})
        elif args.optim == OptimizerNames.ADAMW_TORCH_XLA:
            try:
                from torch_xla.amp.syncfree import AdamW

                optimizer_cls = AdamW
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError("Trainer failed to import syncfree AdamW from torch_xla.")
        elif args.optim == OptimizerNames.ADAMW_APEX_FUSED:
            try:
                from apex.optimizers import FusedAdam

                optimizer_cls = FusedAdam
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError("Trainer tried to instantiate apex FusedAdam but apex is not installed!")
        elif args.optim == OptimizerNames.ADAMW_BNB:
            try:
                from bitsandbytes.optim import Adam8bit

                optimizer_cls = Adam8bit
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError("Trainer tried to instantiate bnb Adam8bit but bnb is not installed!")
        elif args.optim == OptimizerNames.ADAMW_ANYPRECISION:
            try:
                from torchdistx.optimizers import AnyPrecisionAdamW

                optimizer_cls = AnyPrecisionAdamW
                optimizer_kwargs.update(adam_kwargs)

                # TODO Change dtypes back to M=FP32, Var = BF16, Kahan = False once they can be cast together in torchdistx.
                optimizer_kwargs.update(
                    {
                        "use_kahan_summation": strtobool(optim_args.get("use_kahan_summation", "False")),
                        "momentum_dtype": getattr(torch, optim_args.get("momentum_dtype", "float32")),
                        "variance_dtype": getattr(torch, optim_args.get("variance_dtype", "float32")),
                        "compensation_buffer_dtype": getattr(
                            torch, optim_args.get("compensation_buffer_dtype", "bfloat16")
                        ),
                    }
                )
            except ImportError:
                raise ValueError("Please install https://github.com/pytorch/torchdistx")
        elif args.optim == OptimizerNames.SGD:
            optimizer_cls = torch.optim.SGD
        elif args.optim == OptimizerNames.ADAGRAD:
            optimizer_cls = torch.optim.Adagrad
        else:
            raise ValueError(f"Trainer cannot instantiate unsupported optimizer: {args.optim}")
        return optimizer_cls, optimizer_kwargs

    def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):
        """
        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
        passed as an argument.

        Args:
            num_training_steps (int): The number of training steps to do.
        """
        if self.lr_scheduler is None:
            self.lr_scheduler = get_scheduler(
                self.args.lr_scheduler_type,
                optimizer=self.optimizer if optimizer is None else optimizer,
                num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                num_training_steps=num_training_steps,
            )
        return self.lr_scheduler

    def num_examples(self, dataloader: DataLoader) -> int:
        """
        Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When
        dataloader.dataset does not exist or has no length, estimates as best it can
        """
        try:
            dataset = dataloader.dataset
            # Special case for IterableDatasetShard, we need to dig deeper
            if isinstance(dataset, IterableDatasetShard):
                return len(dataloader.dataset.dataset)
            return len(dataloader.dataset)
        except (NameError, AttributeError, TypeError):  # no dataset or length, estimate by length of dataloader
            return len(dataloader) * self.args.per_device_train_batch_size

    def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
        """HP search setup code"""
        self._trial = trial

        if self.hp_search_backend is None or trial is None:
            return
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            params = self.hp_space(trial)
        elif self.hp_search_backend == HPSearchBackend.RAY:
            params = trial
            params.pop("wandb", None)
        elif self.hp_search_backend == HPSearchBackend.SIGOPT:
            params = {k: int(v) if isinstance(v, str) else v for k, v in trial.assignments.items()}
        elif self.hp_search_backend == HPSearchBackend.WANDB:
            params = trial

        for key, value in params.items():
            if not hasattr(self.args, key):
                logger.warning(
                    f"Trying to set {key} in the hyperparameter search but there is no corresponding field in"
                    " `TrainingArguments`."
                )
                continue
            old_attr = getattr(self.args, key, None)
            # Casting value to the proper type
            if old_attr is not None:
                value = type(old_attr)(value)
            setattr(self.args, key, value)
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            logger.info(f"Trial: {trial.params}")
        if self.hp_search_backend == HPSearchBackend.SIGOPT:
            logger.info(f"SigOpt Assignments: {trial.assignments}")
        if self.hp_search_backend == HPSearchBackend.WANDB:
            logger.info(f"W&B Sweep parameters: {trial}")
        if self.args.deepspeed:
            # Rebuild the deepspeed config to reflect the updated training parameters
            from transformers.deepspeed import HfTrainerDeepSpeedConfig

            self.args.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.args.deepspeed)
            self.args.hf_deepspeed_config.trainer_config_process(self.args)

    def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, Any]], step: int, metrics: Dict[str, float]):
        if self.hp_search_backend is None or trial is None:
            return
        self.objective = self.compute_objective(metrics.copy())
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            import optuna

            trial.report(self.objective, step)
            if trial.should_prune():
                self.callback_handler.on_train_end(self.args, self.state, self.control)
                raise optuna.TrialPruned()
        elif self.hp_search_backend == HPSearchBackend.RAY:
            from ray import tune

            if self.control.should_save:
                self._tune_save_checkpoint()
            tune.report(objective=self.objective, **metrics)

    def _tune_save_checkpoint(self):
        from ray import tune

        if not self.use_tune_checkpoints:
            return
        with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
            output_dir = os.path.join(checkpoint_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
            self.save_model(output_dir, _internal_call=True)
            if self.args.should_save:
                self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
                torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))

    def call_model_init(self, trial=None):
        model_init_argcount = number_of_arguments(self.model_init)
        if model_init_argcount == 0:
            model = self.model_init()
        elif model_init_argcount == 1:
            model = self.model_init(trial)
        else:
            raise RuntimeError("model_init should have 0 or 1 argument.")

        if model is None:
            raise RuntimeError("model_init should not return None.")

        return model

    def torch_jit_model_eval(self, model, dataloader, training=False):
        if not training:
            if dataloader is None:
                logger.warning("failed to use PyTorch jit mode due to current dataloader is none.")
                return model
            example_batch = next(iter(dataloader))
            example_batch = self._prepare_inputs(example_batch)
            try:
                jit_model = model.eval()
                with ContextManagers([self.autocast_smart_context_manager(cache_enabled=False), torch.no_grad()]):
                    if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.14.0"):
                        if isinstance(example_batch, dict):
                            jit_model = torch.jit.trace(jit_model, example_kwarg_inputs=example_batch, strict=False)
                        else:
                            jit_model = torch.jit.trace(
                                jit_model,
                                example_kwarg_inputs={key: example_batch[key] for key in example_batch},
                                strict=False,
                            )
                    else:
                        jit_inputs = []
                        for key in example_batch:
                            example_tensor = torch.ones_like(example_batch[key])
                            jit_inputs.append(example_tensor)
                        jit_inputs = tuple(jit_inputs)
                        jit_model = torch.jit.trace(jit_model, jit_inputs, strict=False)
                jit_model = torch.jit.freeze(jit_model)
                with torch.no_grad():
                    jit_model(**example_batch)
                    jit_model(**example_batch)
                model = jit_model
                self.use_cpu_amp = False
                self.use_cuda_amp = False
            except (RuntimeError, TypeError, ValueError, NameError, IndexError) as e:
                logger.warning(f"failed to use PyTorch jit mode due to: {e}.")

        return model

    def ipex_optimize_model(self, model, training=False, dtype=torch.float32):
        if not is_ipex_available():
            raise ImportError(
                "Using IPEX but IPEX is not installed or IPEX's version does not match current PyTorch, please refer"
                " to https://github.com/intel/intel-extension-for-pytorch."
            )

        import intel_extension_for_pytorch as ipex

        if not training:
            model.eval()
            dtype = torch.bfloat16 if not self.is_in_train and self.args.bf16_full_eval else dtype
            # conv_bn_folding is disabled as it fails in symbolic tracing, resulting in ipex warnings
            model = ipex.optimize(model, dtype=dtype, level="O1", conv_bn_folding=False, inplace=not self.is_in_train)
        else:
            if not model.training:
                model.train()
            model, self.optimizer = ipex.optimize(
                model, dtype=dtype, optimizer=self.optimizer, inplace=True, level="O1"
            )

        return model

    def _wrap_model(self, model, training=True, dataloader=None):
        if self.args.torch_compile:
            model = torch.compile(model, backend=self.args.torch_compile_backend, mode=self.args.torch_compile_mode)

        if self.args.use_ipex:
            dtype = torch.bfloat16 if self.use_cpu_amp else torch.float32
            model = self.ipex_optimize_model(model, training, dtype=dtype)

        if is_sagemaker_mp_enabled():
            # Wrapping the base model twice in a DistributedModel will raise an error.
            if isinstance(self.model_wrapped, smp.model.DistributedModel):
                return self.model_wrapped
            return smp.DistributedModel(model, backward_passes_per_step=self.args.gradient_accumulation_steps)

        # already initialized its own DDP and AMP
        if self.deepspeed:
            return self.deepspeed

        # train/eval could be run multiple-times - if already wrapped, don't re-wrap it again
        if unwrap_model(model) is not model:
            return model

        # Mixed precision training with apex (torch < 1.6)
        if self.use_apex and training:
            model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)

        # Multi-gpu training (should be after apex fp16 initialization)
        if self.args.n_gpu > 1:
            model = nn.DataParallel(model)

        if self.args.jit_mode_eval:
            start_time = time.time()
            model = self.torch_jit_model_eval(model, dataloader, training)
            self.jit_compilation_time = round(time.time() - start_time, 4)

        # Note: in torch.distributed mode, there's no point in wrapping the model
        # inside a DistributedDataParallel as we'll be under `no_grad` anyways.
        if not training:
            return model

        # Distributed training (should be after apex fp16 initialization)
        if self.sharded_ddp is not None:
            # Sharded DDP!
            if self.sharded_ddp == ShardedDDPOption.SIMPLE:
                model = ShardedDDP(model, self.optimizer)
            else:
                mixed_precision = self.args.fp16 or self.args.bf16
                cpu_offload = ShardedDDPOption.OFFLOAD in self.args.sharded_ddp
                zero_3 = self.sharded_ddp == ShardedDDPOption.ZERO_DP_3
                # XXX: Breaking the self.model convention but I see no way around it for now.
                if ShardedDDPOption.AUTO_WRAP in self.args.sharded_ddp:
                    model = auto_wrap(model)
                self.model = model = FullyShardedDDP(
                    model,
                    mixed_precision=mixed_precision,
                    reshard_after_forward=zero_3,
                    cpu_offload=cpu_offload,
                ).to(self.args.device)
        # Distributed training using PyTorch FSDP
        elif self.fsdp is not None:
            if not self.args.fsdp_config["xla"]:
                # PyTorch FSDP!
                from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision
                from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
                from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy

                if FSDPOption.OFFLOAD in self.args.fsdp:
                    cpu_offload = CPUOffload(offload_params=True)
                else:
                    cpu_offload = CPUOffload(offload_params=False)

