'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   ["image_processor", "tokenizer"]
   __lowerCAmelCase         =   "FlavaImageProcessor"
   __lowerCAmelCase         =   ("BertTokenizer", "BertTokenizerFast")
   def __init__(      self      :			int, UpperCamelCase__      :			str=None, UpperCamelCase__      :			Any=None, **UpperCamelCase__      :			List[Any]     )  ->			str:
        _A    		=  None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.', UpperCamelCase__, )
             _A    		=  kwargs.pop('feature_extractor'     )

        _A    		=  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__(UpperCamelCase__, UpperCamelCase__     )
        _A    		=  self.image_processor
   def __call__(      self      :			Union[str, Any], UpperCamelCase__      :			Optional[ImageInput] = None, UpperCamelCase__      :			Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None, UpperCamelCase__      :			bool = True, UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False, UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = False, UpperCamelCase__      :			Optional[int] = None, UpperCamelCase__      :			int = 0, UpperCamelCase__      :			Optional[int] = None, UpperCamelCase__      :			Optional[bool] = None, UpperCamelCase__      :			Optional[bool] = None, UpperCamelCase__      :			Optional[bool] = None, UpperCamelCase__      :			Optional[bool] = None, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = False, UpperCamelCase__      :			bool = True, UpperCamelCase__      :			Optional[Union[str, TensorType]] = None, **UpperCamelCase__      :			Dict, )  ->			List[str]:

        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:
             _A    		=  self.tokenizer(
                 text=UpperCamelCase__, add_special_tokens=UpperCamelCase__, padding=UpperCamelCase__, truncation=UpperCamelCase__, max_length=UpperCamelCase__, stride=UpperCamelCase__, pad_to_multiple_of=UpperCamelCase__, return_token_type_ids=UpperCamelCase__, return_attention_mask=UpperCamelCase__, return_overflowing_tokens=UpperCamelCase__, return_special_tokens_mask=UpperCamelCase__, return_offsets_mapping=UpperCamelCase__, return_length=UpperCamelCase__, verbose=UpperCamelCase__, return_tensors=UpperCamelCase__, **UpperCamelCase__, )
        if images is not None:
             _A    		=  self.image_processor(
                 UpperCamelCase__, return_image_mask=UpperCamelCase__, return_codebook_pixels=UpperCamelCase__, return_tensors=UpperCamelCase__, **UpperCamelCase__, )

        if text is not None and images is not None:
             encoding.update(UpperCamelCase__     )
             return encoding
        elif text is not None:
             return encoding
        else:
             return BatchEncoding(data=dict(**UpperCamelCase__     ), tensor_type=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], *UpperCamelCase__      :			int, **UpperCamelCase__      :			Tuple     )  ->			Union[str, Any]:
        return self.tokenizer.batch_decode(*UpperCamelCase__, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any, *UpperCamelCase__      :			Dict, **UpperCamelCase__      :			Tuple     )  ->			Optional[Any]:
        return self.tokenizer.decode(*UpperCamelCase__, **UpperCamelCase__     )
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  self.tokenizer.model_input_names
        _A    		=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[Any]:
        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.', UpperCamelCase__, )
        return self.image_processor_class

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Union[str, Any]:
        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.', UpperCamelCase__, )
        return self.image_processor





from __future__ import annotations

from collections import Counter
from random import random
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					{}

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					{}

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			float   ):






      """simple docstring"""



      if nodea not in self.connections:
           self.add_node(SCREAMING_SNAKE_CASE__   )
      if nodea not in self.connections:
           self.add_node(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					probability

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return list(self.connections   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					0
      UpperCamelCase						    =					random()

      for dest in self.connections[node]:
           current_probability += self.connections[node][dest]
           if current_probability > random_value:
                return dest
      return ""




def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      dict[str, int]:
     UpperCamelCase						    =					MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(_lowercase						,     _lowercase						,     _lowercase			)

     UpperCamelCase						    =					Counter(graph.get_nodes()			)
     UpperCamelCase						    =					start

     for _ in range(_lowercase			):
          UpperCamelCase						    =					graph.transition(_lowercase			)
          visited[node] += 1

     return visited


if __name__ == "__main__":
 import doctest

 doctest.testmod()


