Spaces:
Runtime error
Runtime error
Delete mplug_docowl/model/convert_mplug_docowl_weight_to_hf_v2.py
Browse files
mplug_docowl/model/convert_mplug_docowl_weight_to_hf_v2.py
DELETED
@@ -1,320 +0,0 @@
|
|
1 |
-
# Copyright 2023 DAMO Academy and The HuggingFace Inc. team. All rights reserved.
|
2 |
-
#
|
3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
-
# you may not use this file except in compliance with the License.
|
5 |
-
# You may obtain a copy of the License at
|
6 |
-
#
|
7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
-
#
|
9 |
-
# Unless required by applicable law or agreed to in writing, software
|
10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
-
# See the License for the specific language governing permissions and
|
13 |
-
# limitations under the License.
|
14 |
-
import argparse
|
15 |
-
import gc
|
16 |
-
import json
|
17 |
-
import math
|
18 |
-
import os
|
19 |
-
import shutil
|
20 |
-
import warnings
|
21 |
-
|
22 |
-
import torch
|
23 |
-
|
24 |
-
from transformers import LlamaTokenizer
|
25 |
-
from .configuration_mplug_docowl import MPLUGDocOwlConfig
|
26 |
-
from icecream import ic
|
27 |
-
|
28 |
-
try:
|
29 |
-
from transformers import LlamaTokenizerFast
|
30 |
-
except ImportError as e:
|
31 |
-
warnings.warn(e)
|
32 |
-
warnings.warn(
|
33 |
-
"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
|
34 |
-
)
|
35 |
-
LlamaTokenizerFast = None
|
36 |
-
|
37 |
-
"""
|
38 |
-
Sample usage:
|
39 |
-
|
40 |
-
```
|
41 |
-
python3 /pure-mlo-scratch/sfan/model-parallel-trainer/llama2megatron/convert_llama2hf.py \
|
42 |
-
--input_dir /pure-mlo-scratch/llama/ --model_size 7 --output_dir /pure-mlo-scratch/llama/converted_HF_7B
|
43 |
-
```
|
44 |
-
|
45 |
-
Thereafter, models can be loaded via:
|
46 |
-
|
47 |
-
```py
|
48 |
-
from transformers import LlamaForCausalLM, LlamaTokenizer
|
49 |
-
|
50 |
-
model = LlamaForCausalLM.from_pretrained("/output/path")
|
51 |
-
tokenizer = LlamaTokenizer.from_pretrained("/output/path")
|
52 |
-
```
|
53 |
-
|
54 |
-
Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
|
55 |
-
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
|
56 |
-
"""
|
57 |
-
|
58 |
-
llama_s2layer = {7: 32, 13: 40, 30: 60, 65: 80, 70: 80}
|
59 |
-
llama_s2heads = {7: 32, 13: 40, 30: 52, 65: 64, 70: 64}
|
60 |
-
llama_s2dense = {7: 11008, 13: 13824, 30: 17920, 65: 22016,
|
61 |
-
70: 28672} # should be (2/3)*4*d, but it isn't exaclty that
|
62 |
-
llama_s2hidden = {7: 4096, 13: 5120, 32: 6656, 65: 8192, 70: 8192}
|
63 |
-
|
64 |
-
|
65 |
-
def compute_intermediate_size(n):
|
66 |
-
return int(math.ceil(n * 8 / 3) + 255) // 256 * 256
|
67 |
-
|
68 |
-
|
69 |
-
def read_json(path):
|
70 |
-
with open(path, "r") as f:
|
71 |
-
return json.load(f)
|
72 |
-
|
73 |
-
|
74 |
-
def write_json(text, path):
|
75 |
-
with open(path, "w") as f:
|
76 |
-
json.dump(text, f)
|
77 |
-
|
78 |
-
|
79 |
-
def write_model(model_path,
|
80 |
-
input_base_path,
|
81 |
-
model_size,
|
82 |
-
num_input_shards=1,
|
83 |
-
num_output_shards=2,
|
84 |
-
skip_permute=True,
|
85 |
-
norm_eps=1e-05):
|
86 |
-
# if os.path.exists(model_path):
|
87 |
-
# shutil.rmtree(model_path)
|
88 |
-
os.makedirs(model_path, exist_ok=True)
|
89 |
-
# tmp_model_path = os.path.join(model_path, "tmp")
|
90 |
-
tmp_model_path = model_path
|
91 |
-
os.makedirs(tmp_model_path, exist_ok=True)
|
92 |
-
|
93 |
-
num_shards = num_input_shards
|
94 |
-
n_layers = llama_s2layer[model_size]
|
95 |
-
n_heads = llama_s2heads[model_size]
|
96 |
-
n_heads_per_shard = n_heads // num_shards
|
97 |
-
n_dense = llama_s2dense[model_size]
|
98 |
-
n_hidden = llama_s2hidden[model_size]
|
99 |
-
hidden_per_head = n_hidden // n_heads
|
100 |
-
base = 10000.0
|
101 |
-
inv_freq = 1.0 / (base ** (torch.arange(0, hidden_per_head, 2).float() / hidden_per_head))
|
102 |
-
|
103 |
-
# permute for sliced rotary
|
104 |
-
def permute(w, skip_permute=skip_permute):
|
105 |
-
if skip_permute:
|
106 |
-
return w
|
107 |
-
return w.view(n_heads, n_hidden // n_heads // 2, 2, n_hidden).transpose(1, 2).reshape(n_hidden, n_hidden)
|
108 |
-
|
109 |
-
print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
|
110 |
-
# Load weights
|
111 |
-
if num_shards==1:
