


'''simple docstring'''






import argparse
from collections import defaultdict





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :Optional[int] ):







		'''simple docstring'''
		snake_case_		:							Any			       =						F'''{file}_{class_name}_{test_name}'''
		done_test[_id] += 1

		with open(lowerCamelCase_				,      """r""" ) as f:
				snake_case_		:							Optional[Any]			       =						f.readlines()

		snake_case_		:							int			       =						F'''class {class_name}('''
		snake_case_		:							Dict			       =						F'''{4 * " "}def {test_name}('''
		snake_case_		:							Dict			       =						F'''{8 * " "}{correct_line.split()[0]}'''
		snake_case_		:							Optional[int]			       =						F'''{16 * " "}{correct_line.split()[0]}'''
		snake_case_		:							int			       =						False
		snake_case_		:							Optional[Any]			       =						False
		snake_case_		:							Dict			       =						False
		snake_case_		:							List[str]			       =						False
		snake_case_		:							Optional[Any]			       =						0
		snake_case_		:							Dict			       =						0

		snake_case_		:							Any			       =						[]
		for line in lines:
				if line.startswith(lowerCamelCase_ ):
						snake_case_		:							str			       =						True
				elif in_class and line.startswith(lowerCamelCase_ ):
						snake_case_		:							str			       =						True
				elif in_class and in_func and (line.startswith(lowerCamelCase_ ) or line.startswith(lowerCamelCase_ )):
						snake_case_		:							Tuple			       =						len(line.split(correct_line.split()[0] )[0] )
						count += 1

						if count == done_test[_id]:
								snake_case_		:							Optional[int]			       =						True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								snake_case_		:							Union[str, Any]			       =						True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F'''{spaces * " "}{correct_line}''' )
						snake_case_		:							Any			       =						False
				else:
						new_lines.append(lowerCamelCase_ )

		with open(lowerCamelCase_				,      """w""" ) as f:
				for line in new_lines:
						f.write(lowerCamelCase_ )





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :str=None ):







		'''simple docstring'''
		if fail is not None:
				with open(lowerCamelCase_				,      """r""" ) as f:
						snake_case_		:							Tuple			       =						{l.strip() for l in f.readlines()}
		else:
				snake_case_		:							List[Any]			       =						None

		with open(lowerCamelCase_				,      """r""" ) as f:
				snake_case_		:							int			       =						f.readlines()

		snake_case_		:							int			       =						defaultdict(lowerCamelCase_ )
		for line in correct_lines:
				snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Any			       =						line.split(""";""" )
				if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
						overwrite_file(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )


if __name__ == "__main__":
							__A	:	Optional[Any]     =     argparse.ArgumentParser()
							parser.add_argument('--correct_filename', help='filename of tests with expected result')
							parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
							__A	:	int     =     parser.parse_args()

							main(args.correct_filename, args.fail_filename)



'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)