




'''simple docstring'''







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 			_a  (	__UpperCAmelCase							,							unittest.TestCase ):
       __a				:     Any   				=					MvpTokenizer
       __a				:     int   				=					MvpTokenizerFast
       __a				:     Tuple   				=					True
       __a				:     List[Any]   				=					filter_roberta_detectors
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase			       =		[
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''\u0120''',
               '''\u0120l''',
               '''\u0120n''',
               '''\u0120lo''',
               '''\u0120low''',
               '''er''',
               '''\u0120lowest''',
               '''\u0120newer''',
               '''\u0120wider''',
               '''<unk>''',
           ]
           UpperCAmelCase			       =		dict(zip(lowerCamelCase__   ,				range(len(lowerCamelCase__				)				)				)				)
           UpperCAmelCase			       =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
           UpperCAmelCase			       =		{'''unk_token''': '''<unk>'''}

           UpperCAmelCase			       =		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']				)
           UpperCAmelCase			       =		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']				)
           with open(self.vocab_file   ,				'''w'''   ,				encoding='''utf-8'''				) as fp:
               fp.write(json.dumps(lowerCamelCase__				) + '''\n'''				)
           with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8'''				) as fp:
               fp.write('''\n'''.join(lowerCamelCase__				)				)
       def     A      (	self						:	Union[str, Any]   ,				**lowercase						:	List[Any]				):



           '''simple docstring'''

           kwargs.update(self.special_tokens_map				)
           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowerCamelCase__				)
       def     A      (	self						:	str   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           kwargs.update(self.special_tokens_map				)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowerCamelCase__				)
       def     A      (	self						:	Dict   ,				lowercase						:	List[Any]				):



           '''simple docstring'''

           return "lower newer", "lower newer"
       @cached_property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''				)
       @cached_property
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''				)
       @require_torch
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           UpperCAmelCase			       =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase			       =		tokenizer(lowerCamelCase__   ,				max_length=len(lowerCamelCase__				)   ,				padding=lowerCamelCase__   ,				return_tensors='''pt'''				)
               self.assertIsInstance(lowerCamelCase__   ,				lowerCamelCase__				)

               self.assertEqual((2, 9)   ,				batch.input_ids.shape				)
               self.assertEqual((2, 9)   ,				batch.attention_mask.shape				)
               UpperCAmelCase			       =		batch.input_ids.tolist()[0]
               self.assertListEqual(lowerCamelCase__   ,				lowerCamelCase__				)
               # Test that special tokens are reset
       @require_torch
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase			       =		tokenizer(lowerCamelCase__   ,				padding=lowerCamelCase__   ,				return_tensors='''pt'''				)
               # check if input_ids are returned and no labels
               self.assertIn('''input_ids'''   ,				lowerCamelCase__				)
               self.assertIn('''attention_mask'''   ,				lowerCamelCase__				)
               self.assertNotIn('''labels'''   ,				lowerCamelCase__				)
               self.assertNotIn('''decoder_attention_mask'''   ,				lowerCamelCase__				)
       @require_torch
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase			       =		tokenizer(text_target=lowerCamelCase__   ,				max_length=32   ,				padding='''max_length'''   ,				return_tensors='''pt'''				)
               self.assertEqual(32   ,				targets['''input_ids'''].shape[1]				)
       @require_torch
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase			       =		tokenizer(
                   ['''I am a small frog''' * 1_024, '''I am a small frog''']   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				return_tensors='''pt'''				)
               self.assertIsInstance(lowerCamelCase__   ,				lowerCamelCase__				)
               self.assertEqual(batch.input_ids.shape   ,				(2, 1_024)				)
       @require_torch
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		['''A long paragraph for summarization.''']
           UpperCAmelCase			       =		[
               '''Summary of the text.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               UpperCAmelCase			       =		tokenizer(lowerCamelCase__   ,				text_target=lowerCamelCase__   ,				return_tensors='''pt'''				)
               UpperCAmelCase			       =		inputs['''input_ids''']
               UpperCAmelCase			       =		inputs['''labels''']
               self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
               self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
               self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
               self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           pass







       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                   UpperCAmelCase			       =		self.rust_tokenizer_class.from_pretrained(lowerCamelCase__   ,				**lowerCamelCase__				)
                   UpperCAmelCase			       =		self.tokenizer_class.from_pretrained(lowerCamelCase__   ,				**lowerCamelCase__				)
                   UpperCAmelCase			       =		'''A, <mask> AllenNLP sentence.'''
                   UpperCAmelCase			       =		tokenizer_r.encode_plus(lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__				)
                   UpperCAmelCase			       =		tokenizer_p.encode_plus(lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__				)

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['''token_type_ids''']				)   ,				sum(tokens_p['''token_type_ids''']				)				)

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)   ,				sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)   ,				)

                   UpperCAmelCase			       =		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                   UpperCAmelCase			       =		tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['''input_ids''']   ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
                   self.assertSequenceEqual(tokens_r['''input_ids''']   ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

                   self.assertSequenceEqual(
                       lowerCamelCase__   ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
                   self.assertSequenceEqual(
                       lowerCamelCase__   ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)





"""simple docstring"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for i, input_name in enumerate(node_proto.input       ):
								if input_name == name:
												node_proto.input.insert(lowerCamelCase					,			lowerCamelCase       )
												node_proto.input.pop(i + 1       )

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )
								_graph_replace_input_with(node_proto.attribute[1].g					,			lowerCamelCase					,			lowerCamelCase       )
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							list(model.graph.initializer       )
				UpperCAmelCase__             =							list(model_without_ext.graph.initializer       )
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								UpperCAmelCase__             =							inits[i].name
								UpperCAmelCase__             =							inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]       )

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph					,			lowerCamelCase					,			lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							os.path.dirname(lowerCamelCase       )
				UpperCAmelCase__             =							os.path.basename(lowerCamelCase       )

				UpperCAmelCase__             =							onnx.load(os.path.join(lowerCamelCase					,			lowerCamelCase       )       )

				UpperCAmelCase__             =							list(model.graph.initializer       )

				UpperCAmelCase__             =							set()
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							[]

				UpperCAmelCase__             =							0

				for i in range(len(lowerCamelCase       )       ):
								if i in dup_set:
												continue

								for j in range(i + 1					,			len(lowerCamelCase       )       ):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]					,			inits[j]       ):
																dup_set.add(lowerCamelCase       )
																dup_set.add(lowerCamelCase       )

																UpperCAmelCase__             =							inits[j].data_type
																UpperCAmelCase__             =							numpy.prod(inits[j].dims       )
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 1_1:
																				mem_size *= 8
																else:
																				print('unexpected data type: '					,			lowerCamelCase       )
																total_reduced_size += mem_size

																UpperCAmelCase__             =							inits[i].name
																UpperCAmelCase__             =							inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCamelCase       )
																else:
																				UpperCAmelCase__             =							[name_j]
																ind_to_replace.append((j, i)       )

				print('total reduced size: '					,			total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4					,			'GB'       )

				UpperCAmelCase__             =							sorted(lowerCamelCase       )
				_remove_dup_initializers_from_model(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				UpperCAmelCase__             =							'optimized_' + model_file_name
				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			lowerCamelCase       )
				onnx.save(lowerCamelCase					,			lowerCamelCase       )

				return new_model