                auto_wrap_policy = None

                if FSDPOption.AUTO_WRAP in self.args.fsdp:
                    if self.args.fsdp_config["fsdp_min_num_params"] > 0:
                        auto_wrap_policy = functools.partial(
                            size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["fsdp_min_num_params"]
                        )
                    elif self.args.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
                        transformer_cls_to_wrap = set()
                        for layer_class in self.args.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]:
                            transformer_cls = get_module_class_from_name(model, layer_class)
                            if transformer_cls is None:
                                raise Exception("Could not find the transformer layer class to wrap in the model.")
                            else:
                                transformer_cls_to_wrap.add(transformer_cls)
                        auto_wrap_policy = functools.partial(
                            transformer_auto_wrap_policy,
                            # Transformer layer class to wrap
                            transformer_layer_cls=transformer_cls_to_wrap,
                        )
                mixed_precision_policy = None
                dtype = None
                if self.args.fp16:
                    dtype = torch.float16
                elif self.args.bf16:
                    dtype = torch.bfloat16
                if dtype is not None:
                    mixed_precision_policy = MixedPrecision(param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype)
                if type(model) != FSDP:
                    # XXX: Breaking the self.model convention but I see no way around it for now.
                    self.model = model = FSDP(
                        model,
                        sharding_strategy=self.fsdp,
                        cpu_offload=cpu_offload,
                        auto_wrap_policy=auto_wrap_policy,
                        mixed_precision=mixed_precision_policy,
                        device_id=self.args.device,
                        backward_prefetch=self.backward_prefetch,
                        forward_prefetch=self.forword_prefetch,
                        limit_all_gathers=self.limit_all_gathers,
                    )
            else:
                try:
                    from torch_xla.distributed.fsdp import XlaFullyShardedDataParallel as FSDP
                    from torch_xla.distributed.fsdp import checkpoint_module
                    from torch_xla.distributed.fsdp.wrap import (
                        size_based_auto_wrap_policy,
                        transformer_auto_wrap_policy,
                    )
                except ImportError:
                    raise ImportError("Missing XLA FSDP related module; please make sure to use torch-xla >= 2.0.")
                auto_wrap_policy = None
                auto_wrapper_callable = None
                if self.args.fsdp_config["fsdp_min_num_params"] > 0:
                    auto_wrap_policy = functools.partial(
                        size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["fsdp_min_num_params"]
                    )
                elif self.args.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
                    transformer_cls_to_wrap = set()
                    for layer_class in self.args.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]:
                        transformer_cls = get_module_class_from_name(model, layer_class)
                        if transformer_cls is None:
                            raise Exception("Could not find the transformer layer class to wrap in the model.")
                        else:
                            transformer_cls_to_wrap.add(transformer_cls)
                    auto_wrap_policy = functools.partial(
                        transformer_auto_wrap_policy,
                        # Transformer layer class to wrap
                        transformer_layer_cls=transformer_cls_to_wrap,
                    )
                fsdp_kwargs = self.args.xla_fsdp_config
                if self.args.fsdp_config["xla_fsdp_grad_ckpt"]:
                    # Apply gradient checkpointing to auto-wrapped sub-modules if specified
                    def auto_wrapper_callable(m, *args, **kwargs):
                        return FSDP(checkpoint_module(m), *args, **kwargs)

                # Wrap the base model with an outer FSDP wrapper
                self.model = model = FSDP(
                    model,
                    auto_wrap_policy=auto_wrap_policy,
                    auto_wrapper_callable=auto_wrapper_callable,
                    **fsdp_kwargs,
                )

                # Patch `xm.optimizer_step` should not reduce gradients in this case,
                # as FSDP does not need gradient reduction over sharded parameters.
                def patched_optimizer_step(optimizer, barrier=False, optimizer_args={}):
                    loss = optimizer.step(**optimizer_args)
                    if barrier:
                        xm.mark_step()
                    return loss

                xm.optimizer_step = patched_optimizer_step
        elif is_sagemaker_dp_enabled():
            model = nn.parallel.DistributedDataParallel(
                model, device_ids=[int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))]
            )
        elif self.args.local_rank != -1:
            kwargs = {}
            if self.args.ddp_find_unused_parameters is not None:
                kwargs["find_unused_parameters"] = self.args.ddp_find_unused_parameters
            elif isinstance(model, PreTrainedModel):
                # find_unused_parameters breaks checkpointing as per
                # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
                kwargs["find_unused_parameters"] = not model.is_gradient_checkpointing
            else:
                kwargs["find_unused_parameters"] = True

            if self.args.ddp_bucket_cap_mb is not None:
                kwargs["bucket_cap_mb"] = self.args.ddp_bucket_cap_mb
            if is_torch_neuroncore_available():
                return model
            model = nn.parallel.DistributedDataParallel(
                model,
                device_ids=[self.args.local_rank] if self.args._n_gpu != 0 else None,
                output_device=self.args.local_rank if self.args._n_gpu != 0 else None,
                **kwargs,
            )

        return model

    def train(
        self,
        resume_from_checkpoint: Optional[Union[str, bool]] = None,
        trial: Union["optuna.Trial", Dict[str, Any]] = None,
        ignore_keys_for_eval: Optional[List[str]] = None,
        **kwargs,
    ):
        """
        Main training entry point.

        Args:
            resume_from_checkpoint (`str` or `bool`, *optional*):
                If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a
                `bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance
                of [`Trainer`]. If present, training will resume from the model/optimizer/scheduler states loaded here.
            trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):
                The trial run or the hyperparameter dictionary for hyperparameter search.
            ignore_keys_for_eval (`List[str]`, *optional*)
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions for evaluation during the training.
            kwargs:
                Additional keyword arguments used to hide deprecated arguments
        """
        if resume_from_checkpoint is False:
            resume_from_checkpoint = None

        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        args = self.args

        self.is_in_train = True

        # do_train is not a reliable argument, as it might not be set and .train() still called, so
        # the following is a workaround:
        if (args.fp16_full_eval or args.bf16_full_eval) and not args.do_train:
            self._move_model_to_device(self.model, args.device)

        if "model_path" in kwargs:
            resume_from_checkpoint = kwargs.pop("model_path")
            warnings.warn(
                "`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` "
                "instead.",
                FutureWarning,
            )
        if len(kwargs) > 0:
            raise TypeError(f"train() received got unexpected keyword arguments: {', '.join(list(kwargs.keys()))}.")
        # This might change the seed so needs to run first.
        self._hp_search_setup(trial)
        self._train_batch_size = self.args.train_batch_size

        # Model re-init
        model_reloaded = False
        if self.model_init is not None:
            # Seed must be set before instantiating the model when using model_init.
            enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
            self.model = self.call_model_init(trial)
            model_reloaded = True
            # Reinitializes optimizer and scheduler
            self.optimizer, self.lr_scheduler = None, None

        # Load potential model checkpoint
        if isinstance(resume_from_checkpoint, bool) and resume_from_checkpoint:
            resume_from_checkpoint = get_last_checkpoint(args.output_dir)
            if resume_from_checkpoint is None:
                raise ValueError(f"No valid checkpoint found in output directory ({args.output_dir})")

        if resume_from_checkpoint is not None and not is_sagemaker_mp_enabled() and args.deepspeed is None:
            self._load_from_checkpoint(resume_from_checkpoint)

        # If model was re-initialized, put it on the right device and update self.model_wrapped
        if model_reloaded:
            if self.place_model_on_device:
                self._move_model_to_device(self.model, args.device)
            self.model_wrapped = self.model

        inner_training_loop = find_executable_batch_size(
            self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
        )
        return inner_training_loop(
            args=args,
            resume_from_checkpoint=resume_from_checkpoint,
            trial=trial,
            ignore_keys_for_eval=ignore_keys_for_eval,
        )

    def _inner_training_loop(
        self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None
    ):
        self._train_batch_size = batch_size
        # Data loader and number of training steps
        train_dataloader = self.get_train_dataloader()

        # Setting up training control variables:
        # number of training epochs: num_train_epochs
        # number of training steps per epoch: num_update_steps_per_epoch
        # total number of training steps to execute: max_steps
        total_train_batch_size = args.train_batch_size * args.gradient_accumulation_steps * args.world_size

        len_dataloader = None
        if has_length(train_dataloader):
            len_dataloader = len(train_dataloader)
            num_update_steps_per_epoch = len_dataloader // args.gradient_accumulation_steps
            num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
            num_examples = self.num_examples(train_dataloader)
            if args.max_steps > 0:
                max_steps = args.max_steps
                num_train_epochs = args.max_steps // num_update_steps_per_epoch + int(
                    args.max_steps % num_update_steps_per_epoch > 0
                )
                # May be slightly incorrect if the last batch in the training dataloader has a smaller size but it's
                # the best we can do.
                num_train_samples = args.max_steps * total_train_batch_size
            else:
                max_steps = math.ceil(args.num_train_epochs * num_update_steps_per_epoch)
                num_train_epochs = math.ceil(args.num_train_epochs)
                num_train_samples = self.num_examples(train_dataloader) * args.num_train_epochs
        elif args.max_steps > 0:  # Rely on max_steps when dataloader does not have a working size
            max_steps = args.max_steps
            # Setting a very large number of epochs so we go as many times as necessary over the iterator.
            num_train_epochs = sys.maxsize
            num_update_steps_per_epoch = max_steps
            num_examples = total_train_batch_size * args.max_steps
            num_train_samples = args.max_steps * total_train_batch_size
        else:
            raise ValueError(
                "args.max_steps must be set to a positive value if dataloader does not have a length, was"
                f" {args.max_steps}"
            )

        if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:
            if self.args.n_gpu > 1:
                # nn.DataParallel(model) replicates the model, creating new variables and module
                # references registered here no longer work on other gpus, breaking the module
                raise ValueError(
                    "Currently --debug underflow_overflow is not supported under DP. Please use DDP"
                    " (torch.distributed.launch)."
                )
            else:
                debug_overflow = DebugUnderflowOverflow(self.model)  # noqa

        delay_optimizer_creation = (
            self.sharded_ddp is not None
            and self.sharded_ddp != ShardedDDPOption.SIMPLE
            or is_sagemaker_mp_enabled()
            or self.fsdp is not None
        )
        if args.deepspeed:
            deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(
                self, num_training_steps=max_steps, resume_from_checkpoint=resume_from_checkpoint
            )
            self.model = deepspeed_engine.module
            self.model_wrapped = deepspeed_engine
            self.deepspeed = deepspeed_engine
            self.optimizer = optimizer
            self.lr_scheduler = lr_scheduler
        elif not delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        self.state = TrainerState()
        self.state.is_hyper_param_search = trial is not None

        # Activate gradient checkpointing if needed
        if args.gradient_checkpointing:
            self.model.gradient_checkpointing_enable()

        model = self._wrap_model(self.model_wrapped)

        if is_sagemaker_mp_enabled() and resume_from_checkpoint is not None:
            self._load_from_checkpoint(resume_from_checkpoint, model)

        # for the rest of this function `model` is the outside model, whether it was wrapped or not
        if model is not self.model:
            self.model_wrapped = model

        if delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        # Check if saved optimizer or scheduler states exist
        self._load_optimizer_and_scheduler(resume_from_checkpoint)

        # important: at this point:
        # self.model         is the Transformers Model
        # self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model), etc.

