





from __future__ import annotations





def lowerCAmelCase__(__snake_case							)				->      bool:







	'''simple docstring'''







	lowerCamelCase__								=		len(snake_case__							)
	# We need to create solution object to save path.
	lowerCamelCase__								=		[[0 for _ in range(snake_case__							)] for _ in range(snake_case__							)]
	lowerCamelCase__								=		run_maze(snake_case__       ,0       ,0       ,snake_case__							)
	if solved:
		print('''\n'''.join(str(snake_case__							) for row in solutions							)							)
	else:
		print('''No solution exists!'''							)
	return solved





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      bool:







	'''simple docstring'''







	lowerCamelCase__								=		len(snake_case__							)
	# Final check point.
	if i == j == (size - 1):
		lowerCamelCase__								=		1
		return True

	lowerCamelCase__								=		(not i < 0) and (not j < 0)  # Check lower bounds
	lowerCamelCase__								=		(i < size) and (j < size)  # Check upper bounds

	if lower_flag and upper_flag:
		# check for already visited and block points.
		lowerCamelCase__								=		(not solutions[i][j]) and (not maze[i][j])
		if block_flag:
			# check visited
			lowerCamelCase__								=		1

			# check for directions
			if (
			    run_maze(snake_case__       ,i + 1       ,snake_case__       ,snake_case__							)
			    or run_maze(snake_case__       ,snake_case__       ,j + 1       ,snake_case__							)
			    or run_maze(snake_case__       ,i - 1       ,snake_case__       ,snake_case__							)
			    or run_maze(snake_case__       ,snake_case__       ,j - 1       ,snake_case__							)
			):
				return True

			lowerCamelCase__								=		0
			return False
	return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()









"""simple docstring"""




from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["pixel_values"]





				def __init__(       self		, _a = True		, _a = None		, _a = PILImageResampling.BILINEAR		, _a = True		, _a = None		, _a = True		, _a = 1 / 255		, _a = True		, _a = None		, _a = None		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a )
							lowerCamelCase			      =					size if size is not None else {"""shortest_edge""": 256}
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_resize
							lowerCamelCase			      =					size
							lowerCamelCase			      =					resample
							lowerCamelCase			      =					do_center_crop
							lowerCamelCase			      =					crop_size
							lowerCamelCase			      =					do_rescale
							lowerCamelCase			      =					rescale_factor
							lowerCamelCase			      =					do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase			      =					image_std if image_std is not None else IMAGENET_STANDARD_STD





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = PILImageResampling.BICUBIC		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							if "shortest_edge" not in size:
										raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
							lowerCamelCase			      =					get_resize_output_image_size(_a		, size=size["""shortest_edge"""]		, default_to_square=_a )
							return resize(_a		, size=_a		, resample=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a )
							if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
							return center_crop(_a		, size=(size["""height"""], size["""width"""])		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a ):





							"""simple docstring"""
							return rescale(_a		, scale=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							return normalize(_a		, mean=_a		, std=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = ChannelDimension.FIRST		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					do_resize if do_resize is not None else self.do_resize
							lowerCamelCase			      =					size if size is not None else self.size
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					resample if resample is not None else self.resample
							lowerCamelCase			      =					do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase			      =					crop_size if crop_size is not None else self.crop_size
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase			      =					rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase			      =					do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase			      =					image_mean if image_mean is not None else self.image_mean
							lowerCamelCase			      =					image_std if image_std is not None else self.image_std

							lowerCamelCase			      =					make_list_of_images(_a )

							if not valid_images(_a ):
										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.
							lowerCamelCase			      =					[to_numpy_array(_a ) for image in images]

							if do_resize:
										lowerCamelCase			      =					[self.resize(image=_a		, size=_a		, resample=_a ) for image in images]

							if do_center_crop:
										lowerCamelCase			      =					[self.center_crop(image=_a		, size=_a ) for image in images]

							if do_rescale:
										lowerCamelCase			      =					[self.rescale(image=_a		, scale=_a ) for image in images]

							if do_normalize:
										lowerCamelCase			      =					[self.normalize(image=_a		, mean=_a		, std=_a ) for image in images]

							lowerCamelCase			      =					[to_channel_dimension_format(_a		, _a ) for image in images]

							lowerCamelCase			      =					{"""pixel_values""": images}
							return BatchFeature(data=_a		, tensor_type=_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
										if len(_a ) != len(_a ):
													raise ValueError(
													    """Make sure that you pass in as many target sizes as the batch dimension of the logits""" )

										if is_torch_tensor(_a ):
													lowerCamelCase			      =					target_sizes.numpy()

										lowerCamelCase			      =					[]

										for idx in range(len(_a ) ):
													lowerCamelCase			      =					torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0 )		, size=target_sizes[idx]		, mode="""bilinear"""		, align_corners=_a )
													lowerCamelCase			      =					resized_logits[0].argmax(dim=0 )
													semantic_segmentation.append(_a )
							else:
										lowerCamelCase			      =					logits.argmax(dim=1 )
										lowerCamelCase			      =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

							return semantic_segmentation



