





"""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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return None






class 						__snake_case   :





 """simple docstring"""


 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return None






class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


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

 @require_tf
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase   ,		'''tf'''   ,		12   ,		**__lowerCamelCase    )

 @require_torch
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase   ,		'''pt'''   ,		12   ,		**__lowerCamelCase    )

 @require_torch
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     from transformers import BertModel

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

     with TemporaryDirectory() as bert_save_dir:
         __A       :		str							=	BertModel(BertConfig(vocab_size=len(__lowerCamelCase    )    )    )
         model.save_pretrained(__lowerCamelCase    )
         self._test_export(__lowerCamelCase   ,		'''pt'''   ,		12   ,		__lowerCamelCase    )

 @require_tf
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         __A       :		List[str]							=	self._test_export(__lowerCamelCase   ,		'''tf'''   ,		12   ,		**__lowerCamelCase    )
         __A       :		List[str]							=	quantize(Path(__lowerCamelCase    )    )

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

 @require_torch
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         __A       :		Dict							=	self._test_export(__lowerCamelCase   ,		'''pt'''   ,		12   ,		**__lowerCamelCase    )
         __A       :		str							=	quantize(__lowerCamelCase    )

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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     try:
         # Compute path
         with TemporaryDirectory() as tempdir:
             __A       :		Tuple							=	Path(__lowerCamelCase    ).joinpath('''model.onnx'''    )

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

         # Export
         convert(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		**__lowerCamelCase    )

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

 @require_torch
 @require_tokenizers
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     from transformers import BertModel

     __A       :		List[Any]							=	BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
     __A       :		Optional[Any]							=	BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
     self._test_infer_dynamic_axis(__lowerCamelCase   ,		__lowerCamelCase   ,		'''pt'''    )

 @require_tf
 @require_tokenizers
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     from transformers import TFBertModel

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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Any							=	FeatureExtractionPipeline(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		int							=	['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
     __A					,			__A					,			__A					,			__A       :		Tuple							=	infer_shapes(__lowerCamelCase   ,		__lowerCamelCase    )

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

     # 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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	['''input_ids''', '''attention_mask''', '''token_type_ids''']
     __A       :		Tuple							=	{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
     __A					,			__A       :		Optional[int]							=	ensure_valid_input(FuncContiguousArgs()   ,		__lowerCamelCase   ,		__lowerCamelCase    )

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

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

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

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

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

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







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''    )   ,		'''-test'''    )
     self.assertEqual('''/home/something/my_fake_model-test.onnx'''   ,		generated.as_posix()    )











"""simple docstring"""



import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_					=     get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	AlbertTokenizer
 _lowerCamelCase								=	AlbertTokenizerFast
 _lowerCamelCase								=	True
 _lowerCamelCase								=	True
 _lowerCamelCase								=	True

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().setUp()

     # We have a SentencePiece fixture for testing
     __A       :		str							=	AlbertTokenizer(__lowerCamelCase    )
     tokenizer.save_pretrained(self.tmpdirname    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Tuple							=	'''this is a test'''
     __A       :		Union[str, Any]							=	'''this is a test'''
     return input_text, output_text

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	'''<pad>'''
     __A       :		Optional[int]							=	0

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

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	list(self.get_tokenizer().get_vocab().keys()    )

     self.assertEqual(vocab_keys[0]   ,		'''<pad>'''    )
     self.assertEqual(vocab_keys[1]   ,		'''<unk>'''    )
     self.assertEqual(vocab_keys[-1]   ,		'''▁eloquent'''    )
     self.assertEqual(len(__lowerCamelCase    )   ,		3_0000    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size   ,		3_0000    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A       :		Dict							=	self.get_tokenizer()
     __A       :		List[Any]							=	self.get_rust_tokenizer()

     __A       :		int							=	'''I was born in 92000, and this is falsé.'''

     __A       :		Any							=	tokenizer.tokenize(__lowerCamelCase    )
     __A       :		Dict							=	rust_tokenizer.tokenize(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		List[Any]							=	tokenizer.encode(__lowerCamelCase   ,		add_special_tokens=__lowerCamelCase    )
     __A       :		List[str]							=	rust_tokenizer.encode(__lowerCamelCase   ,		add_special_tokens=__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		List[Any]							=	self.get_rust_tokenizer()
     __A       :		Union[str, Any]							=	tokenizer.encode(__lowerCamelCase    )
     __A       :		List[Any]							=	rust_tokenizer.encode(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	AlbertTokenizer(__lowerCamelCase   ,		keep_accents=__lowerCamelCase    )

     __A       :		Optional[int]							=	tokenizer.tokenize('''This is a test'''    )
     self.assertListEqual(__lowerCamelCase   ,		['''▁this''', '''▁is''', '''▁a''', '''▁test''']    )

     self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase    )   ,		[48, 25, 21, 1289]    )

     __A       :		Tuple							=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
     self.assertListEqual(
         __lowerCamelCase   ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']    )
     __A       :		Optional[Any]							=	tokenizer.convert_tokens_to_ids(__lowerCamelCase    )
     self.assertListEqual(__lowerCamelCase   ,		[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

     __A       :		Optional[Any]							=	tokenizer.convert_ids_to_tokens(__lowerCamelCase    )
     self.assertListEqual(
         __lowerCamelCase   ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']   ,		)

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	AlbertTokenizer(__lowerCamelCase    )

     __A       :		List[Any]							=	tokenizer.encode('''sequence builders'''    )
     __A       :		str							=	tokenizer.encode('''multi-sequence build'''    )

     __A       :		Union[str, Any]							=	tokenizer.build_inputs_with_special_tokens(__lowerCamelCase    )
     __A       :		Optional[int]							=	tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   ,		__lowerCamelCase    )

     assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
     assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
         tokenizer.sep_token_id
     ]







 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	{'''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, 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], [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, 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]], '''input_ids''': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 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, 0, 0, 0, 0, 0]], '''token_type_ids''': [[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, 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, 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, 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=__lowerCamelCase   ,		model_name='''albert-base-v2'''   ,		revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'''   ,		)




