





'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow

class      snake_case     :




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Optional[Any]							,	__A       :							Any							,	__A       :							Union[str, Any]	):
        return None






class      snake_case     :




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Optional[Any]							,	__A       :							Union[str, Any]							,	__A       :							Dict							,	__A       :							Optional[int]	):
        return None






class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=[
     # (model_name, model_kwargs)
     ("bert-base-cased", {}),
     ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
 ]






 @require_tf
 @slow
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
               self._test_export(__A							,	'tf'							,	1_2							,	**__A	)






 @require_torch
 @slow
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
               self._test_export(__A							,	'pt'							,	1_2							,	**__A	)






 @require_torch
 @slow
 def        _lowerCamelCase					(       self       :							Any	):
        from transformers import BertModel

        __UpperCamelCase       =  ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
        with NamedTemporaryFile(mode='w+t'	) as vocab_file:
               vocab_file.write('\n'.join(__A	)	)
               vocab_file.flush()
               __UpperCamelCase       =  BertTokenizerFast(vocab_file.name	)

        with TemporaryDirectory() as bert_save_dir:
               __UpperCamelCase       =  BertModel(BertConfig(vocab_size=len(__A	)	)	)
               model.save_pretrained(__A	)
               self._test_export(__A							,	'pt'							,	1_2							,	__A	)






 @require_tf
 @slow
 def        _lowerCamelCase					(       self       :							List[str]	):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
               __UpperCamelCase       =  self._test_export(__A							,	'tf'							,	1_2							,	**__A	)
               __UpperCamelCase       =  quantize(Path(__A	)	)

               # Ensure the actual quantized model is not bigger than the original one
               if quantized_path.stat().st_size >= Path(__A	).stat().st_size:
                      self.fail('Quantized model is bigger than initial ONNX model'	)






 @require_torch
 @slow
 def        _lowerCamelCase					(       self       :							int	):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
               __UpperCamelCase       =  self._test_export(__A							,	'pt'							,	1_2							,	**__A	)
               __UpperCamelCase       =  quantize(__A	)

               # Ensure the actual quantized model is not bigger than the original one
               if quantized_path.stat().st_size >= Path(__A	).stat().st_size:
                      self.fail('Quantized model is bigger than initial ONNX model'	)






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							Union[str, Any]							,	__A       :							Any							,	__A       :							str							,	__A       :							Tuple=None							,	**__A       :							Tuple	):
        try:
               # Compute path
               with TemporaryDirectory() as tempdir:
                      __UpperCamelCase       =  Path(__A	).joinpath('model.onnx'	)

               # Remove folder if exists
               if path.parent.exists():
                      path.parent.rmdir()

               # Export
               convert(__A							,	__A							,	__A							,	__A							,	__A							,	**__A	)

               return path
        except Exception as e:
               self.fail(__A	)






 @require_torch
 @require_tokenizers
 @slow
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        from transformers import BertModel

        __UpperCamelCase       =  BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'	)	)
        __UpperCamelCase       =  BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'	)
        self._test_infer_dynamic_axis(__A							,	__A							,	'pt'	)






 @require_tf
 @require_tokenizers
 @slow
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        from transformers import TFBertModel

        __UpperCamelCase       =  TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'	)	)
        __UpperCamelCase       =  BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'	)
        self._test_infer_dynamic_axis(__A							,	__A							,	'tf'	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							int							,	__A       :							Optional[int]							,	__A       :							int	):
        __UpperCamelCase       =  FeatureExtractionPipeline(__A							,	__A	)

        __UpperCamelCase       =  ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  infer_shapes(__A							,	__A	)

        # Assert all variables are present
        self.assertEqual(len(__A	)							,	len(__A	)	)
        self.assertTrue(all(var_name in shapes for var_name in variable_names	)	)
        self.assertSequenceEqual(variable_names[:3]							,	__A	)
        self.assertSequenceEqual(variable_names[3:]							,	__A	)

        # Assert inputs are {0: batch, 1: sequence}
        for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
               self.assertDictEqual(shapes[var_name]							,	{0: 'batch', 1: 'sequence'}	)

        # Assert outputs are {0: batch, 1: sequence} and {0: batch}
        self.assertDictEqual(shapes['output_0']							,	{0: 'batch', 1: 'sequence'}	)
        self.assertDictEqual(shapes['output_1']							,	{0: 'batch'}	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  ['input_ids', 'attention_mask', 'token_type_ids']
        __UpperCamelCase       =  {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
        __UpperCamelCase       ,						__UpperCamelCase       =  ensure_valid_input(FuncContiguousArgs()							,	__A							,	__A	)

        # Should have exactly the same number of args (all are valid)
        self.assertEqual(len(__A	)							,	3	)

        # Should have exactly the same input names
        self.assertEqual(set(__A	)							,	set(__A	)	)

        # Parameter should be reordered according to their respective place in the function:
        # (input_ids, token_type_ids, attention_mask)
        self.assertEqual(__A							,	(tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])	)

