

'''simple docstring'''





from typing import Dict, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


A_	= logging.get_logger(__name__)


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					['pixel_values']
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = 1 / 255   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     None:






							'''simple docstring'''



							super().__init__(**SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      size if size is not None else {'shortest_edge': 384}
							lowerCamelCase_  				=      get_size_dict(SCREAMING_SNAKE_CASE_   ,			default_to_square=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      do_resize
							lowerCamelCase_  				=      size
							# Default value set here for backwards compatibility where the value in config is None
							lowerCamelCase_  				=      crop_pct if crop_pct is not None else 224 / 256
							lowerCamelCase_  				=      resample
							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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC   ,			SCREAMING_SNAKE_CASE_ = None   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     np.ndarray:






							'''simple docstring'''



							lowerCamelCase_  				=      get_size_dict(SCREAMING_SNAKE_CASE_   ,			default_to_square=SCREAMING_SNAKE_CASE_		)
							if "shortest_edge" not in size:
												raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''		)
							lowerCamelCase_  				=      size['shortest_edge']

							if shortest_edge < 384:
												# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
												lowerCamelCase_  				=      int(shortest_edge / crop_pct		)
												lowerCamelCase_  				=      get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,			size=SCREAMING_SNAKE_CASE_   ,			default_to_square=SCREAMING_SNAKE_CASE_		)
												lowerCamelCase_  				=      resize(image=SCREAMING_SNAKE_CASE_   ,			size=SCREAMING_SNAKE_CASE_   ,			resample=SCREAMING_SNAKE_CASE_   ,			data_format=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
												# then crop to (shortest_edge, shortest_edge)
												return center_crop(image=SCREAMING_SNAKE_CASE_   ,			size=(shortest_edge, shortest_edge)   ,			data_format=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
							else:
												# warping (no cropping) when evaluated at 384 or larger
												return resize(
												    SCREAMING_SNAKE_CASE_   ,			size=(shortest_edge, shortest_edge)   ,			resample=SCREAMING_SNAKE_CASE_   ,			data_format=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Dict:






							'''simple docstring'''



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






							'''simple docstring'''



							return normalize(SCREAMING_SNAKE_CASE_   ,			mean=SCREAMING_SNAKE_CASE_   ,			std=SCREAMING_SNAKE_CASE_   ,			data_format=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     PIL.Image.Image:






							'''simple docstring'''



							lowerCamelCase_  				=      do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_  				=      crop_pct if crop_pct is not None else self.crop_pct
							lowerCamelCase_  				=      resample if resample is not None else self.resample
							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_  				=      size if size is not None else self.size
							lowerCamelCase_  				=      get_size_dict(SCREAMING_SNAKE_CASE_   ,			default_to_square=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      make_list_of_images(SCREAMING_SNAKE_CASE_		)

							if not valid_images(SCREAMING_SNAKE_CASE_		):
												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 or resample is None:
												raise ValueError('Size and resample must be specified if do_resize is True.'		)

							if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
												raise ValueError('crop_pct must be specified if size < 384.'		)

							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(SCREAMING_SNAKE_CASE_		) for image in images]

							if do_resize:
												lowerCamelCase_  				=      [self.resize(image=SCREAMING_SNAKE_CASE_   ,			size=SCREAMING_SNAKE_CASE_   ,			crop_pct=SCREAMING_SNAKE_CASE_   ,			resample=SCREAMING_SNAKE_CASE_		) for image in images]

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

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

							lowerCamelCase_  				=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		) for image in images]

							lowerCamelCase_  				=      {'pixel_values': images}
							return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,			tensor_type=SCREAMING_SNAKE_CASE_		)











from __future__ import annotations





def    __lowerCAmelCase   (    __snake_case							):
     __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							):
     __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()