|
112 |
-
# Not sharded
|
113 |
-
# (The sharded implementation would also work, but this is simpler.)
|
114 |
-
# /pure-mlo-scratch/alhernan/megatron-data/checkpoints/llama2-7b-tp4-pp1-optim/release/mp_rank_00/model_optim_rng.pt
|
115 |
-
if os.path.exists(os.path.join(input_base_path, 'release')):
|
116 |
-
filename = os.path.join(input_base_path, 'release', 'mp_rank_00', 'model_optim_rng.pt')
|
117 |
-
elif input_base_path.split('/')[-1].startswith('iter_'):
|
118 |
-
iteration = eval(input_base_path.split('/')[-1].replace('iter_', '').lstrip('0'))
|
119 |
-
load_dir = '/'.join(input_base_path.split('/')[:-1])
|
120 |
-
filename = os.path.join(input_base_path, 'mp_rank_00', 'model_optim_rng.pt')
|
121 |
-
if not os.path.exists(filename):
|
122 |
-
filename = filename.replace('model_optim_rng.pt', 'model_rng.pt')
|
123 |
-
else:
|
124 |
-
tracker_filename = os.path.join(input_base_path, 'latest_checkpointed_iteration.txt')
|
125 |
-
with open(tracker_filename, 'r') as f:
|
126 |
-
metastring = f.read().strip()
|
127 |
-
iteration = 'iter_{:07d}'.format(int(metastring))
|
128 |
-
filename = os.path.join(input_base_path, iteration, 'mp_rank_00', 'model_optim_rng.pt')
|
129 |
-
if not os.path.exists(filename):
|
130 |
-
filename = filename.replace('model_optim_rng.pt', 'model_rng.pt')
|
131 |
-
original_filename = filename
|
132 |
-
loaded = torch.load(filename, map_location="cpu")['model']['language_model']
|
133 |
-
|
134 |
-
else:
|
135 |
-
# Sharded
|
136 |
-
filenames = []
|
137 |
-
for i in range(num_shards):
|
138 |
-
if os.path.exists(os.path.join(input_base_path, 'release')):
|
139 |
-
filename = os.path.join(input_base_path, 'release', f'mp_rank_{i:02d}', 'model_optim_rng.pt')
|
140 |
-
else:
|
141 |
-
tracker_filename = os.path.join(input_base_path, 'latest_checkpointed_iteration.txt')
|
142 |
-
with open(tracker_filename, 'r') as f:
|
143 |
-
metastring = f.read().strip()
|
144 |
-
iteration = 'iter_{:07d}'.format(int(metastring))
|
145 |
-
filename = os.path.join(input_base_path, iteration, f'mp_rank_{i:02d}', 'model_optim_rng.pt')
|
146 |
-
if not os.path.exists(filename):
|
147 |
-
filename = filename.replace('model_optim_rng.pt', 'model_rng.pt')
|
148 |
-
filenames.append(filename)
|
149 |
-
loaded = [
|
150 |
-
torch.load(filenames[i], map_location="cpu")['model']['language_model']
|
151 |
-
for i in range(num_shards)
|
152 |
-
]
|
153 |
-
|
154 |
-
print('Llama-Megatron Loaded!')
|
155 |
-
param_count = 0
|
156 |
-
index_dict = {"weight_map": {}}
|
157 |
-
state_dict = {}
|
158 |
-
print(f'Weighted Converting for {n_layers} layers...')