        # Train!
        logger.info("***** Running training *****")
        logger.info(f"  Num examples = {num_examples}")
        logger.info(f"  Num Epochs = {num_train_epochs}")
        logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
        logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
        logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
        logger.info(f"  Total optimization steps = {max_steps}")
        logger.info(
            f"  Number of trainable parameters = {sum(p.numel() for p in model.parameters() if p.requires_grad)}"
        )

        self.state.epoch = 0
        start_time = time.time()
        epochs_trained = 0
        steps_trained_in_current_epoch = 0
        steps_trained_progress_bar = None

        # Check if continuing training from a checkpoint
        if resume_from_checkpoint is not None and os.path.isfile(
            os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
        ):
            self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
            epochs_trained = self.state.global_step // num_update_steps_per_epoch
            if not args.ignore_data_skip:
                steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
                steps_trained_in_current_epoch *= args.gradient_accumulation_steps
            else:
                steps_trained_in_current_epoch = 0

            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
            logger.info(f"  Continuing training from epoch {epochs_trained}")
            logger.info(f"  Continuing training from global step {self.state.global_step}")
            if not args.ignore_data_skip:
                if skip_first_batches is None:
                    logger.info(
                        f"  Will skip the first {epochs_trained} epochs then the first"
                        f" {steps_trained_in_current_epoch} batches in the first epoch. If this takes a lot of time,"
                        " you can install the latest version of Accelerate with `pip install -U accelerate`.You can"
                        " also add the `--ignore_data_skip` flag to your launch command, but you will resume the"
                        " training on data already seen by your model."
                    )
                else:
                    logger.info(
                        f"  Will skip the first {epochs_trained} epochs then the first"
                        f" {steps_trained_in_current_epoch} batches in the first epoch."
                    )
                if self.is_local_process_zero() and not args.disable_tqdm and skip_first_batches is None:
                    steps_trained_progress_bar = tqdm(total=steps_trained_in_current_epoch)
                    steps_trained_progress_bar.set_description("Skipping the first batches")

        # Update the references
        self.callback_handler.model = self.model
        self.callback_handler.optimizer = self.optimizer
        self.callback_handler.lr_scheduler = self.lr_scheduler
        self.callback_handler.train_dataloader = train_dataloader
        if self.hp_name is not None and self._trial is not None:
            # use self._trial because the SigOpt/Optuna hpo only call `_hp_search_setup(trial)` instead of passing trial
            # parameter to Train when using DDP.
            self.state.trial_name = self.hp_name(self._trial)
        if trial is not None:
            assignments = trial.assignments if self.hp_search_backend == HPSearchBackend.SIGOPT else trial
            self.state.trial_params = hp_params(assignments)
        else:
            self.state.trial_params = None
        # This should be the same if the state has been saved but in case the training arguments changed, it's safer
        # to set this after the load.
        self.state.max_steps = max_steps
        self.state.num_train_epochs = num_train_epochs
        self.state.is_local_process_zero = self.is_local_process_zero()
        self.state.is_world_process_zero = self.is_world_process_zero()

        # tr_loss is a tensor to avoid synchronization of TPUs through .item()
        tr_loss = torch.tensor(0.0).to(args.device)
        # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
        self._total_loss_scalar = 0.0
        self._globalstep_last_logged = self.state.global_step
        model.zero_grad()

        self.control = self.callback_handler.on_train_begin(args, self.state, self.control)

        # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.
        if not args.ignore_data_skip:
            for epoch in range(epochs_trained):
                is_random_sampler = hasattr(train_dataloader, "sampler") and isinstance(
                    train_dataloader.sampler, RandomSampler
                )
                if is_torch_less_than_1_11 or not is_random_sampler:
                    # We just need to begin an iteration to create the randomization of the sampler.
                    # That was before PyTorch 1.11 however...
                    for _ in train_dataloader:
                        break
                else:
                    # Otherwise we need to call the whooooole sampler cause there is some random operation added
                    # AT THE VERY END!
                    _ = list(train_dataloader.sampler)

        total_batched_samples = 0
        for epoch in range(epochs_trained, num_train_epochs):
            if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                train_dataloader.sampler.set_epoch(epoch)
            elif hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDatasetShard):
                train_dataloader.dataset.set_epoch(epoch)

            if is_torch_tpu_available():
                parallel_loader = pl.ParallelLoader(train_dataloader, [args.device]).per_device_loader(args.device)
                epoch_iterator = parallel_loader
            else:
                epoch_iterator = train_dataloader

            # Reset the past mems state at the beginning of each epoch if necessary.
            if args.past_index >= 0:
                self._past = None

            steps_in_epoch = (
                len(epoch_iterator)
                if len_dataloader is not None
                else args.max_steps * args.gradient_accumulation_steps
            )
            self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)

            if epoch == epochs_trained and resume_from_checkpoint is not None and steps_trained_in_current_epoch == 0:
                self._load_rng_state(resume_from_checkpoint)

            rng_to_sync = False
            steps_skipped = 0
            if skip_first_batches is not None and steps_trained_in_current_epoch > 0:
                epoch_iterator = skip_first_batches(epoch_iterator, steps_trained_in_current_epoch)
                steps_skipped = steps_trained_in_current_epoch
                steps_trained_in_current_epoch = 0
                rng_to_sync = True

            step = -1
            for step, inputs in enumerate(epoch_iterator):
                total_batched_samples += 1
                if rng_to_sync:
                    self._load_rng_state(resume_from_checkpoint)
                    rng_to_sync = False

                # Skip past any already trained steps if resuming training
                if steps_trained_in_current_epoch > 0:
                    steps_trained_in_current_epoch -= 1
                    if steps_trained_progress_bar is not None:
                        steps_trained_progress_bar.update(1)
                    if steps_trained_in_current_epoch == 0:
                        self._load_rng_state(resume_from_checkpoint)
                    continue
                elif steps_trained_progress_bar is not None:
                    steps_trained_progress_bar.close()
                    steps_trained_progress_bar = None

                if step % args.gradient_accumulation_steps == 0:
                    self.control = self.callback_handler.on_step_begin(args, self.state, self.control)

                if (
                    (total_batched_samples % args.gradient_accumulation_steps != 0)
                    and args.local_rank != -1
                    and args._no_sync_in_gradient_accumulation
                ):
                    # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.
                    with model.no_sync():
                        tr_loss_step = self.training_step(model, inputs)
                else:
                    tr_loss_step = self.training_step(model, inputs)

                if (
                    args.logging_nan_inf_filter
                    and not is_torch_tpu_available()
                    and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
                ):
                    # if loss is nan or inf simply add the average of previous logged losses
                    tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)
                else:
                    tr_loss += tr_loss_step

                self.current_flos += float(self.floating_point_ops(inputs))

                # Optimizer step for deepspeed must be called on every step regardless of the value of gradient_accumulation_steps
                if self.deepspeed:
                    self.deepspeed.step()

                if total_batched_samples % args.gradient_accumulation_steps == 0 or (
                    # last step in epoch but step is always smaller than gradient_accumulation_steps
                    steps_in_epoch <= args.gradient_accumulation_steps
                    and (step + 1) == steps_in_epoch
                ):
                    # Gradient clipping
                    if args.max_grad_norm is not None and args.max_grad_norm > 0 and not self.deepspeed:
                        # deepspeed does its own clipping

                        if self.do_grad_scaling:
                            # Reduce gradients first for XLA
                            if is_torch_tpu_available():
                                gradients = xm._fetch_gradients(self.optimizer)
                                xm.all_reduce("sum", gradients, scale=1.0 / xm.xrt_world_size())
                            # AMP: gradients need unscaling
                            self.scaler.unscale_(self.optimizer)

                        if is_sagemaker_mp_enabled() and args.fp16:
                            self.optimizer.clip_master_grads(args.max_grad_norm)
                        elif hasattr(self.optimizer, "clip_grad_norm"):
                            # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
                            self.optimizer.clip_grad_norm(args.max_grad_norm)
                        elif hasattr(model, "clip_grad_norm_"):
                            # Some models (like FullyShardedDDP) have a specific way to do gradient clipping
                            model.clip_grad_norm_(args.max_grad_norm)
                        else:
                            # Revert to normal clipping otherwise, handling Apex or full precision
                            nn.utils.clip_grad_norm_(
                                amp.master_params(self.optimizer) if self.use_apex else model.parameters(),
                                args.max_grad_norm,
                            )

                    # Optimizer step
                    optimizer_was_run = True
                    if self.deepspeed:
                        pass  # called outside the loop
                    elif is_torch_tpu_available():
                        if self.do_grad_scaling:
                            self.scaler.step(self.optimizer)
                            self.scaler.update()
                        else:
                            xm.optimizer_step(self.optimizer)
                    elif self.do_grad_scaling:
                        scale_before = self.scaler.get_scale()
                        self.scaler.step(self.optimizer)
                        self.scaler.update()
                        scale_after = self.scaler.get_scale()
                        optimizer_was_run = scale_before <= scale_after
                    else:
                        self.optimizer.step()

                    if optimizer_was_run and not self.deepspeed:
                        self.lr_scheduler.step()

                    model.zero_grad()
                    self.state.global_step += 1
                    self.state.epoch = epoch + (step + 1 + steps_skipped) / steps_in_epoch
                    self.control = self.callback_handler.on_step_end(args, self.state, self.control)

                    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
                else:
                    self.control = self.callback_handler.on_substep_end(args, self.state, self.control)

                if self.control.should_epoch_stop or self.control.should_training_stop:
                    break
            if step < 0:
                logger.warning(
                    "There seems to be not a single sample in your epoch_iterator, stopping training at step"
                    f" {self.state.global_step}! This is expected if you're using an IterableDataset and set"
                    f" num_steps ({max_steps}) higher than the number of available samples."
                )
                self.control.should_training_stop = True

            self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
            self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)

            if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
                if is_torch_tpu_available():
                    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                    xm.master_print(met.metrics_report())
                else:
                    logger.warning(
                        "You enabled PyTorch/XLA debug metrics but you don't have a TPU "
                        "configured. Check your training configuration if this is unexpected."
                    )
            if self.control.should_training_stop:
                break

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of training
            delattr(self, "_past")

        logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
        if args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
            # Wait for everyone to get here so we are sur the model has been saved by process 0.
            if is_torch_tpu_available():
                xm.rendezvous("load_best_model_at_end")
            elif args.local_rank != -1:
                dist.barrier()
            elif is_sagemaker_mp_enabled():
                smp.barrier()

            self._load_best_model()

        # add remaining tr_loss
        self._total_loss_scalar += tr_loss.item()
        train_loss = self._total_loss_scalar / self.state.global_step

        metrics = speed_metrics("train", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps)
        self.store_flos()
        metrics["total_flos"] = self.state.total_flos
        metrics["train_loss"] = train_loss

        self.is_in_train = False

        self._memory_tracker.stop_and_update_metrics(metrics)

        self.log(metrics)

        run_dir = self._get_output_dir(trial)
        checkpoints_sorted = self._sorted_checkpoints(use_mtime=False, output_dir=run_dir)

        # Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint and process allowed to save.
        if self.args.should_save and self.state.best_model_checkpoint is not None and self.args.save_total_limit == 1:
            for checkpoint in checkpoints_sorted:
                if checkpoint != self.state.best_model_checkpoint:
                    logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
                    shutil.rmtree(checkpoint)

        self.control = self.callback_handler.on_train_end(args, self.state, self.control)

        return TrainOutput(self.state.global_step, train_loss, metrics)

    def _get_output_dir(self, trial):
        if self.hp_search_backend is not None and trial is not None:
            if self.hp_search_backend == HPSearchBackend.OPTUNA:
                run_id = trial.number
            elif self.hp_search_backend == HPSearchBackend.RAY:
                from ray import tune

                run_id = tune.get_trial_id()
            elif self.hp_search_backend == HPSearchBackend.SIGOPT:
                run_id = trial.id
            elif self.hp_search_backend == HPSearchBackend.WANDB:
                import wandb

                run_id = wandb.run.id
            run_name = self.hp_name(trial) if self.hp_name is not None else f"run-{run_id}"
            run_dir = os.path.join(self.args.output_dir, run_name)
        else:
            run_dir = self.args.output_dir
        return run_dir

    def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
        if model is None:
            model = self.model

        if not os.path.isfile(os.path.join(resume_from_checkpoint, WEIGHTS_NAME)) and not os.path.isfile(
            os.path.join(resume_from_checkpoint, WEIGHTS_INDEX_NAME)
        ):
            raise ValueError(f"Can't find a valid checkpoint at {resume_from_checkpoint}")

        logger.info(f"Loading model from {resume_from_checkpoint}.")

