





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							['''image_processor''', '''tokenizer''']
      a						       =							'''AutoImageProcessor'''
      a						       =							'''AutoTokenizer'''
      def __init__(		self      ,       a__=None      ,       a__=None      ,       **a__       ):
         A_     :				Dict			      =	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__      ,       )
            A_     :				Optional[int]			      =	kwargs.pop("""feature_extractor"""       )

         A_     :				List[str]			      =	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__       )
         A_     :				List[Any]			      =	self.image_processor
         A_     :				Optional[Any]			      =	False
      def __call__(		self      ,       *a__      ,       **a__       ):
         # For backward compatibility
         if self._in_target_context_manager:
            return self.current_processor(*a__      ,       **a__       )

         A_     :				List[Any]			      =	kwargs.pop("""images"""      ,       a__       )
         A_     :				List[Any]			      =	kwargs.pop("""text"""      ,       a__       )
         if len(a__       ) > 0:
            A_     :				Tuple			      =	args[0]
            A_     :				int			      =	args[1:]

         if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""       )

         if images is not None:
            A_     :				List[Any]			      =	self.image_processor(a__      ,       *a__      ,       **a__       )
         if text is not None:
            A_     :				Dict			      =	self.tokenizer(a__      ,       **a__       )

         if text is None:
            return inputs
         elif images is None:
            return encodings
         else:
            A_     :				int			      =	encodings["""input_ids"""]
            return inputs
      def 		_lowerCamelCase      (		self      ,       *a__      ,       **a__       ):
         return self.tokenizer.batch_decode(*a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       *a__      ,       **a__       ):
         return self.tokenizer.decode(*a__      ,       **a__       )
      @contextmanager
      def 		_lowerCamelCase      (		self       ):
         warnings.warn(
             """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
             """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
             """your images inputs, or in a separate call."""       )
         A_     :				str			      =	True
         A_     :				List[Any]			      =	self.tokenizer
         yield
         A_     :				Dict			      =	self.image_processor
         A_     :				Any			      =	False
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=False      ,       a__=None       ):
         if added_vocab is None:
            A_     :				str			      =	self.tokenizer.get_added_vocab()

         A_     :				Any			      =	{}

         while tokens:
            A_     :				List[str]			      =	re.search(R"""<s_(.*?)>"""      ,       a__      ,       re.IGNORECASE       )
            if start_token is None:
               break
            A_     :				Optional[Any]			      =	start_token.group(1       )
            A_     :				Optional[int]			      =	re.search(RF"""</s_{key}>"""      ,       a__      ,       re.IGNORECASE       )
            A_     :				Dict			      =	start_token.group()
            if end_token is None:
               A_     :				Tuple			      =	tokens.replace(a__      ,       """"""       )
            else:
               A_     :				Union[str, Any]			      =	end_token.group()
               A_     :				int			      =	re.escape(a__       )
               A_     :				Optional[Any]			      =	re.escape(a__       )
               A_     :				Union[str, Any]			      =	re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""      ,       a__      ,       re.IGNORECASE       )
               if content is not None:
                  A_     :				Optional[Any]			      =	content.group(1       ).strip()
                  if r"<s_" in content and r"</s_" in content:  # non-leaf node
                     A_     :				Tuple			      =	self.tokenajson(a__      ,       is_inner_value=a__      ,       added_vocab=a__       )
                     if value:
                        if len(a__       ) == 1:
                           A_     :				Tuple			      =	value[0]
                        A_     :				List[str]			      =	value
                  else:  # leaf nodes
                     A_     :				Optional[Any]			      =	[]
                     for leaf in content.split(R"""<sep/>"""       ):
                        A_     :				Optional[int]			      =	leaf.strip()
                        if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                           A_     :				str			      =	leaf[1:-2]  # for categorical special tokens
                        output[key].append(a__       )
                     if len(output[key]       ) == 1:
                        A_     :				str			      =	output[key][0]

               A_     :				str			      =	tokens[tokens.find(a__       ) + len(a__       ) :].strip()
               if tokens[:6] == r"<sep/>":  # non-leaf nodes
                  return [output] + self.tokenajson(tokens[6:]      ,       is_inner_value=a__      ,       added_vocab=a__       )

         if len(a__       ):
            return [output] if is_inner_value else output
         else:
            return [] if is_inner_value else {"text_sequence": tokens}
      @property
      def 		_lowerCamelCase      (		self       ):
         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 		_lowerCamelCase      (		self       ):
         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,       a__      ,       )
         return self.image_processor













def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	[0] * len(_lowerCAmelCase     )
   A_     :				Optional[int]			      =	[]
   A_     :				str			      =	[]
   A_     :				Dict			      =	0

   for values in graph.values():
      for i in values:
         indegree[i] += 1

   for i in range(len(_lowerCAmelCase     )     ):
      if indegree[i] == 0:
         queue.append(_lowerCAmelCase     )

   while queue:
      A_     :				List[str]			      =	queue.pop(0     )
      cnt += 1
      topo.append(_lowerCAmelCase     )
      for x in graph[vertex]:
         indegree[x] -= 1
         if indegree[x] == 0:
            queue.append(_lowerCAmelCase     )

   if cnt != len(_lowerCAmelCase     ):
      print("""Cycle exists"""     )
   else:
      print(_lowerCAmelCase     )


# Adjacency List of Graph
_lowerCAmelCase 				=     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)






