



"""simple docstring"""


from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

snake_case_								=			{
    """A""": ["""B""", """C"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E"""],
}

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					{start}
						UpperCAmelCase			 =					[start]
						UpperCAmelCase			 =					Queue()
						queue.put(lowercase_		)
						while not queue.empty():
												UpperCAmelCase			 =					queue.get()
												for w in graph[v]:
																		if w not in explored:
																								explored.add(lowercase_		)
																								result.append(lowercase_		)
																								queue.put(lowercase_		)
						return result

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					{start}
						UpperCAmelCase			 =					[start]
						UpperCAmelCase			 =					deque([start]		)
						while queue:
												UpperCAmelCase			 =					queue.popleft()
												for child in graph[v]:
																		if child not in visited:
																								visited.add(lowercase_		)
																								result.append(lowercase_		)
																								queue.append(lowercase_		)
						return result

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					F"""from __main__ import G, {name}"""
						UpperCAmelCase			 =					10000
						UpperCAmelCase			 =					timeit(F"""{name}(G, 'A')"""						,      setup=lowercase_						,      number=lowercase_		)
						print(F"""{name:<35} finished {number} runs in {res:.5f} seconds"""		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				benchmark_function("""breadth_first_search""")
				benchmark_function("""breadth_first_search_with_deque""")
				# breadth_first_search                finished 10000 runs in 0.20999 seconds
				# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds











'''simple docstring'''





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






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''EfficientNetImageProcessor'''
      __lowercase					=	('''BertTokenizer''', '''BertTokenizerFast''')






      def __init__(self			,	a__			,	a__       )							-> Tuple:
         super().__init__(a__			,	a__       )






      def __call__(self			,	a__=None			,	a__=None			,	a__="max_length"			,	a__=6_4			,	a__=None			,	**a__       )							-> Any:
         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:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.tokenizer(
                a__			,	padding=a__			,	max_length=a__			,	return_tensors=a__			,	**a__       )

         if images is not None:
            SCREAMING_SNAKE_CASE__      :					Any							    =			self.image_processor(a__			,	return_tensors=a__			,	**a__       )

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






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.tokenizer.model_input_names
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

