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   lowercase      (       _a       ):
							lowercase__  :    str									=							["""image_processor""", """tokenizer"""]
							lowercase__  :    Any									=							"""Pix2StructImageProcessor"""
							lowercase__  :    List[Any]									=							("""T5Tokenizer""", """T5TokenizerFast""")




							def __init__(  self				:   Any		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Dict )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    False
												super().__init__(_UpperCamelCase		,							_UpperCamelCase )




							def __call__(  self				:   Any		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[Any] = None		,							_UpperCamelCase				:   Any = True		,							_UpperCamelCase				:   List[str] = False		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   Optional[Any] = None		,							_UpperCamelCase				:   Any = 2_048		,							_UpperCamelCase				:   str = 0		,							_UpperCamelCase				:   str = None		,							_UpperCamelCase				:   int = None		,							_UpperCamelCase				:   List[str] = False		,							_UpperCamelCase				:   Tuple = False		,							_UpperCamelCase				:   str = False		,							_UpperCamelCase				:   Optional[Any] = False		,							_UpperCamelCase				:   str = False		,							_UpperCamelCase				:   List[str] = True		,							_UpperCamelCase				:   Tuple = None		,							**_UpperCamelCase				:   List[str]		,							)   ->			BatchEncoding:



												'''simple docstring'''

												if images is None and text is None:
																	raise ValueError("You have to specify either images or text." )

												# Get only text
												if images is None and not self.image_processor.is_vqa:
																	SCREAMING_SNAKE_CASE           =    self.tokenizer
																	SCREAMING_SNAKE_CASE           =    self.tokenizer(
																	    text=_UpperCamelCase		,							add_special_tokens=_UpperCamelCase		,							padding=_UpperCamelCase		,							truncation=_UpperCamelCase		,							max_length=_UpperCamelCase		,							stride=_UpperCamelCase		,							pad_to_multiple_of=_UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							return_overflowing_tokens=_UpperCamelCase		,							return_special_tokens_mask=_UpperCamelCase		,							return_offsets_mapping=_UpperCamelCase		,							return_token_type_ids=_UpperCamelCase		,							return_length=_UpperCamelCase		,							verbose=_UpperCamelCase		,							return_tensors=_UpperCamelCase		,							**_UpperCamelCase		,							)
																	return text_encoding

												if not self.image_processor.is_vqa:
																	# add pixel_values
																	SCREAMING_SNAKE_CASE           =    self.image_processor(
																	    _UpperCamelCase		,							return_tensors=_UpperCamelCase		,							max_patches=_UpperCamelCase		,							**_UpperCamelCase )
												else:
																	# add pixel_values and bbox
																	SCREAMING_SNAKE_CASE           =    self.image_processor(
																	    _UpperCamelCase		,							return_tensors=_UpperCamelCase		,							max_patches=_UpperCamelCase		,							header_text=_UpperCamelCase		,							**_UpperCamelCase )

												if text is not None and not self.image_processor.is_vqa:
																	SCREAMING_SNAKE_CASE           =    self.tokenizer(
																	    text=_UpperCamelCase		,							add_special_tokens=_UpperCamelCase		,							padding=_UpperCamelCase		,							truncation=_UpperCamelCase		,							max_length=_UpperCamelCase		,							stride=_UpperCamelCase		,							pad_to_multiple_of=_UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							return_overflowing_tokens=_UpperCamelCase		,							return_special_tokens_mask=_UpperCamelCase		,							return_offsets_mapping=_UpperCamelCase		,							return_token_type_ids=_UpperCamelCase		,							return_length=_UpperCamelCase		,							verbose=_UpperCamelCase		,							return_tensors=_UpperCamelCase		,							**_UpperCamelCase		,							)

																	if "attention_mask" in text_encoding:
																						SCREAMING_SNAKE_CASE           =    text_encoding.pop("attention_mask" )
																	if "input_ids" in text_encoding:
																						SCREAMING_SNAKE_CASE           =    text_encoding.pop("input_ids" )
												else:
																	SCREAMING_SNAKE_CASE           =    None

												if text_encoding is not None:
																	encoding_image_processor.update(_UpperCamelCase )

												return encoding_image_processor




							def 		__snake_case(  self				:   Union[str, Any]		,							*_UpperCamelCase				:   List[Any]		,							**_UpperCamelCase				:   Tuple )   ->			Dict:



												'''simple docstring'''

												return self.tokenizer.batch_decode(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							*_UpperCamelCase				:   Optional[Any]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Tuple:



												'''simple docstring'''

												return self.tokenizer.decode(*_UpperCamelCase		,							**_UpperCamelCase )





							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.tokenizer.model_input_names
												SCREAMING_SNAKE_CASE           =    self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowerCamelCase   :				Union[str, Any]     			=						TypeVar('''T''')


class   lowercase      (       Generic[T]       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {}  # dictionary of lists
												SCREAMING_SNAKE_CASE           =    directed




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   T		,							_UpperCamelCase				:   T )   ->			GraphAdjacencyList[T]:



												'''simple docstring'''

												if not self.directed:  # For undirected graphs
																	# if both source vertex and destination vertex are both present in the
																	# adjacency list, add destination vertex to source vertex list of adjacent
																	# vertices and add source vertex to destination vertex list of adjacent
																	# vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination vertex
																	# to source vertex list of adjacent vertices, then create a new vertex with
																	# destination vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
																	# if only destination vertex is present in adjacency list, add source vertex
																	# to destination vertex list of adjacent vertices, then create a new vertex
																	# with source vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif destination_vertex in self.adj_list:
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and assign a list
																	# containing the destination vertex as it's first adjacent vertex also
																	# create a new vertex with destination vertex as key and assign a list
																	# containing the source vertex as it's first adjacent vertex.
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
												else:  # For directed graphs
																	# if both source vertex and destination vertex are present in adjacency
																	# list, add destination vertex to source vertex list of adjacent vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination
																	# vertex to source vertex list of adjacent vertices and create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    []
																	# if only destination vertex is present in adjacency list, create a new
																	# vertex with source vertex as key and assign a list containing destination
																	# vertex as first adjacent vertex
																	elif destination_vertex in self.adj_list:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and a list containing
																	# destination vertex as it's first adjacent vertex. Then create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    []

												return self





							def __repr__(  self				:   Union[str, Any] )   ->			str:



												'''simple docstring'''

												return pformat(self.adj_list )