|
159 |
-
for layer_i in range(n_layers):
|
160 |
-
print(layer_i)
|
161 |
-
# filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
|
162 |
-
if num_shards == 1:
|
163 |
-
# Unsharded
|
164 |
-
state_dict.update({
|
165 |
-
f"model.layers.{layer_i}.self_attn.q_proj.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.q_proj.weight"],
|
166 |
-
f"model.layers.{layer_i}.self_attn.k_proj.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.k_proj.multiway.0.weight"],
|
167 |
-
f"model.layers.{layer_i}.self_attn.v_proj.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.v_proj.multiway.0.weight"],
|
168 |
-
f"model.layers.{layer_i}.self_attn.k_proj.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.k_proj.multiway.1.weight"],
|
169 |
-
f"model.layers.{layer_i}.self_attn.v_proj.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.v_proj.multiway.1.weight"],
|
170 |
-
f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.o_proj.weight"],
|
171 |
-
f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded['encoder'][f"layers.{layer_i}.mlp.gate_proj.weight"],
|
172 |
-
f"model.layers.{layer_i}.mlp.down_proj.weight": loaded['encoder'][f"layers.{layer_i}.mlp.down_proj.weight"],
|
173 |
-
f"model.layers.{layer_i}.mlp.up_proj.weight": loaded['encoder'][f"layers.{layer_i}.mlp.up_proj.weight"],
|
174 |
-
f"model.layers.{layer_i}.input_layernorm.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.input_layernorm.multiway.0.weight"],
|
175 |
-
f"model.layers.{layer_i}.post_attention_layernorm.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.post_attention_layernorm.multiway.0.weight"],
|
176 |
-
f"model.layers.{layer_i}.input_layernorm.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.input_layernorm.multiway.1.weight"],
|
177 |
-
f"model.layers.{layer_i}.post_attention_layernorm.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.post_attention_layernorm.multiway.1.weight"],
|
178 |
-
})
|
179 |
-
else:
|
180 |
-
raise NotImplemented
|
181 |
-
|
182 |
-
state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
|
183 |
-
for k, v in state_dict.items():
|
184 |
-
index_dict["weight_map"][k] = filename
|
185 |
-
param_count += v.numel()
|
186 |
-
# torch.save(state_dict, os.path.join(tmp_model_path, filename))
|
187 |
-
# print(f'Sharded file saved to {filename}')
|
188 |
-
|
189 |
-
# filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
|
190 |
-
filename = "pytorch_model.bin"
|
191 |
-
if num_shards==1:
|
192 |
-
# Unsharded
|
193 |
-
state_dict.update({
|
194 |
-
"model.embed_tokens.weight": loaded['embedding']['word_embeddings']['weight'],
|
195 |
-
"model.norm.weight": loaded['encoder']['norm.weight'],
|
196 |
-
"lm_head.weight": loaded['encoder']['lm_head.weight'],
|
197 |
-
})
|
198 |
-
else:
|
199 |
-
state_dict.update({
|
200 |
-
"model.embed_tokens.weight": loaded[0]['embedding']['word_embeddings']['weight'],
|
201 |
-
"model.norm.weight": loaded[0]['encoder']['norm.weight'],
|
202 |
-
"lm_head.weight": loaded[0]['encoder']['lm_head.weight'],
|
203 |
-
})
|
204 |
-
|
205 |
-
loaded_all = torch.load(original_filename, map_location="cpu")['model']
|
206 |
-
# Vision Part
|
207 |
-
state_dict.update({
|
208 |
-
"model.vision_model.embeddings.cls_token": loaded_all['vision_model']['cls_token'],
|
209 |
-
"model.vision_model.embeddings.patch_embed.weight": loaded_all['vision_model']['patch_embed']['weight'],
|
210 |
-
"model.vision_model.embeddings.position_embedding": loaded_all['vision_model']['position_embeddings'],
|
211 |
-
"model.vision_model.embeddings.pre_layernorm.bias": loaded_all['vision_model']['pre_layernorm']['bias'],
|
212 |
-
"model.vision_model.embeddings.pre_layernorm.weight": loaded_all['vision_model']['pre_layernorm']['weight'],
|
213 |
-
"model.vision_model.post_layernorm.bias": loaded_all['vision_model']['transformer']['final_layernorm.bias'],
|
214 |
-
"model.vision_model.post_layernorm.weight": loaded_all['vision_model']['transformer']['final_layernorm.weight'],
|
215 |
-
})
|
216 |
-
for v_layer_idx in range(24):
|
217 |
-
state_dict.update({
|
218 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.input_layernorm.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.input_layernorm.bias'],
|
219 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.input_layernorm.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.input_layernorm.weight'],
|
220 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc1.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_h_to_4h.bias'],
|
221 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc1.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_h_to_4h.weight'],
|
