






"""simple docstring"""
import argparse
from collections import defaultdict

def 				snake_case      (   A__	,A__	,A__	,A__	,A__						):
			UpperCAmelCase_     : List[Any]			     =					F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

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

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

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

									if count == done_test[_id]:
												UpperCAmelCase_     : Dict			     =					True

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

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""						)
									UpperCAmelCase_     : Tuple			     =					False
						else:
									new_lines.append(A__						)

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

def 				snake_case      (   A__	,A__=None						):



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

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

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


if __name__ == "__main__":
			lowerCamelCase_					=      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)
			lowerCamelCase_					=      parser.parse_args()

			main(args.correct_filename, args.fail_filename)










import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.transformer(input_ids=lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					int										=(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
        return self.LinearTransformation(lowerCAmelCase__							), embs