        if os.path.isfile(os.path.join(resume_from_checkpoint, CONFIG_NAME)):
            config = PretrainedConfig.from_json_file(os.path.join(resume_from_checkpoint, CONFIG_NAME))
            checkpoint_version = config.transformers_version
            if checkpoint_version is not None and checkpoint_version != __version__:
                logger.warning(
                    f"You are resuming training from a checkpoint trained with {checkpoint_version} of "
                    f"Transformers but your current version is {__version__}. This is not recommended and could "
                    "yield to errors or unwanted behaviors."
                )

        if os.path.isfile(os.path.join(resume_from_checkpoint, WEIGHTS_NAME)):
            # If the model is on the GPU, it still works!
            if is_sagemaker_mp_enabled():
                if os.path.isfile(os.path.join(resume_from_checkpoint, "user_content.pt")):
                    # If the 'user_content.pt' file exists, load with the new smp api.
                    # Checkpoint must have been saved with the new smp api.
                    smp.resume_from_checkpoint(
                        path=resume_from_checkpoint, tag=WEIGHTS_NAME, partial=False, load_optimizer=False
                    )
                else:
                    # If the 'user_content.pt' file does NOT exist, load with the old smp api.
                    # Checkpoint must have been saved with the old smp api.
                    if hasattr(self.args, "fp16") and self.args.fp16 is True:
                        logger.warning(
                            "Enabling FP16 and loading from smp < 1.10 checkpoint together is not suppported."
                        )
                    state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu")
                    # Required for smp to not auto-translate state_dict from hf to smp (is already smp).
                    state_dict["_smp_is_partial"] = False
                    load_result = model.load_state_dict(state_dict, strict=True)
                    # release memory
                    del state_dict
            else:
                # We load the model state dict on the CPU to avoid an OOM error.
                state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu")
                # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
                # which takes *args instead of **kwargs
                load_result = model.load_state_dict(state_dict, False)
                # release memory
                del state_dict
                self._issue_warnings_after_load(load_result)
        else:
            # We load the sharded checkpoint
            load_result = load_sharded_checkpoint(model, resume_from_checkpoint, strict=is_sagemaker_mp_enabled())
            if not is_sagemaker_mp_enabled():
                self._issue_warnings_after_load(load_result)

    def _load_best_model(self):
        logger.info(f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric}).")
        best_model_path = os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)
        model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
        if os.path.exists(best_model_path):
            if self.deepspeed:
                if self.model_wrapped is not None:
                    # this removes the pre-hooks from the previous engine
                    self.model_wrapped.destroy()
                    self.model_wrapped = None

                # temp hack until Deepspeed fixes the problem with resume from an existing engine that did some stepping
                deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(
                    self,
                    num_training_steps=self.args.max_steps,
                    resume_from_checkpoint=self.state.best_model_checkpoint,
                )
                self.model = deepspeed_engine.module
                self.model_wrapped = deepspeed_engine
                self.deepspeed = deepspeed_engine
                self.optimizer = optimizer
                self.lr_scheduler = lr_scheduler
            else:
                if is_sagemaker_mp_enabled():
                    if os.path.isfile(os.path.join(self.state.best_model_checkpoint, "user_content.pt")):
                        # If the 'user_content.pt' file exists, load with the new smp api.
                        # Checkpoint must have been saved with the new smp api.
                        smp.resume_from_checkpoint(
                            path=self.state.best_model_checkpoint,
                            tag=WEIGHTS_NAME,
                            partial=False,
                            load_optimizer=False,
                        )
                    else:
                        # If the 'user_content.pt' file does NOT exist, load with the old smp api.
                        # Checkpoint must have been saved with the old smp api.
                        state_dict = torch.load(best_model_path, map_location="cpu")
                        state_dict["_smp_is_partial"] = False
                        load_result = model.load_state_dict(state_dict, strict=True)
                else:
                    # We load the model state dict on the CPU to avoid an OOM error.
                    state_dict = torch.load(best_model_path, map_location="cpu")
                    # If the model is on the GPU, it still works!
                    # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
                    # which takes *args instead of **kwargs
                    load_result = model.load_state_dict(state_dict, False)
                if not is_sagemaker_mp_enabled():
                    self._issue_warnings_after_load(load_result)
        elif os.path.exists(os.path.join(self.state.best_model_checkpoint, WEIGHTS_INDEX_NAME)):
            load_result = load_sharded_checkpoint(
                model, self.state.best_model_checkpoint, strict=is_sagemaker_mp_enabled()
            )
            if not is_sagemaker_mp_enabled():
                self._issue_warnings_after_load(load_result)
        else:
            logger.warning(
                f"Could not locate the best model at {best_model_path}, if you are running a distributed training "
                "on multiple nodes, you should activate `--save_on_each_node`."
            )

    def _issue_warnings_after_load(self, load_result):
        if len(load_result.missing_keys) != 0:
            if self.model._keys_to_ignore_on_save is not None and set(load_result.missing_keys) == set(
                self.model._keys_to_ignore_on_save
            ):
                self.model.tie_weights()
            else:
                logger.warning(f"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}.")
        if len(load_result.unexpected_keys) != 0:
            logger.warning(
                f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}."
            )

    def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignore_keys_for_eval):
        if self.control.should_log:
            if is_torch_tpu_available():
                xm.mark_step()

            logs: Dict[str, float] = {}

            # all_gather + mean() to get average loss over all processes
            tr_loss_scalar = self._nested_gather(tr_loss).mean().item()

            # reset tr_loss to zero
            tr_loss -= tr_loss

            logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
            logs["learning_rate"] = self._get_learning_rate()

            self._total_loss_scalar += tr_loss_scalar
            self._globalstep_last_logged = self.state.global_step
            self.store_flos()

            self.log(logs)

        metrics = None
        if self.control.should_evaluate:
            if isinstance(self.eval_dataset, dict):
                for eval_dataset_name, eval_dataset in self.eval_dataset.items():
                    metrics = self.evaluate(
                        eval_dataset=eval_dataset,
                        ignore_keys=ignore_keys_for_eval,
                        metric_key_prefix=f"eval_{eval_dataset_name}",
                    )
            else:
                metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
            self._report_to_hp_search(trial, self.state.global_step, metrics)

        if self.control.should_save:
            self._save_checkpoint(model, trial, metrics=metrics)
            self.control = self.callback_handler.on_save(self.args, self.state, self.control)

    def _load_rng_state(self, checkpoint):
        # Load RNG states from `checkpoint`
        if checkpoint is None:
            return

        if self.args.world_size > 1:
            process_index = self.args.process_index
            rng_file = os.path.join(checkpoint, f"rng_state_{process_index}.pth")
            if not os.path.isfile(rng_file):
                logger.info(
                    f"Didn't find an RNG file for process {process_index}, if you are resuming a training that "
                    "wasn't launched in a distributed fashion, reproducibility is not guaranteed."
                )
                return
        else:
            rng_file = os.path.join(checkpoint, "rng_state.pth")
            if not os.path.isfile(rng_file):
                logger.info(
                    "Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
                    "fashion, reproducibility is not guaranteed."
                )
                return

        checkpoint_rng_state = torch.load(rng_file)
        random.setstate(checkpoint_rng_state["python"])
        np.random.set_state(checkpoint_rng_state["numpy"])
        torch.random.set_rng_state(checkpoint_rng_state["cpu"])
        if torch.cuda.is_available():
            if self.args.local_rank != -1:
                torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"])
            else:
                try:
                    torch.cuda.random.set_rng_state_all(checkpoint_rng_state["cuda"])
                except Exception as e:
                    logger.info(
                        f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}"
                        "\nThis won't yield the same results as if the training had not been interrupted."
                    )
        if is_torch_tpu_available():
            xm.set_rng_state(checkpoint_rng_state["xla"])

    def _save_checkpoint(self, model, trial, metrics=None):
        # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we
        # want to save except FullyShardedDDP.
        # assert unwrap_model(model) is self.model, "internal model should be a reference to self.model"

        # Save model checkpoint
        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"

        if self.hp_search_backend is None and trial is None:
            self.store_flos()

        run_dir = self._get_output_dir(trial=trial)
        output_dir = os.path.join(run_dir, checkpoint_folder)
        self.save_model(output_dir, _internal_call=True)
        if self.deepspeed:
            # under zero3 model file itself doesn't get saved since it's bogus! Unless deepspeed
            # config `stage3_gather_16bit_weights_on_model_save` is True
            self.deepspeed.save_checkpoint(output_dir)

        # Save optimizer and scheduler
        if self.sharded_ddp == ShardedDDPOption.SIMPLE:
            self.optimizer.consolidate_state_dict()

        if is_torch_tpu_available():
            xm.rendezvous("saving_optimizer_states")
            xm.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
            with warnings.catch_warnings(record=True) as caught_warnings:
                xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
                reissue_pt_warnings(caught_warnings)
        elif is_sagemaker_mp_enabled():
            opt_state_dict = self.optimizer.local_state_dict(gather_if_shard=False)
            smp.barrier()
            if smp.rdp_rank() == 0 or smp.state.cfg.shard_optimizer_state:
                smp.save(
                    opt_state_dict,
                    os.path.join(output_dir, OPTIMIZER_NAME),
                    partial=True,
                    v3=smp.state.cfg.shard_optimizer_state,
                )
            if self.args.should_save:
                with warnings.catch_warnings(record=True) as caught_warnings:
                    torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
                reissue_pt_warnings(caught_warnings)
                if self.do_grad_scaling:
                    torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))
        elif self.args.should_save and not self.deepspeed:
            # deepspeed.save_checkpoint above saves model/optim/sched
            torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
            with warnings.catch_warnings(record=True) as caught_warnings:
                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
            reissue_pt_warnings(caught_warnings)
            if self.do_grad_scaling:
                torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))

        # Determine the new best metric / best model checkpoint
        if metrics is not None and self.args.metric_for_best_model is not None:
            metric_to_check = self.args.metric_for_best_model
            if not metric_to_check.startswith("eval_"):
                metric_to_check = f"eval_{metric_to_check}"
            metric_value = metrics[metric_to_check]

            operator = np.greater if self.args.greater_is_better else np.less
            if (
                self.state.best_metric is None
                or self.state.best_model_checkpoint is None
                or operator(metric_value, self.state.best_metric)
            ):
                self.state.best_metric = metric_value
                self.state.best_model_checkpoint = output_dir

        # Save the Trainer state
        if self.args.should_save:
            self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))

        # Save RNG state in non-distributed training
        rng_states = {
            "python": random.getstate(),
            "numpy": np.random.get_state(),
            "cpu": torch.random.get_rng_state(),
        }
        if torch.cuda.is_available():
            if self.args.local_rank == -1:
                # In non distributed, we save the global CUDA RNG state (will take care of DataParallel)
                rng_states["cuda"] = torch.cuda.random.get_rng_state_all()
            else:
                rng_states["cuda"] = torch.cuda.random.get_rng_state()

        if is_torch_tpu_available():
            rng_states["xla"] = xm.get_rng_state()