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        __UpperCamelCase       ,						__UpperCamelCase       =  ensure_valid_input(FuncNonContiguousArgs()							,	__A							,	__A	)

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(__A	)							,	1	)
        self.assertEqual(len(__A	)							,	1	)

        # Should have only "input_ids"
        self.assertEqual(inputs_args[0]							,	tokens['input_ids']	)
        self.assertEqual(ordered_input_names[0]							,	'input_ids'	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  generate_identified_filename(Path('/home/something/my_fake_model.onnx'	)							,	'-test'	)
        self.assertEqual('/home/something/my_fake_model-test.onnx'							,	generated.as_posix()	)







import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase						:   List[str]			  =     '''▁'''

UpperCAmelCase						:   Optional[Any]			  =     get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[int]					    =	BertGenerationTokenizer
				UpperCamelCase    :  str					    =	False
				UpperCamelCase    :  Tuple					    =	True



				def 					UpperCAmelCase_		(			self     ):
									super().setUp()

									__A	:						Tuple       =				BertGenerationTokenizer(_A     ,					keep_accents=_A     )
									tokenizer.save_pretrained(self.tmpdirname     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				'<s>'
									__A	:						str       =				1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A     )     ,					_A     )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A     )     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				list(self.get_tokenizer().get_vocab().keys()     )

									self.assertEqual(vocab_keys[0]     ,					'<unk>'     )
									self.assertEqual(vocab_keys[1]     ,					'<s>'     )
									self.assertEqual(vocab_keys[-1]     ,					'<pad>'     )
									self.assertEqual(len(_A     )     ,					1002     )



				def 					UpperCAmelCase_		(			self     ):
									self.assertEqual(self.get_tokenizer().vocab_size     ,					1000     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				BertGenerationTokenizer(_A     ,					keep_accents=_A     )

									__A	:						Dict       =				tokenizer.tokenize('This is a test'     )
									self.assertListEqual(_A     ,					['▁This', '▁is', '▁a', '▁t', 'est']     )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_A     )     ,					[285, 46, 10, 170, 382]     ,					)

									__A	:						int       =				tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
									self.assertListEqual(
									    _A     ,					[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '9',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        'é',
									        '.',
									    ]     ,					)
									__A	:						Dict       =				tokenizer.convert_tokens_to_ids(_A     )
									self.assertListEqual(
									    _A     ,					[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,					)

									__A	:						Optional[int]       =				tokenizer.convert_ids_to_tokens(_A     )
									self.assertListEqual(
									    _A     ,					[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '<unk>',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        '<unk>',
									        '.',
									    ]     ,					)



				@cached_property
				def 					UpperCAmelCase_		(			self     ):
									return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				'Hello World!'
									__A	:						Optional[Any]       =				[18536, 2260, 101]

									self.assertListEqual(_A     ,					self.big_tokenizer.encode(_A     )     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				(
									    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
									    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
									)
									__A	:						int       =				[
									    871,
									    419,
									    358,
									    946,
									    991,
									    2521,
									    452,
									    358,
									    1357,
									    387,
									    7751,
									    3536,
									    112,
									    985,
									    456,
									    126,
									    865,
									    938,
									    5400,
									    5734,
									    458,
									    1368,
									    467,
									    786,
									    2462,
									    5246,
									    1159,
									    633,
									    865,
									    4519,
									    457,
									    582,
									    852,
									    2557,
									    427,
									    916,
									    508,
									    405,
									    34324,
									    497,
									    391,
									    408,
									    11342,
									    1244,
									    385,
									    100,
									    938,
									    985,
									    456,
									    574,
									    362,
									    12597,
									    3200,
									    3129,
									    1172,
									]

									self.assertListEqual(_A     ,					self.big_tokenizer.encode(_A     )     )



				@require_torch
				@slow
				def 					UpperCAmelCase_		(			self     ):
									import torch

									from transformers import BertGenerationConfig, BertGenerationEncoder

									# Build sequence
									__A	:						Tuple       =				list(self.big_tokenizer.get_vocab().keys()     )[:10]
									__A	:						List[Any]       =				' '.join(_A     )
									__A	:						Union[str, Any]       =				self.big_tokenizer.encode_plus(_A     ,					return_tensors='pt'     ,					return_token_type_ids=_A     )
									__A	:						Optional[Any]       =				self.big_tokenizer.batch_encode_plus(
									    [sequence + ' ' + sequence]     ,					return_tensors='pt'     ,					return_token_type_ids=_A     )

									__A	:						int       =				BertGenerationConfig()
									__A	:						List[str]       =				BertGenerationEncoder(_A     )

									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
														model(**_A     )
														model(**_A     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									# fmt: off
									__A	:						str       =				{'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_A     ,					model_name='google/bert_for_seq_generation_L-24_bbc_encoder'     ,					revision='c817d1fd1be2ffa69431227a1fe320544943d4db'     ,					)




