

import argparse
from collections import defaultdict



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Tuple						,__a :     Tuple						,__a :     Dict						,__a :     Tuple      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						F"""{file}_{class_name}_{test_name}"""
					done_test[_id] += 1

					with open(__a						,'''r'''      ) as f:
										_a      :       Dict   =						f.readlines()

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

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

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

										if in_class and in_func and in_line:
															if ")" not in line:
																				continue
															else:
																				_a      :       Optional[Any]   =						True

										if in_class and in_func and in_line and insert_line:
															new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
															_a      :       Optional[Any]   =						False
										else:
															new_lines.append(__a      )

					with open(__a						,'''w'''      ) as f:
										for line in new_lines:
															f.write(__a      )



def   __UpperCAmelCase					(      __a :     Dict						,__a :     Tuple=None      )     ->	Union[str, Any]:

					"""simple docstring"""

					if fail is not None:
										with open(__a						,'''r'''      ) as f:
															_a      :       Optional[int]   =						{l.strip() for l in f.readlines()}
					else:
										_a      :       List[Any]   =						None

					with open(__a						,'''r'''      ) as f:
										_a      :       List[Any]   =						f.readlines()

					_a      :       List[Any]   =						defaultdict(__a      )
					for line in correct_lines:
										_a ,   _a ,   _a ,   _a      :       Dict   =						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__":
			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)





"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'ViTImageProcessor'
		lowercase__         = ('CLIPTokenizer', 'CLIPTokenizerFast')



		def __init__(   self							,						__a=None							,						__a=None							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,						__a							,						)
												_UpperCamelCase									=    kwargs.pop('''feature_extractor''')

							_UpperCamelCase									=    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.''')
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.''')

							super().__init__(__a							,						__a)



		def __call__(   self							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						**__a)			->			Tuple:






							'''simple docstring'''
							if text is None and visual_prompt is None and images is None:
												raise ValueError('''You have to specify either text, visual prompt or images.''')

							if text is not None and visual_prompt is not None:
												raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')

							if text is not None:
												_UpperCamelCase									=    self.tokenizer(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if images is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None and images is not None:
												_UpperCamelCase									=    {
												    '''pixel_values''': image_features.pixel_values,
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							elif text is not None and images is not None:
												_UpperCamelCase									=    image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							elif visual_prompt is not None:
												_UpperCamelCase									=    {
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							else:
												return BatchEncoding(data=dict(**__a)							,						tensor_type=__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,						__a							,						)
							return self.image_processor_class




		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,						__a							,						)
							return self.image_processor