        # A process can arrive here before the process 0 has a chance to save the model, in which case output_dir may
        # not yet exist.
        os.makedirs(output_dir, exist_ok=True)

        if self.args.world_size <= 1:
            torch.save(rng_states, os.path.join(output_dir, "rng_state.pth"))
        else:
            torch.save(rng_states, os.path.join(output_dir, f"rng_state_{self.args.process_index}.pth"))

        if self.args.push_to_hub:
            self._push_from_checkpoint(output_dir)

        # Maybe delete some older checkpoints.
        if self.args.should_save:
            self._rotate_checkpoints(use_mtime=True, output_dir=run_dir)

    def _load_optimizer_and_scheduler(self, checkpoint):
        """If optimizer and scheduler states exist, load them."""
        if checkpoint is None:
            return

        if self.deepspeed:
            # deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init
            return

        checkpoint_file_exists = (
            glob.glob(os.path.join(checkpoint, OPTIMIZER_NAME) + "_*")
            if is_sagemaker_mp_enabled()
            else os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME))
        )
        if checkpoint_file_exists and os.path.isfile(os.path.join(checkpoint, SCHEDULER_NAME)):
            # Load in optimizer and scheduler states
            if is_torch_tpu_available():
                # On TPU we have to take some extra precautions to properly load the states on the right device.
                optimizer_state = torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location="cpu")
                with warnings.catch_warnings(record=True) as caught_warnings:
                    lr_scheduler_state = torch.load(os.path.join(checkpoint, SCHEDULER_NAME), map_location="cpu")
                reissue_pt_warnings(caught_warnings)

                xm.send_cpu_data_to_device(optimizer_state, self.args.device)
                xm.send_cpu_data_to_device(lr_scheduler_state, self.args.device)

                self.optimizer.load_state_dict(optimizer_state)
                self.lr_scheduler.load_state_dict(lr_scheduler_state)
            else:
                map_location = "cpu" if is_sagemaker_mp_enabled() else self.args.device
                if is_sagemaker_mp_enabled():
                    if os.path.isfile(os.path.join(checkpoint, "user_content.pt")):
                        # Optimizer checkpoint was saved with smp >= 1.10
                        def opt_load_hook(mod, opt):
                            opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))

                    else:
                        # Optimizer checkpoint was saved with smp < 1.10
                        def opt_load_hook(mod, opt):
                            if IS_SAGEMAKER_MP_POST_1_10:
                                opt.load_state_dict(
                                    smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True, back_compat=True)
                                )
                            else:
                                opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))

                    self.model_wrapped.register_post_step_hook(opt_load_hook)
                else:
                    self.optimizer.load_state_dict(
                        torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)
                    )
                with warnings.catch_warnings(record=True) as caught_warnings:
                    self.lr_scheduler.load_state_dict(torch.load(os.path.join(checkpoint, SCHEDULER_NAME)))
                reissue_pt_warnings(caught_warnings)
                if self.do_grad_scaling and os.path.isfile(os.path.join(checkpoint, SCALER_NAME)):
                    self.scaler.load_state_dict(torch.load(os.path.join(checkpoint, SCALER_NAME)))

    def hyperparameter_search(
        self,
        hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
        compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
        n_trials: int = 20,
        direction: str = "minimize",
        backend: Optional[Union["str", HPSearchBackend]] = None,
        hp_name: Optional[Callable[["optuna.Trial"], str]] = None,
        **kwargs,
    ) -> BestRun:
        """
        Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined
        by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
        the sum of all metrics otherwise.

        <Tip warning={true}>

        To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to
        reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to
        subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom
        optimizer/scheduler.

        </Tip>

        Args:
            hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*):
                A function that defines the hyperparameter search space. Will default to
                [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or
                [`~trainer_utils.default_hp_space_sigopt`] depending on your backend.
            compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):
                A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`
                method. Will default to [`~trainer_utils.default_compute_objective`].
            n_trials (`int`, *optional*, defaults to 100):
                The number of trial runs to test.
            direction (`str`, *optional*, defaults to `"minimize"`):
                Whether to optimize greater or lower objects. Can be `"minimize"` or `"maximize"`, you should pick
                `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics.
            backend (`str` or [`~training_utils.HPSearchBackend`], *optional*):
                The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending
                on which one is installed. If all are installed, will default to optuna.
            hp_name (`Callable[["optuna.Trial"], str]]`, *optional*):
                A function that defines the trial/run name. Will default to None.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more
                information see:

                - the documentation of
                  [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)
                - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run)
                - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)

        Returns:
            [`trainer_utils.BestRun`]: All the information about the best run. Experiment summary can be found in
            `run_summary` attribute for Ray backend.
        """
        if backend is None:
            backend = default_hp_search_backend()
            if backend is None:
                raise RuntimeError(
                    "At least one of optuna or ray should be installed. "
                    "To install optuna run `pip install optuna`. "
                    "To install ray run `pip install ray[tune]`. "
                    "To install sigopt run `pip install sigopt`."
                )
        backend = HPSearchBackend(backend)
        if backend == HPSearchBackend.OPTUNA and not is_optuna_available():
            raise RuntimeError("You picked the optuna backend, but it is not installed. Use `pip install optuna`.")
        if backend == HPSearchBackend.RAY and not is_ray_tune_available():
            raise RuntimeError(
                "You picked the Ray Tune backend, but it is not installed. Use `pip install 'ray[tune]'`."
            )
        if backend == HPSearchBackend.SIGOPT and not is_sigopt_available():
            raise RuntimeError("You picked the sigopt backend, but it is not installed. Use `pip install sigopt`.")
        if backend == HPSearchBackend.WANDB and not is_wandb_available():
            raise RuntimeError("You picked the wandb backend, but it is not installed. Use `pip install wandb`.")
        self.hp_search_backend = backend
        if self.model_init is None:
            raise RuntimeError(
                "To use hyperparameter search, you need to pass your model through a model_init function."
            )

        self.hp_space = default_hp_space[backend] if hp_space is None else hp_space
        self.hp_name = hp_name
        self.compute_objective = default_compute_objective if compute_objective is None else compute_objective

        backend_dict = {
            HPSearchBackend.OPTUNA: run_hp_search_optuna,
            HPSearchBackend.RAY: run_hp_search_ray,
            HPSearchBackend.SIGOPT: run_hp_search_sigopt,
            HPSearchBackend.WANDB: run_hp_search_wandb,
        }
        best_run = backend_dict[backend](self, n_trials, direction, **kwargs)

        self.hp_search_backend = None
        return best_run

    def log(self, logs: Dict[str, float]) -> None:
        """
        Log `logs` on the various objects watching training.

        Subclass and override this method to inject custom behavior.

        Args:
            logs (`Dict[str, float]`):
                The values to log.
        """
        if self.state.epoch is not None:
            logs["epoch"] = round(self.state.epoch, 2)

        output = {**logs, **{"step": self.state.global_step}}
        self.state.log_history.append(output)
        self.control = self.callback_handler.on_log(self.args, self.state, self.control, logs)

    def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any]:
        """
        Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
        """
        if isinstance(data, Mapping):
            return type(data)({k: self._prepare_input(v) for k, v in data.items()})
        elif isinstance(data, (tuple, list)):
            return type(data)(self._prepare_input(v) for v in data)
        elif isinstance(data, torch.Tensor):
            kwargs = {"device": self.args.device}
            if self.deepspeed and (torch.is_floating_point(data) or torch.is_complex(data)):
                # NLP models inputs are int/uint and those get adjusted to the right dtype of the
                # embedding. Other models such as wav2vec2's inputs are already float and thus
                # may need special handling to match the dtypes of the model
                kwargs.update({"dtype": self.args.hf_deepspeed_config.dtype()})
            return data.to(**kwargs)
        return data

    def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
        """
        Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and
        handling potential state.
        """
        inputs = self._prepare_input(inputs)
        if len(inputs) == 0:
            raise ValueError(
                "The batch received was empty, your model won't be able to train on it. Double-check that your "
                f"training dataset contains keys expected by the model: {','.join(self._signature_columns)}."
            )
        if self.args.past_index >= 0 and self._past is not None:
            inputs["mems"] = self._past

        return inputs

    def compute_loss_context_manager(self):
        """
        A helper wrapper to group together context managers.
        """
        return self.autocast_smart_context_manager()

    def autocast_smart_context_manager(self, cache_enabled: Optional[bool] = True):
        """
        A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired
        arguments, depending on the situation.
        """
        if self.use_cuda_amp or self.use_cpu_amp:
            if is_torch_greater_or_equal_than_1_10:
                ctx_manager = (
                    torch.cpu.amp.autocast(cache_enabled=cache_enabled, dtype=self.amp_dtype)
                    if self.use_cpu_amp
                    else torch.cuda.amp.autocast(cache_enabled=cache_enabled, dtype=self.amp_dtype)
                )
            else:
                ctx_manager = torch.cuda.amp.autocast()
        else:
            ctx_manager = contextlib.nullcontext() if sys.version_info >= (3, 7) else contextlib.suppress()

        return ctx_manager

    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
        """
        Perform a training step on a batch of inputs.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to train.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.

        Return:
            `torch.Tensor`: The tensor with training loss on this batch.
        """
        model.train()
        inputs = self._prepare_inputs(inputs)

        if is_sagemaker_mp_enabled():
            loss_mb = smp_forward_backward(model, inputs, self.args.gradient_accumulation_steps)
            return loss_mb.reduce_mean().detach().to(self.args.device)

        with self.compute_loss_context_manager():
            loss = self.compute_loss(model, inputs)

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training

        if self.args.gradient_accumulation_steps > 1 and not self.deepspeed:
            # deepspeed handles loss scaling by gradient_accumulation_steps in its `backward`
            loss = loss / self.args.gradient_accumulation_steps

        if self.do_grad_scaling:
            self.scaler.scale(loss).backward()
        elif self.use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        elif self.deepspeed:
            # loss gets scaled under gradient_accumulation_steps in deepspeed
            loss = self.deepspeed.backward(loss)
        else:
            loss.backward()

        return loss.detach()

    def compute_loss(self, model, inputs, return_outputs=False):
        """
        How the loss is computed by Trainer. By default, all models return the loss in the first element.

        Subclass and override for custom behavior.
        """
        if self.label_smoother is not None and "labels" in inputs:
            labels = inputs.pop("labels")
        else:
            labels = None
        outputs = model(**inputs)
        # Save past state if it exists
        # TODO: this needs to be fixed and made cleaner later.
        if self.args.past_index >= 0:
            self._past = outputs[self.args.past_index]

        if labels is not None:
            if unwrap_model(model)._get_name() in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
                loss = self.label_smoother(outputs, labels, shift_labels=True)
            else:
                loss = self.label_smoother(outputs, labels)
        else:
            if isinstance(outputs, dict) and "loss" not in outputs:
                raise ValueError(
                    "The model did not return a loss from the inputs, only the following keys: "
                    f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}."
                )
            # We don't use .loss here since the model may return tuples instead of ModelOutput.
            loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

        return (loss, outputs) if return_outputs else loss

    def is_local_process_zero(self) -> bool:
        """
        Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
        machines) main process.
        """
        return self.args.local_process_index == 0

    def is_world_process_zero(self) -> bool:
        """
        Whether or not this process is the global main process (when training in a distributed fashion on several
        machines, this is only going to be `True` for one process).
        """
        # Special case for SageMaker ModelParallel since there process_index is dp_process_index, not the global
        # process index.
        if is_sagemaker_mp_enabled():
            return smp.rank() == 0
        else:
            return self.args.process_index == 0

    def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
        """
        Will save the model, so you can reload it using `from_pretrained()`.

