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




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





 '''simple docstring'''



 _UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
 _UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
 _UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



 def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


    '''simple docstring'''


    super().__init__(lowercase    ,						lowercase			)



 def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


    '''simple docstring'''


    if text is None and images is None:
       raise ValueError('You have to specify either text or images. Both cannot be none.'			)

    if text is not None:
       _snake_case						=    self.tokenizer(
           lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

    if images is not None:
       _snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

    if text is not None and images is not None:
       _snake_case						=    image_features.pixel_values
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



 def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


    '''simple docstring'''


    return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



 def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


    '''simple docstring'''


    return self.tokenizer.decode(*lowercase    ,						**lowercase			)





 @property
 def 				A							(						self			:					List[str]			):


    '''simple docstring'''


    _snake_case						=    self.tokenizer.model_input_names
    _snake_case						=    self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

_lowerCamelCase :       Union[str, Any]     					=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_lowerCamelCase :       Optional[Any]     					=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase :       Dict     					=    [0 for x in range(len(graph))]
_lowerCamelCase :       Dict     					=    []






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Dict				)				->						Optional[int]:
			_snake_case						=    1
			while stack:
						_snake_case						=    stack.pop()
						print(__lowercase ,      clothes[current_clothing]				)
						order += 1






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			Dict ,      __lowercase     :			Union[str, Any]				)				->						List[Any]:
			_snake_case						=    1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)

			stack.append(__lowercase				)






def  a_	( __lowercase     :			Dict ,      __lowercase     :			int				)				->						List[str]:
			for v in range(len(__lowercase				)				):
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)