






'''simple docstring'''







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	(				_lowercase		):
							a				      =       ["""image_processor""", """tokenizer"""]
							a				      =       """Pix2StructImageProcessor"""
							a				      =       ("""T5Tokenizer""", """T5TokenizerFast""")




							def __init__(							self:				Optional[int]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       False
														super().__init__(UpperCamelCase__			,							UpperCamelCase__     )




							def __call__(							self:				Union[str, Any]			,							UpperCamelCase__:				Union[str, Any]=None			,							UpperCamelCase__:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,							UpperCamelCase__:				bool = True			,							UpperCamelCase__:				Union[bool, str, PaddingStrategy] = False			,							UpperCamelCase__:				Union[bool, str, TruncationStrategy] = None			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				Optional[int] = 2_048			,							UpperCamelCase__:				int = 0			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				Optional[bool] = None			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = True			,							UpperCamelCase__:				Optional[Union[str, TensorType]] = None			,							**UpperCamelCase__:				Optional[Any]			,							):
														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:
																					lowerCamelCase__					:	Dict         =       self.tokenizer
																					lowerCamelCase__					:	Any         =       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
																					lowerCamelCase__					:	List[Any]         =       self.image_processor(
																					    UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							max_patches=UpperCamelCase__			,							**UpperCamelCase__     )
														else:
																					# add pixel_values and bbox
																					lowerCamelCase__					:	List[str]         =       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:
																					lowerCamelCase__					:	Optional[Any]         =       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:
																												lowerCamelCase__					:	Dict         =       text_encoding.pop("""attention_mask"""     )
																					if "input_ids" in text_encoding:
																												lowerCamelCase__					:	int         =       text_encoding.pop("""input_ids"""     )
														else:
																					lowerCamelCase__					:	Optional[int]         =       None

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

														return encoding_image_processor




							def 		lowerCamelCase_				(							self:				str			,							*UpperCamelCase__:				Optional[Any]			,							**UpperCamelCase__:				List[str]     ):
														return self.tokenizer.batch_decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							*UpperCamelCase__:				Any			,							**UpperCamelCase__:				Optional[int]     ):
														return self.tokenizer.decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							@property
							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.tokenizer.model_input_names
														lowerCamelCase__					:	List[Any]         =       self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )









'''simple docstring'''







from heapq import heappop, heappush

import numpy as np





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			)      ->       tuple[float | int, list[tuple[int, int]]]:
							lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       grid.shape
							lowerCamelCase__					:	List[str]         =       [-1, 1, 0, 0]
							lowerCamelCase__					:	Dict         =       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       [(0, source)], set()
							lowerCamelCase__					:	Tuple         =       np.full((rows, cols)				,			np.inf      )
							lowerCamelCase__					:	List[str]         =       0
							lowerCamelCase__					:	Optional[int]         =       np.empty((rows, cols)				,			dtype=UpperCamelCase      )
							lowerCamelCase__					:	str         =       None

							while queue:
														((lowerCamelCase__)  ,		(lowerCamelCase__))					:	List[str]         =       heappop(UpperCamelCase      )
														if (x, y) in visited:
																					continue
														visited.add((x, y)      )

														if (x, y) == destination:
																					lowerCamelCase__					:	Optional[int]         =       []
																					while (x, y) != source:
																												path.append((x, y)      )
																												lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       predecessors[x, y]
																					path.append(UpperCamelCase      )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(UpperCamelCase      )      ):
																					lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												lowerCamelCase__					:	Any         =       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(UpperCamelCase				,			(dist + 1, (nx, ny))      )
																																			lowerCamelCase__					:	Union[str, Any]         =       dist + 1
																																			lowerCamelCase__					:	List[str]         =       (x, y)

							return np.inf, []


if __name__ == "__main__":
			import doctest

			doctest.testmod()