        Will only save from the main process.
        """

        if output_dir is None:
            output_dir = self.args.output_dir

        if is_torch_tpu_available():
            self._save_tpu(output_dir)
        elif is_sagemaker_mp_enabled():
            # Calling the state_dict needs to be done on the wrapped model and on all processes.
            os.makedirs(output_dir, exist_ok=True)
            state_dict = self.model_wrapped.state_dict()
            if self.args.should_save:
                self._save(output_dir, state_dict=state_dict)
            if IS_SAGEMAKER_MP_POST_1_10:
                # 'user_content.pt' indicates model state_dict saved with smp >= 1.10
                Path(os.path.join(output_dir, "user_content.pt")).touch()
        elif (
            ShardedDDPOption.ZERO_DP_2 in self.args.sharded_ddp
            or ShardedDDPOption.ZERO_DP_3 in self.args.sharded_ddp
            or self.fsdp is not None
        ):
            state_dict = self.model.state_dict()

            if self.args.should_save:
                self._save(output_dir, state_dict=state_dict)
        elif self.deepspeed:
            # this takes care of everything as long as we aren't under zero3
            if self.args.should_save:
                self._save(output_dir)

            if is_deepspeed_zero3_enabled():
                # It's too complicated to try to override different places where the weights dump gets
                # saved, so since under zero3 the file is bogus, simply delete it. The user should
                # either user deepspeed checkpoint to resume or to recover full weights use
                # zero_to_fp32.py stored in the checkpoint.
                if self.args.should_save:
                    file = os.path.join(output_dir, WEIGHTS_NAME)
                    if os.path.isfile(file):
                        # logger.info(f"deepspeed zero3: removing {file}, see zero_to_fp32.py to recover weights")
                        os.remove(file)

                # now save the real model if stage3_gather_16bit_weights_on_model_save=True
                # if false it will not be saved.
                # This must be called on all ranks
                if not self.deepspeed.save_16bit_model(output_dir, WEIGHTS_NAME):
                    logger.warning(
                        "deepspeed.save_16bit_model didn't save the model, since"
                        " stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use"
                        " zero_to_fp32.py to recover weights"
                    )
                    self.deepspeed.save_checkpoint(output_dir)

        elif self.args.should_save:
            self._save(output_dir)

        # Push to the Hub when `save_model` is called by the user.
        if self.args.push_to_hub and not _internal_call:
            self.push_to_hub(commit_message="Model save")

    def _save_tpu(self, output_dir: Optional[str] = None):
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        logger.info(f"Saving model checkpoint to {output_dir}")

        if xm.is_master_ordinal():
            os.makedirs(output_dir, exist_ok=True)
            torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        xm.rendezvous("saving_checkpoint")
        if not isinstance(self.model, PreTrainedModel):
            if isinstance(unwrap_model(self.model), PreTrainedModel):
                unwrap_model(self.model).save_pretrained(
                    output_dir,
                    is_main_process=self.args.should_save,
                    state_dict=self.model.state_dict(),
                    save_function=xm.save,
                )
            else:
                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
                state_dict = self.model.state_dict()
                xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            self.model.save_pretrained(output_dir, is_main_process=self.args.should_save, save_function=xm.save)
        if self.tokenizer is not None and self.args.should_save:
            self.tokenizer.save_pretrained(output_dir)

    def _save(self, output_dir: Optional[str] = None, state_dict=None):
        # If we are executing this function, we are the process zero, so we don't check for that.
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        os.makedirs(output_dir, exist_ok=True)
        logger.info(f"Saving model checkpoint to {output_dir}")
        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        if not isinstance(self.model, PreTrainedModel):
            if isinstance(unwrap_model(self.model), PreTrainedModel):
                if state_dict is None:
                    state_dict = self.model.state_dict()
                unwrap_model(self.model).save_pretrained(output_dir, state_dict=filtered_state_dict)
            else:
                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
                if state_dict is None:
                    state_dict = self.model.state_dict()
                torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            if self.save_prefixencoder:
                print("Saving PrefixEncoder")
                state_dict = self.model.state_dict()
                filtered_state_dict = {}
                for k, v in self.model.named_parameters():
                    if v.requires_grad:
                        filtered_state_dict[k] = state_dict[k]
                self.model.save_pretrained(output_dir, state_dict=filtered_state_dict)
            else:
                print("Saving the whole model")
                self.model.save_pretrained(output_dir, state_dict=state_dict)
        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

    def store_flos(self):
        # Storing the number of floating-point operations that went into the model
        if self.args.local_rank != -1:
            self.state.total_flos += (
                distributed_broadcast_scalars([self.current_flos], device=self.args.device).sum().item()
            )
            self.current_flos = 0
        else:
            self.state.total_flos += self.current_flos
            self.current_flos = 0

    def _sorted_checkpoints(
        self, output_dir=None, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False
    ) -> List[str]:
        ordering_and_checkpoint_path = []

        glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]

        for path in glob_checkpoints:
            if use_mtime:
                ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
            else:
                regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
                if regex_match is not None and regex_match.groups() is not None:
                    ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))

        checkpoints_sorted = sorted(ordering_and_checkpoint_path)
        checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
        # Make sure we don't delete the best model.
        if self.state.best_model_checkpoint is not None:
            best_model_index = checkpoints_sorted.index(str(Path(self.state.best_model_checkpoint)))
            for i in range(best_model_index, len(checkpoints_sorted) - 2):
                checkpoints_sorted[i], checkpoints_sorted[i + 1] = checkpoints_sorted[i + 1], checkpoints_sorted[i]
        return checkpoints_sorted

    def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
        if self.args.save_total_limit is None or self.args.save_total_limit <= 0:
            return

        # Check if we should delete older checkpoint(s)
        checkpoints_sorted = self._sorted_checkpoints(use_mtime=use_mtime, output_dir=output_dir)
        if len(checkpoints_sorted) <= self.args.save_total_limit:
            return

        # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which
        # we don't do to allow resuming.
        save_total_limit = self.args.save_total_limit
        if (
            self.state.best_model_checkpoint is not None
            and self.args.save_total_limit == 1
            and checkpoints_sorted[-1] != self.state.best_model_checkpoint
        ):
            save_total_limit = 2

        number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
        checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
        for checkpoint in checkpoints_to_be_deleted:
            logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
            shutil.rmtree(checkpoint, ignore_errors=True)

    def evaluate(
        self,
        eval_dataset: Optional[Dataset] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> Dict[str, float]:
        """
        Run evaluation and returns metrics.

        The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
        (pass it to the init `compute_metrics` argument).

        You can also subclass and override this method to inject custom behavior.

        Args:
            eval_dataset (`Dataset`, *optional*):
                Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns
                not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__`
                method.
            ignore_keys (`Lst[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "eval_bleu" if the prefix is "eval" (default)

        Returns:
            A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
            dictionary also contains the epoch number which comes from the training state.
        """
        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        eval_dataloader = self.get_eval_dataloader(eval_dataset)
        start_time = time.time()

        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        output = eval_loop(
            eval_dataloader,
            description="Evaluation",
            # No point gathering the predictions if there are no metrics, otherwise we defer to
            # self.args.prediction_loss_only
            prediction_loss_only=True if self.compute_metrics is None else None,
            ignore_keys=ignore_keys,
            metric_key_prefix=metric_key_prefix,
        )

        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )

        self.log(output.metrics)

        if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report())

        self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)

        self._memory_tracker.stop_and_update_metrics(output.metrics)

        return output.metrics

    def predict(
        self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "test"
    ) -> PredictionOutput:
        """
        Run prediction and returns predictions and potential metrics.

        Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
        will also return metrics, like in `evaluate()`.

        Args:
            test_dataset (`Dataset`):
                Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the
                `model.forward()` method are automatically removed. Has to implement the method `__len__`
            ignore_keys (`Lst[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"test"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "test_bleu" if the prefix is "test" (default)

        <Tip>

        If your predictions or labels have different sequence length (for instance because you're doing dynamic padding
        in a token classification task) the predictions will be padded (on the right) to allow for concatenation into
        one array. The padding index is -100.

        </Tip>

        Returns: *NamedTuple* A namedtuple with the following keys:

            - predictions (`np.ndarray`): The predictions on `test_dataset`.
            - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).
            - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained
              labels).
        """
        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        test_dataloader = self.get_test_dataloader(test_dataset)
        start_time = time.time()

        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        output = eval_loop(
            test_dataloader, description="Prediction", ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix
        )
        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )

        self.control = self.callback_handler.on_predict(self.args, self.state, self.control, output.metrics)
        self._memory_tracker.stop_and_update_metrics(output.metrics)

        return PredictionOutput(predictions=output.predictions, label_ids=output.label_ids, metrics=output.metrics)

    def evaluation_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """
        args = self.args

        prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only

        # if eval is called w/o train init deepspeed here
        if args.deepspeed and not self.deepspeed:
            # XXX: eval doesn't have `resume_from_checkpoint` arg but we should be able to do eval
            # from the checkpoint eventually
            deepspeed_engine, _, _ = deepspeed_init(
                self, num_training_steps=0, resume_from_checkpoint=None, inference=True
            )
            self.model = deepspeed_engine.module
            self.model_wrapped = deepspeed_engine
            self.deepspeed = deepspeed_engine

        model = self._wrap_model(self.model, training=False, dataloader=dataloader)

        # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
        # while ``train`` is running, cast it to the right dtype first and then put on device
        if not self.is_in_train:
            if args.fp16_full_eval:
                model = model.to(dtype=torch.float16, device=args.device)
            elif args.bf16_full_eval:
                model = model.to(dtype=torch.bfloat16, device=args.device)

        batch_size = self.args.eval_batch_size

        logger.info(f"***** Running {description} *****")
        if has_length(dataloader):
            logger.info(f"  Num examples = {self.num_examples(dataloader)}")
        else:
            logger.info("  Num examples: Unknown")
        logger.info(f"  Batch size = {batch_size}")

        model.eval()

        self.callback_handler.eval_dataloader = dataloader
        # Do this before wrapping.
        eval_dataset = getattr(dataloader, "dataset", None)

        if is_torch_tpu_available():
            dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)

        if args.past_index >= 0:
            self._past = None

        # Initialize containers
        # losses/preds/labels on GPU/TPU (accumulated for eval_accumulation_steps)
        losses_host = None
        preds_host = None
        labels_host = None
        inputs_host = None

        # losses/preds/labels on CPU (final containers)
        all_losses = None
        all_preds = None
        all_labels = None
        all_inputs = None
        # Will be useful when we have an iterable dataset so don't know its length.

        observed_num_examples = 0
        # Main evaluation loop
        for step, inputs in enumerate(dataloader):
            # Update the observed num examples
            observed_batch_size = find_batch_size(inputs)
            if observed_batch_size is not None:
                observed_num_examples += observed_batch_size
                # For batch samplers, batch_size is not known by the dataloader in advance.
                if batch_size is None:
                    batch_size = observed_batch_size

