






import warnings
from typing import List, Optional, Union

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


class 							__a							(  __A						):





						'''simple docstring'''



						UpperCAmelCase__      :				Any				    =						["""image_processor""", """tokenizer"""]
						UpperCAmelCase__      :				Dict				    =						"""ViltImageProcessor"""
						UpperCAmelCase__      :				Tuple				    =						("""BertTokenizer""", """BertTokenizerFast""")


						def __init__(						self ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    **UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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__ ,    )
														SCREAMING_SNAKE_CASE_   :	Tuple													=   kwargs.pop('feature_extractor'       )

										SCREAMING_SNAKE_CASE_   :	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__(UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.image_processor


						def __call__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = False ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = 0 ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False ,    UpperCamelCase__ = False ,    UpperCamelCase__ = False ,    UpperCamelCase__ = False ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   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__ ,    )
										# add pixel_values + pixel_mask
										SCREAMING_SNAKE_CASE_   :	str													=   self.image_processor(UpperCamelCase__ ,    return_tensors=UpperCamelCase__       )
										encoding.update(UpperCamelCase__       )

										return encoding


						def   __snake_case			(						self ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
										return self.tokenizer.batch_decode(*UpperCamelCase__ ,    **UpperCamelCase__       )


						def   __snake_case			(						self ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
										return self.tokenizer.decode(*UpperCamelCase__ ,    **UpperCamelCase__       )


						@property
						def   __snake_case			(						self       ):
										SCREAMING_SNAKE_CASE_   :	List[str]													=   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       )       )


						@property
						def   __snake_case			(						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   __snake_case			(						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       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

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






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

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

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()