from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE		:     Tuple         =	logging.get_logger(__name__)

class        UpperCamelCase						(				__a				):
						a__		:Tuple      =			['''pixel_values''']





						def __init__(self			,     __UpperCamelCase = True			,     __UpperCamelCase = None			,     __UpperCamelCase = PILImageResampling.BICUBIC			,     __UpperCamelCase = True			,     __UpperCamelCase = None			,     __UpperCamelCase = True			,     __UpperCamelCase = 1 / 255			,     __UpperCamelCase = True			,     __UpperCamelCase = IMAGENET_DEFAULT_MEAN			,     __UpperCamelCase = IMAGENET_DEFAULT_STD			,     **__UpperCamelCase			,     )				->				None:
												super().__init__(**__UpperCamelCase						)
												UpperCamelCase_		:							Optional[Any]  =		size if size is not None else {"""shortest_edge""": 224}
												UpperCamelCase_		:							List[str]  =		get_size_dict(__UpperCamelCase			,     default_to_square=__UpperCamelCase						)
												UpperCamelCase_		:							int  =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
												UpperCamelCase_		:							Optional[Any]  =		get_size_dict(__UpperCamelCase			,     param_name="""crop_size"""						)

												UpperCamelCase_		:							Tuple  =		do_resize
												UpperCamelCase_		:							Tuple  =		size
												UpperCamelCase_		:							List[str]  =		resample
												UpperCamelCase_		:							Optional[int]  =		do_center_crop
												UpperCamelCase_		:							Union[str, Any]  =		crop_size
												UpperCamelCase_		:							Tuple  =		do_rescale
												UpperCamelCase_		:							Tuple  =		rescale_factor
												UpperCamelCase_		:							Optional[int]  =		do_normalize
												UpperCamelCase_		:							Dict  =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												UpperCamelCase_		:							Tuple  =		image_std if image_std is not None else IMAGENET_DEFAULT_STD





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = PILImageResampling.BICUBIC			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				np.ndarray:
												UpperCamelCase_		:							List[str]  =		get_size_dict(__UpperCamelCase			,     default_to_square=__UpperCamelCase						)
												# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
												if "shortest_edge" in size:
																		UpperCamelCase_		:							Optional[Any]  =		int((256 / 224) * size["""shortest_edge"""]						)
																		UpperCamelCase_		:							str  =		get_resize_output_image_size(__UpperCamelCase			,     size=__UpperCamelCase			,     default_to_square=__UpperCamelCase						)
																		UpperCamelCase_		:							Optional[Any]  =		{"""height""": output_size[0], """width""": output_size[1]}
												if "height" not in size_dict or "width" not in size_dict:
																		raise ValueError(
																		    f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''						)
												return resize(
												    __UpperCamelCase			,     size=(size_dict["""height"""], size_dict["""width"""])			,     resample=__UpperCamelCase			,     data_format=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				np.ndarray:
												UpperCamelCase_		:							Any  =		get_size_dict(__UpperCamelCase						)
												if "height" not in size or "width" not in size:
																		raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
												return center_crop(__UpperCamelCase			,     size=(size["""height"""], size["""width"""])			,     data_format=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				np.ndarray:
												return rescale(__UpperCamelCase			,     scale=__UpperCamelCase			,     data_format=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				np.ndarray:
												return normalize(__UpperCamelCase			,     mean=__UpperCamelCase			,     std=__UpperCamelCase			,     data_format=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = ChannelDimension.FIRST			,     **__UpperCamelCase			,     )				->				BatchFeature:
												UpperCamelCase_		:							Optional[Any]  =		do_resize if do_resize is not None else self.do_resize
												UpperCamelCase_		:							Tuple  =		resample if resample is not None else self.resample
												UpperCamelCase_		:							Dict  =		do_center_crop if do_center_crop is not None else self.do_center_crop
												UpperCamelCase_		:							List[Any]  =		do_rescale if do_rescale is not None else self.do_rescale
												UpperCamelCase_		:							Tuple  =		rescale_factor if rescale_factor is not None else self.rescale_factor
												UpperCamelCase_		:							Optional[int]  =		do_normalize if do_normalize is not None else self.do_normalize
												UpperCamelCase_		:							Optional[int]  =		image_mean if image_mean is not None else self.image_mean
												UpperCamelCase_		:							Optional[Any]  =		image_std if image_std is not None else self.image_std

												UpperCamelCase_		:							List[str]  =		size if size is not None else self.size
												UpperCamelCase_		:							Any  =		get_size_dict(__UpperCamelCase			,     default_to_square=__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		crop_size if crop_size is not None else self.crop_size
												UpperCamelCase_		:							int  =		get_size_dict(__UpperCamelCase			,     param_name="""crop_size"""						)

												UpperCamelCase_		:							Optional[Any]  =		make_list_of_images(__UpperCamelCase						)

												if not valid_images(__UpperCamelCase						):
																		raise ValueError(
																		    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																		    """torch.Tensor, tf.Tensor or jax.ndarray."""						)

												if do_resize and size is None:
																		raise ValueError("""Size must be specified if do_resize is True."""						)

												if do_center_crop and crop_size is None:
																		raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

												if do_rescale and rescale_factor is None:
																		raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

												# All transformations expect numpy arrays.
												UpperCamelCase_		:							Tuple  =		[to_numpy_array(__UpperCamelCase						) for image in images]

												if do_resize:
																		UpperCamelCase_		:							int  =		[self.resize(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						) for image in images]

												if do_center_crop:
																		UpperCamelCase_		:							Optional[int]  =		[self.center_crop(__UpperCamelCase			,     __UpperCamelCase						) for image in images]

												if do_rescale:
																		UpperCamelCase_		:							Tuple  =		[self.rescale(__UpperCamelCase			,     __UpperCamelCase						) for image in images]

												if do_normalize:
																		UpperCamelCase_		:							Tuple  =		[self.normalize(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						) for image in images]

												UpperCamelCase_		:							str  =		[to_channel_dimension_format(__UpperCamelCase			,     __UpperCamelCase						) for image in images]

												UpperCamelCase_		:							Optional[Any]  =		{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase			,     tensor_type=__UpperCamelCase						)







from heapq import heappop, heappush

import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.ndarray		, _SCREAMING_SNAKE_CASE     :				tuple[int, int]		, _SCREAMING_SNAKE_CASE     :				tuple[int, int]		, _SCREAMING_SNAKE_CASE     :				bool		, ):
						UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		grid.shape
						UpperCamelCase_		:							List[str]  =		[-1, 1, 0, 0]
						UpperCamelCase_		:							Dict  =		[0, 0, -1, 1]
						if allow_diagonal:
												dx += [-1, -1, 1, 1]
												dy += [-1, 1, -1, 1]

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

						while queue:
												((UpperCamelCase_),(UpperCamelCase_))		:							Any  =		heappop(_SCREAMING_SNAKE_CASE			)
												if (x, y) in visited:
																		continue
												visited.add((x, y)			)

												if (x, y) == destination:
																		UpperCamelCase_		:							Tuple  =		[]
																		while (x, y) != source:
																								path.append((x, y)			)
																								UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		predecessors[x, y]
																		path.append(_SCREAMING_SNAKE_CASE			)  # add the source manually
																		path.reverse()
																		return matrix[destination], path

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

						return np.inf, []


if __name__ == "__main__":
	import doctest

	doctest.testmod()






