
"""simple docstring"""





import argparse
from collections import defaultdict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    f"""{file}_{class_name}_{test_name}"""
   done_test[_id] += 1

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					List[Any]			     =    f.readlines()

   UpperCAmelCase_					:					int			     =    f"""class {class_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{4 * " "}def {test_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{8 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					Tuple			     =    f"""{16 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					int			     =    False
   UpperCAmelCase_					:					Union[str, Any]			     =    False
   UpperCAmelCase_					:					str			     =    False
   UpperCAmelCase_					:					Optional[Any]			     =    False
   UpperCAmelCase_					:					List[str]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    0

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

         if count == done_test[_id]:
            UpperCAmelCase_					:					Union[str, Any]			     =    True

      if in_class and in_func and in_line:
         if ")" not in line:
            continue
         else:
            UpperCAmelCase_					:					Any			     =    True

      if in_class and in_func and in_line and insert_line:
         new_lines.append(f"""{spaces * " "}{correct_line}"""  )
         UpperCAmelCase_					:					int			     =    False
      else:
         new_lines.append(__lowerCamelCase  )

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







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=None  ):
   if fail is not None:
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					Tuple			     =    {l.strip() for l in f.readlines()}
   else:
      UpperCAmelCase_					:					str			     =    None

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   UpperCAmelCase_					:					Any			     =    defaultdict(__lowerCamelCase  )
   for line in correct_lines:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					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		       =							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		       =							parser.parse_args()

    main(args.correct_filename, args.fail_filename)









from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


