






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      __A(    __SCREAMING_SNAKE_CASE			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__         =      "FlavaImageProcessor"
  SCREAMING_SNAKE_CASE__         =      ("BertTokenizer", "BertTokenizerFast")







  def __init__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       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."""	,      __UpperCAmelCase	,      )
            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__(__UpperCAmelCase	,      __UpperCAmelCase						)
       UpperCamelCase__				=    self.image_processor







  def __call__(self	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = 0	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = None	,      **SCREAMING_SNAKE_CASE_	,      ):

       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:
            UpperCamelCase__				=    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:
            UpperCamelCase__				=    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	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.batch_decode(*__UpperCAmelCase	,      **__UpperCAmelCase						)







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.decode(*__UpperCAmelCase	,      **__UpperCAmelCase						)







  @property
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.tokenizer.model_input_names
       UpperCamelCase__				=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







  @property
  def 	UpperCAmelCase_ (self						):
       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						):
       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      __A:

  """simple docstring"""







  def __init__(self						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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 	UpperCAmelCase_ (self						):
       return list(self.connections						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       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 						__magic_name__			(   __a      :	str	,		__a      :	list[tuple[str, str, float]]	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    MarkovChainGraphUndirectedUnweighted()

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

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

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

     return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()





