
"""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        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str  )		->  Any:







											"""simple docstring"""




											return None






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Optional[int]  )		->  List[str]:







											"""simple docstring"""




											return None






class        _SCREAMING_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	:			List[str]  )		->  Dict:







											"""simple docstring"""




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


				@require_torch
				@slow
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Any:







											"""simple docstring"""




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


				@require_torch
				@slow
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											from transformers import BertModel

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

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


				@require_tf
				@slow
				def 				__lowerCamelCase	(					self	:			int  )		->  Tuple:







											"""simple docstring"""




											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		_lowerCAmelCase  	=			self._test_export(UpperCAmelCase_				,     'tf'				,     12				,     **UpperCAmelCase_  )
																		_lowerCAmelCase  	=			quantize(Path(UpperCAmelCase_  )  )

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


				@require_torch
				@slow
				def 				__lowerCamelCase	(					self	:			int  )		->  Union[str, Any]:







											"""simple docstring"""




											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																		_lowerCAmelCase  	=			self._test_export(UpperCAmelCase_				,     'pt'				,     12				,     **UpperCAmelCase_  )
																		_lowerCAmelCase  	=			quantize(UpperCAmelCase_  )

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


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Dict=None				,     **UpperCAmelCase_	:			Any  )		->  str:







											"""simple docstring"""




											try:
																		# Compute path
																		with TemporaryDirectory() as tempdir:
																									_lowerCAmelCase  	=			Path(UpperCAmelCase_  ).joinpath('model.onnx'  )

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

																		# Export
																		convert(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     **UpperCAmelCase_  )

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


				@require_torch
				@require_tokenizers
				@slow
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Tuple:







											"""simple docstring"""




											from transformers import BertModel

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


				@require_tf
				@require_tokenizers
				@slow
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Optional[Any]:







											"""simple docstring"""




											from transformers import TFBertModel

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


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Union[str, Any]  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			FeatureExtractionPipeline(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
											_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			infer_shapes(UpperCAmelCase_				,     UpperCAmelCase_  )

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

											# 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	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			['input_ids', 'attention_mask', 'token_type_ids']
											_lowerCAmelCase  	=			{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
											_lowerCAmelCase	,		_lowerCAmelCase  	=			ensure_valid_input(FuncContiguousArgs()				,     UpperCAmelCase_				,     UpperCAmelCase_  )

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

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

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

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

											# Should have exactly the one arg (all before the one not provided "some_other_args")
											self.assertEqual(len(UpperCAmelCase_  )				,     1  )
											self.assertEqual(len(UpperCAmelCase_  )				,     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	:			List[Any]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			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"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[0 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# initialize interval's left pointer and right pointer
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							for i in range(1     ,  len(SCREAMING_SNAKE_CASE						)						):
														# case when current index is inside the interval
														if i <= right_pointer:
																					_lowerCAmelCase  	=			min(right_pointer - i + 1     ,  z_result[i - left_pointer]						)
																					_lowerCAmelCase  	=			min_edge

														while go_next(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					z_result[i] += 1

														# if new index's result gives us more right interval,
														# we've to update left_pointer and right_pointer
														if i + z_result[i] - 1 > right_pointer:
																					_lowerCAmelCase	,		_lowerCAmelCase  	=			i, i + z_result[i] - 1

							return z_result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							return i + z_result[i] < len(SCREAMING_SNAKE_CASE						) and s[z_result[i]] == s[i + z_result[i]]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0
							# concatenate 'pattern' and 'input_str' and call z_function
							# with concatenated string
							_lowerCAmelCase  	=			z_function(pattern + input_str						)

							for val in z_result:
														# if value is greater then length of the pattern string
														# that means this index is starting position of substring
														# which is equal to pattern string
														if val >= len(SCREAMING_SNAKE_CASE						):
																					answer += 1

							return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()