222 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc2.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_4h_to_h.bias'],
|
223 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc2.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_4h_to_h.weight'],
|
224 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.post_attention_layernorm.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.post_attention_layernorm.bias'],
|
225 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.post_attention_layernorm.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.post_attention_layernorm.weight'],
|
226 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.dense.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.dense.bias'],
|
227 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.dense.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.dense.weight'],
|
228 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.query_key_value.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.query_key_value.bias'],
|
229 |
-
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.query_key_value.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.query_key_value.weight'],
|
230 |
-
})
|
231 |
-
|
232 |
-
# Vision2Text Part: HReducer
|
233 |
-
state_dict.update({
|
234 |
-
"model.vision2text.ln_q.weight": loaded_all['hreducer3']['ln_q']['weight'],
|
235 |
-
"model.vision2text.ln_q.bias": loaded_all['hreducer3']['ln_q']['bias'],
|
236 |
-
"model.vision2text.visual_fc.bias": loaded_all['hreducer3']['visual_fc']['bias'],
|
237 |
-
"model.vision2text.visual_fc.weight": loaded_all['hreducer3']['visual_fc']['weight'],
|
238 |
-
"model.vision2text.vit_eos": loaded_all['hreducer3']['vit_eos'],
|
239 |
-
})
|
240 |
-
# reducer_before conv (layer 0) + gleu (layer 1)
|
241 |
-
state_dict.update({
|
242 |
-
f"model.vision2text.reducer_before.0.weight": loaded_all['hreducer3']['reducer_before']["0.weight"],
|
243 |
-
f"model.vision2text.reducer_before.0.bias": loaded_all['hreducer3']['reducer_before']["0.bias"],
|
244 |
-
})
|
245 |
-
# reducer conv
|
246 |
-
state_dict.update({
|
247 |
-
f"model.vision2text.reducer.weight": loaded_all['hreducer3']['reducer']["weight"],
|
248 |
-
f"model.vision2text.reducer.bias": loaded_all['hreducer3']['reducer']["bias"],
|
249 |
-
})
|
250 |
-
|
251 |
-
for k, v in state_dict.items():
|
252 |
-
# ic(k, v)
|
253 |
-
index_dict["weight_map"][k] = filename
|
254 |
-
param_count += v.numel()
|
255 |
-
torch.save(state_dict, os.path.join(tmp_model_path, filename))
|
256 |
-
print(f'save to {os.path.join(tmp_model_path, filename)}')
|
257 |
-
|
258 |
-
# Write configs
|
259 |
-
index_dict["metadata"] = {"total_size": param_count * 2}
|
260 |
-
write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
|
261 |
-
|
262 |
-
config = MPLUGDocOwlConfig()
|
263 |
-
config.save_pretrained(tmp_model_path)
|
264 |
-
|
265 |
-
# Make space so we can load the model properly now.
|
266 |
-
del state_dict
|
267 |
-
del loaded
|
268 |
-
del loaded_all
|
269 |
-
gc.collect()
|
270 |
-
|
271 |
-
def write_tokenizer(tokenizer_path, input_tokenizer_path):
|
272 |
-
# Initialize the tokenizer based on the `spm` model
|
273 |
-
tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
|
274 |
-
print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
|
275 |
-
tokenizer = tokenizer_class(input_tokenizer_path)
|
276 |
-
tokenizer.save_pretrained(tokenizer_path)
|
277 |
-
|
278 |
-
|
279 |
-
def main():
|
280 |
-
parser = argparse.ArgumentParser()
|
281 |
-
parser.add_argument(
|
282 |
-
"--input_dir",
|
283 |
-
help="Location of LLaMA_Megatron weights",
|
284 |
-
)
|
285 |
-
parser.add_argument(
|
286 |
-
"--model_size",
|
287 |
-
type=int,
|
288 |
-
default=7,
|
289 |
-
choices=[7, 13, 30, 65, 70],
|
290 |
-
)
|
291 |
-
parser.add_argument(
|
292 |
-
"--num_input_shards",
|
293 |
-
type=int,
|
294 |
-
default=1,
|
295 |
-
)
|
296 |
-
parser.add_argument(
|
297 |
-
"--num_output_shards",
|
298 |
-
type=int,
|
299 |
-
default=1,
|
300 |
-
)
|
301 |
-
parser.add_argument('--skip_permute', action='store_true')
|
302 |
-
|
303 |
-
parser.add_argument(
|
304 |
-
"--output_dir",
|
305 |
-
help="Location to write HF model and tokenizer",
|
306 |
-
)
|
307 |
-
|
308 |
-
args = parser.parse_args()
|
309 |
-
write_model(
|
310 |
-
model_path=args.output_dir,
|
311 |
-
input_base_path=args.input_dir,
|
312 |
-
model_size=args.model_size,
|
313 |
-
num_input_shards=args.num_input_shards,
|
314 |
-
num_output_shards=args.num_output_shards,
|
315 |
-
skip_permute=args.skip_permute
|
316 |
-
)
|
317 |
-
|
318 |
-
|
319 |
-
if __name__ == "__main__":
|
320 |
-
main()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|