            # Prediction step
            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
            inputs_decode = self._prepare_input(inputs["input_ids"]) if args.include_inputs_for_metrics else None

            if is_torch_tpu_available():
                xm.mark_step()

            # Update containers on host
            if loss is not None:
                losses = self._nested_gather(loss.repeat(batch_size))
                losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
            if labels is not None:
                labels = self._pad_across_processes(labels)
                labels = self._nested_gather(labels)
                labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)
            if inputs_decode is not None:
                inputs_decode = self._pad_across_processes(inputs_decode)
                inputs_decode = self._nested_gather(inputs_decode)
                inputs_host = (
                    inputs_decode
                    if inputs_host is None
                    else nested_concat(inputs_host, inputs_decode, padding_index=-100)
                )
            if logits is not None:
                logits = self._pad_across_processes(logits)
                logits = self._nested_gather(logits)
                if self.preprocess_logits_for_metrics is not None:
                    logits = self.preprocess_logits_for_metrics(logits, labels)
                preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
            self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)

            # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
            if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:
                if losses_host is not None:
                    losses = nested_numpify(losses_host)
                    all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
                if preds_host is not None:
                    logits = nested_numpify(preds_host)
                    all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
                if inputs_host is not None:
                    inputs_decode = nested_numpify(inputs_host)
                    all_inputs = (
                        inputs_decode
                        if all_inputs is None
                        else nested_concat(all_inputs, inputs_decode, padding_index=-100)
                    )
                if labels_host is not None:
                    labels = nested_numpify(labels_host)
                    all_labels = (
                        labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)
                    )

                # Set back to None to begin a new accumulation
                losses_host, preds_host, inputs_host, labels_host = None, None, None, None

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        # Gather all remaining tensors and put them back on the CPU
        if losses_host is not None:
            losses = nested_numpify(losses_host)
            all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
        if preds_host is not None:
            logits = nested_numpify(preds_host)
            all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
        if inputs_host is not None:
            inputs_decode = nested_numpify(inputs_host)
            all_inputs = (
                inputs_decode if all_inputs is None else nested_concat(all_inputs, inputs_decode, padding_index=-100)
            )
        if labels_host is not None:
            labels = nested_numpify(labels_host)
            all_labels = labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)

        # Number of samples
        if has_length(eval_dataset):
            num_samples = len(eval_dataset)
        # The instance check is weird and does not actually check for the type, but whether the dataset has the right
        # methods. Therefore we need to make sure it also has the attribute.
        elif isinstance(eval_dataset, IterableDatasetShard) and getattr(eval_dataset, "num_examples", 0) > 0:
            num_samples = eval_dataset.num_examples
        else:
            if has_length(dataloader):
                num_samples = self.num_examples(dataloader)
            else:  # both len(dataloader.dataset) and len(dataloader) fail
                num_samples = observed_num_examples
        if num_samples == 0 and observed_num_examples > 0:
            num_samples = observed_num_examples

        # Number of losses has been rounded to a multiple of batch_size and in a distributed training, the number of
        # samplers has been rounded to a multiple of batch_size, so we truncate.
        if all_losses is not None:
            all_losses = all_losses[:num_samples]
        if all_preds is not None:
            all_preds = nested_truncate(all_preds, num_samples)
        if all_labels is not None:
            all_labels = nested_truncate(all_labels, num_samples)
        if all_inputs is not None:
            all_inputs = nested_truncate(all_inputs, num_samples)

        # Metrics!
        if self.compute_metrics is not None and all_preds is not None and all_labels is not None:
            if args.include_inputs_for_metrics:
                metrics = self.compute_metrics(
                    EvalPrediction(predictions=all_preds, label_ids=all_labels, inputs=all_inputs)
                )
            else:
                metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
        else:
            metrics = {}

        # To be JSON-serializable, we need to remove numpy types or zero-d tensors
        metrics = denumpify_detensorize(metrics)

        if all_losses is not None:
            metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()
        if hasattr(self, "jit_compilation_time"):
            metrics[f"{metric_key_prefix}_jit_compilation_time"] = self.jit_compilation_time

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()):
            if not key.startswith(f"{metric_key_prefix}_"):
                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)

        return EvalLoopOutput(predictions=all_preds, label_ids=all_labels, metrics=metrics, num_samples=num_samples)

    def _nested_gather(self, tensors, name=None):
        """
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        """
        if tensors is None:
            return
        if is_torch_tpu_available():
            if name is None:
                name = "nested_gather"
            tensors = nested_xla_mesh_reduce(tensors, name)
        elif is_sagemaker_mp_enabled():
            tensors = smp_gather(tensors)
        elif self.args.local_rank != -1:
            tensors = distributed_concat(tensors)
        return tensors

    # Copied from Accelerate.
    def _pad_across_processes(self, tensor, pad_index=-100):
        """
        Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so
        they can safely be gathered.
        """
        if isinstance(tensor, (list, tuple)):
            return type(tensor)(self._pad_across_processes(t, pad_index=pad_index) for t in tensor)
        elif isinstance(tensor, dict):
            return type(tensor)({k: self._pad_across_processes(v, pad_index=pad_index) for k, v in tensor.items()})
        elif not isinstance(tensor, torch.Tensor):
            raise TypeError(
                f"Can't pad the values of type {type(tensor)}, only of nested list/tuple/dicts of tensors."
            )

        if len(tensor.shape) < 2:
            return tensor
        # Gather all sizes
        size = torch.tensor(tensor.shape, device=tensor.device)[None]
        sizes = self._nested_gather(size).cpu()

        max_size = max(s[1] for s in sizes)
        # When extracting XLA graphs for compilation, max_size is 0,
        # so use inequality to avoid errors.
        if tensor.shape[1] >= max_size:
            return tensor

        # Then pad to the maximum size
        old_size = tensor.shape
        new_size = list(old_size)
        new_size[1] = max_size
        new_tensor = tensor.new_zeros(tuple(new_size)) + pad_index
        new_tensor[:, : old_size[1]] = tensor
        return new_tensor

    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
    ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on `model` using `inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to evaluate.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (`bool`):
                Whether or not to return the loss only.
            ignore_keys (`Lst[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.

        Return:
            Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss,
            logits and labels (each being optional).
        """
        has_labels = False if len(self.label_names) == 0 else all(inputs.get(k) is not None for k in self.label_names)
        # For CLIP-like models capable of returning loss values.
        # If `return_loss` is not specified or being `None` in `inputs`, we check if the default value of `return_loss`
        # is `True` in `model.forward`.
        return_loss = inputs.get("return_loss", None)
        if return_loss is None:
            return_loss = self.can_return_loss
        loss_without_labels = True if len(self.label_names) == 0 and return_loss else False

        inputs = self._prepare_inputs(inputs)
        if ignore_keys is None:
            if hasattr(self.model, "config"):
                ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
            else:
                ignore_keys = []

        # labels may be popped when computing the loss (label smoothing for instance) so we grab them first.
        if has_labels or loss_without_labels:
            labels = nested_detach(tuple(inputs.get(name) for name in self.label_names))
            if len(labels) == 1:
                labels = labels[0]
        else:
            labels = None

        with torch.no_grad():
            if is_sagemaker_mp_enabled():
                raw_outputs = smp_forward_only(model, inputs)
                if has_labels or loss_without_labels:
                    if isinstance(raw_outputs, dict):
                        loss_mb = raw_outputs["loss"]
                        logits_mb = tuple(v for k, v in raw_outputs.items() if k not in ignore_keys + ["loss"])
                    else:
                        loss_mb = raw_outputs[0]
                        logits_mb = raw_outputs[1:]

                    loss = loss_mb.reduce_mean().detach().cpu()
                    logits = smp_nested_concat(logits_mb)
                else:
                    loss = None
                    if isinstance(raw_outputs, dict):
                        logits_mb = tuple(v for k, v in raw_outputs.items() if k not in ignore_keys)
                    else:
                        logits_mb = raw_outputs
                    logits = smp_nested_concat(logits_mb)
            else:
                if has_labels or loss_without_labels:
                    with self.compute_loss_context_manager():
                        loss, outputs = self.compute_loss(model, inputs, return_outputs=True)
                    loss = loss.mean().detach()

                    if isinstance(outputs, dict):
                        logits = tuple(v for k, v in outputs.items() if k not in ignore_keys + ["loss"])
                    else:
                        logits = outputs[1:]
                else:
                    loss = None
                    with self.compute_loss_context_manager():
                        outputs = model(**inputs)
                    if isinstance(outputs, dict):
                        logits = tuple(v for k, v in outputs.items() if k not in ignore_keys)
                    else:
                        logits = outputs
                    # TODO: this needs to be fixed and made cleaner later.
                    if self.args.past_index >= 0:
                        self._past = outputs[self.args.past_index - 1]

        if prediction_loss_only:
            return (loss, None, None)

        logits = nested_detach(logits)
        if len(logits) == 1:
            logits = logits[0]

        return (loss, logits, labels)

    def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
        """
        For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
        operations for every backward + forward pass. If using another model, either implement such a method in the
        model or subclass and override this method.

        Args:
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

        Returns:
            `int`: The number of floating-point operations.
        """
        if hasattr(self.model, "floating_point_ops"):
            return self.model.floating_point_ops(inputs)
        else:
            return 0

    def init_git_repo(self, at_init: bool = False):
        """
        Initializes a git repo in `self.args.hub_model_id`.

        Args:
            at_init (`bool`, *optional*, defaults to `False`):
                Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is
                `True` and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped
                out.
        """
        if not self.is_world_process_zero():
            return
        if self.args.hub_model_id is None:
            repo_name = Path(self.args.output_dir).absolute().name
        else:
            repo_name = self.args.hub_model_id
        if "/" not in repo_name:
            repo_name = get_full_repo_name(repo_name, token=self.args.hub_token)

        # Make sure the repo exists.
        create_repo(repo_name, token=self.args.hub_token, private=self.args.hub_private_repo, exist_ok=True)
        try:
            self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)
        except EnvironmentError:
            if self.args.overwrite_output_dir and at_init:
                # Try again after wiping output_dir
                shutil.rmtree(self.args.output_dir)
                self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)
            else:
                raise

        self.repo.git_pull()

        # By default, ignore the checkpoint folders
        if (
            not os.path.exists(os.path.join(self.args.output_dir, ".gitignore"))
            and self.args.hub_strategy != HubStrategy.ALL_CHECKPOINTS
        ):
            with open(os.path.join(self.args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
                writer.writelines(["checkpoint-*/"])

        # Add "*.sagemaker" to .gitignore if using SageMaker
        if os.environ.get("SM_TRAINING_ENV"):
            self._add_sm_patterns_to_gitignore()

        self.push_in_progress = None

    def create_model_card(
        self,
        language: Optional[str] = None,
        license: Optional[str] = None,
        tags: Union[str, List[str], None] = None,
        model_name: Optional[str] = None,
        finetuned_from: Optional[str] = None,
        tasks: Union[str, List[str], None] = None,
        dataset_tags: Union[str, List[str], None] = None,
        dataset: Union[str, List[str], None] = None,
        dataset_args: Union[str, List[str], None] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            language (`str`, *optional*):
                The language of the model (if applicable)
            license (`str`, *optional*):
                The license of the model. Will default to the license of the pretrained model used, if the original
                model given to the `Trainer` comes from a repo on the Hub.
            tags (`str` or `List[str]`, *optional*):
                Some tags to be included in the metadata of the model card.
            model_name (`str`, *optional*):
                The name of the model.
            finetuned_from (`str`, *optional*):
                The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo
                of the original model given to the `Trainer` (if it comes from the Hub).
            tasks (`str` or `List[str]`, *optional*):
                One or several task identifiers, to be included in the metadata of the model card.
            dataset_tags (`str` or `List[str]`, *optional*):
                One or several dataset tags, to be included in the metadata of the model card.
            dataset (`str` or `List[str]`, *optional*):
                One or several dataset identifiers, to be included in the metadata of the model card.
            dataset_args (`str` or `List[str]`, *optional*):
               One or several dataset arguments, to be included in the metadata of the model card.
        """
        if not self.is_world_process_zero():
            return

        training_summary = TrainingSummary.from_trainer(
            self,
            language=language,
            license=license,
            tags=tags,
            model_name=model_name,
            finetuned_from=finetuned_from,
            tasks=tasks,
            dataset_tags=dataset_tags,
            dataset=dataset,
            dataset_args=dataset_args,
        )
        model_card = training_summary.to_model_card()
        with open(os.path.join(self.args.output_dir, "README.md"), "w") as f:
            f.write(model_card)

    def _push_from_checkpoint(self, checkpoint_folder):
        # Only push from one node.
        if not self.is_world_process_zero() or self.args.hub_strategy == HubStrategy.END:
            return
        # If we haven't finished the last push, we don't do this one.
        if self.push_in_progress is not None and not self.push_in_progress.is_done:
            return

        output_dir = self.args.output_dir
        # To avoid a new synchronization of all model weights, we just copy the file from the checkpoint folder
        modeling_files = [CONFIG_NAME, WEIGHTS_NAME]
        for modeling_file in modeling_files:
            if os.path.isfile(os.path.join(checkpoint_folder, modeling_file)):
                shutil.copy(os.path.join(checkpoint_folder, modeling_file), os.path.join(output_dir, modeling_file))
        # Saving the tokenizer is fast and we don't know how many files it may have spawned, so we resave it to be sure.
        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)
        # Same for the training arguments
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

        try:
            if self.args.hub_strategy == HubStrategy.CHECKPOINT:
                # Temporarily move the checkpoint just saved for the push
                tmp_checkpoint = os.path.join(output_dir, "last-checkpoint")
                # We have to remove the "last-checkpoint" dir if it exists, otherwise the checkpoint is moved as a
                # subfolder.
                if os.path.isdir(tmp_checkpoint):
                    shutil.rmtree(tmp_checkpoint)
                shutil.move(checkpoint_folder, tmp_checkpoint)

            if self.args.save_strategy == IntervalStrategy.STEPS:
                commit_message = f"Training in progress, step {self.state.global_step}"
            else:
                commit_message = f"Training in progress, epoch {int(self.state.epoch)}"
            _, self.push_in_progress = self.repo.push_to_hub(
                commit_message=commit_message, blocking=False, auto_lfs_prune=True
            )
        finally:
            if self.args.hub_strategy == HubStrategy.CHECKPOINT:
                # Move back the checkpoint to its place
                shutil.move(tmp_checkpoint, checkpoint_folder)

    def push_to_hub(self, commit_message: Optional[str] = "End of training", blocking: bool = True, **kwargs) -> str:
        """
        Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.

        Parameters:
            commit_message (`str`, *optional*, defaults to `"End of training"`):
                Message to commit while pushing.
            blocking (`bool`, *optional*, defaults to `True`):
                Whether the function should return only when the `git push` has finished.
            kwargs:
                Additional keyword arguments passed along to [`~Trainer.create_model_card`].

        Returns:
            The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of
            the commit and an object to track the progress of the commit if `blocking=True`
        """
        # If a user calls manually `push_to_hub` with `self.args.push_to_hub = False`, we try to create the repo but
        # it might fail.
        if not hasattr(self, "repo"):
            self.init_git_repo()

        model_name = kwargs.pop("model_name", None)
        if model_name is None and self.args.should_save:
            if self.args.hub_model_id is None:
                model_name = Path(self.args.output_dir).name
            else:
                model_name = self.args.hub_model_id.split("/")[-1]

        # Needs to be executed on all processes for TPU training, but will only save on the processed determined by
        # self.args.should_save.
        self.save_model(_internal_call=True)

        # Only push from one node.
        if not self.is_world_process_zero():
            return

        # Cancel any async push in progress if blocking=True. The commits will all be pushed together.
        if blocking and self.push_in_progress is not None and not self.push_in_progress.is_done:
            self.push_in_progress._process.kill()
            self.push_in_progress = None

        git_head_commit_url = self.repo.push_to_hub(
            commit_message=commit_message, blocking=blocking, auto_lfs_prune=True
        )
        # push separately the model card to be independant from the rest of the model
        if self.args.should_save:
            self.create_model_card(model_name=model_name, **kwargs)
            try:
                self.repo.push_to_hub(
                    commit_message="update model card README.md", blocking=blocking, auto_lfs_prune=True
                )
            except EnvironmentError as exc:
                logger.error(f"Error pushing update to the model card. Please read logs and retry.\n${exc}")

        return git_head_commit_url

    #
    # Deprecated code
    #

    def prediction_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """
        args = self.args

        if not has_length(dataloader):
            raise ValueError("dataloader must implement a working __len__")

        prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only

        # if eval is called w/o train init deepspeed here
        if args.deepspeed and not self.deepspeed:
            # XXX: eval doesn't have `resume_from_checkpoint` arg but we should be able to do eval
            # from the checkpoint eventually
            deepspeed_engine, _, _ = deepspeed_init(self, num_training_steps=0, resume_from_checkpoint=None)
            self.model = deepspeed_engine.module
            self.model_wrapped = deepspeed_engine
            self.deepspeed = deepspeed_engine
            # XXX: we don't need optim/sched for inference, but this needs to be sorted out, since
            # for example the Z3-optimizer is a must for zero3 to work even for inference - what we
            # don't need is the deepspeed basic optimizer which is self.optimizer.optimizer
            deepspeed_engine.optimizer.optimizer = None
            deepspeed_engine.lr_scheduler = None

        model = self._wrap_model(self.model, training=False, dataloader=dataloader)

        # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
        # while ``train`` is running, cast it to the right dtype first and then put on device
        if not self.is_in_train:
            if args.fp16_full_eval:
                model = model.to(dtype=torch.float16, device=args.device)
            elif args.bf16_full_eval:
                model = model.to(dtype=torch.bfloat16, device=args.device)

        batch_size = dataloader.batch_size
        num_examples = self.num_examples(dataloader)
        logger.info(f"***** Running {description} *****")
        logger.info(f"  Num examples = {num_examples}")
        logger.info(f"  Batch size = {batch_size}")
        losses_host: torch.Tensor = None
        preds_host: Union[torch.Tensor, List[torch.Tensor]] = None
        labels_host: Union[torch.Tensor, List[torch.Tensor]] = None
        inputs_host: Union[torch.Tensor, List[torch.Tensor]] = None

        world_size = max(1, args.world_size)

        eval_losses_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=batch_size)
        if not prediction_loss_only:
            # The actual number of eval_sample can be greater than num_examples in distributed settings (when we pass
            # a batch size to the sampler)
            make_multiple_of = None
            if hasattr(dataloader, "sampler") and isinstance(dataloader.sampler, SequentialDistributedSampler):
                make_multiple_of = dataloader.sampler.batch_size
            preds_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)
            labels_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)
            inputs_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)

        model.eval()

        if is_torch_tpu_available():
            dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)

        if args.past_index >= 0:
            self._past = None

        self.callback_handler.eval_dataloader = dataloader

        for step, inputs in enumerate(dataloader):
            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
            inputs_decode = self._prepare_input(inputs["input_ids"]) if args.include_inputs_for_metrics else None

            if loss is not None:
                losses = loss.repeat(batch_size)
                losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
            if logits is not None:
                preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
            if labels is not None:
                labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)
            if inputs_decode is not None:
                inputs_host = (
                    inputs_decode
                    if inputs_host is None
                    else nested_concat(inputs_host, inputs_decode, padding_index=-100)
                )
            self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)

            # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
            if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:
                eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, "eval_losses"))
                if not prediction_loss_only:
                    preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, "eval_preds"))
                    labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, "eval_label_ids"))
                    inputs_gatherer.add_arrays(self._gather_and_numpify(inputs_host, "eval_inputs_ids"))

                # Set back to None to begin a new accumulation
                losses_host, preds_host, labels_host, inputs_host = None, None, None, None

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        # Gather all remaining tensors and put them back on the CPU
        eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, "eval_losses"))
        if not prediction_loss_only:
            preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, "eval_preds"))
            labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, "eval_label_ids"))
            inputs_gatherer.add_arrays(self._gather_and_numpify(inputs_host, "eval_inputs_ids"))

        eval_loss = eval_losses_gatherer.finalize()
        preds = preds_gatherer.finalize() if not prediction_loss_only else None
        label_ids = labels_gatherer.finalize() if not prediction_loss_only else None
        inputs_ids = inputs_gatherer.finalize() if not prediction_loss_only else None

        if self.compute_metrics is not None and preds is not None and label_ids is not None:
            if args.include_inputs_for_metrics:
                metrics = self.compute_metrics(
                    EvalPrediction(predictions=preds, label_ids=label_ids, inputs=inputs_ids)
                )
            else:
                metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
        else:
            metrics = {}

        # To be JSON-serializable, we need to remove numpy types or zero-d tensors
        metrics = denumpify_detensorize(metrics)

        if eval_loss is not None:
            metrics[f"{metric_key_prefix}_loss"] = eval_loss.mean().item()

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()):
            if not key.startswith(f"{metric_key_prefix}_"):
                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)

        return EvalLoopOutput(predictions=preds, label_ids=label_ids, metrics=metrics, num_samples=num_examples)

    def _gather_and_numpify(self, tensors, name):
        """
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        """
        if tensors is None:
            return
        if is_torch_tpu_available():
            tensors = nested_xla_mesh_reduce(tensors, name)
        elif is_sagemaker_mp_enabled():
            tensors = smp_gather(tensors)
        elif self.args.local_rank != -1:
            tensors = distributed_concat(tensors)

        return nested_numpify(tensors)

    def _add_sm_patterns_to_gitignore(self) -> None:
        """Add SageMaker Checkpointing patterns to .gitignore file."""
        # Make sure we only do this on the main process
        if not self.is_world_process_zero():
            return

        patterns = ["*.sagemaker-uploading", "*.sagemaker-uploaded"]

        # Get current .gitignore content
        if os.path.exists(os.path.join(self.repo.local_dir, ".gitignore")):
            with open(os.path.join(self.repo.local_dir, ".gitignore"), "r") as f:
                current_content = f.read()
        else:
            current_content = ""

        # Add the patterns to .gitignore
        content = current_content
        for pattern in patterns:
            if pattern not in content:
                if content.endswith("\n"):
                    content += pattern
                else:
                    content += f"\n{pattern}"

        # Write the .gitignore file if it has changed
        if content != current_content:
            with open(os.path.join(self.repo.local_dir, ".gitignore"), "w") as f:
                logger.debug(f"Writing .gitignore file. Content: {content}")
                f.write(content)

        self.repo.git_add(".gitignore")

        # avoid race condition with git status
        time.sleep(0.5)

        if not self.repo.is_repo_clean():
            self.repo.git_commit("Add *.sagemaker patterns to .gitignore.")
            self.repo.git_push()