'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

lowercase__  :      Dict        =					logging.get_logger(__name__)



def  __lowerCamelCase       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


      if isinstance(_UpperCamelCase							,    (list, tuple)					) and isinstance(videos[0]							,    (list, tuple)					) and is_valid_image(videos[0][0]					):
            return videos

      elif isinstance(_UpperCamelCase							,    (list, tuple)					) and is_valid_image(videos[0]					):
            return [videos]

      elif is_valid_image(_UpperCamelCase					):
            return [[videos]]

      raise ValueError(F"""Could not make batched video from {videos}"""					)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	['''pixel_values''']






  def __init__(      self			:       Optional[Any]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BILINEAR  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							**UpperCAmelCase__			:       Any  ,							)				->None:
        super().__init__(**UpperCAmelCase__  )
        UpperCAmelCase_            =  size if size is not None else {'''shortest_edge''': 256}
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  ,							default_to_square=UpperCAmelCase__  )
        UpperCAmelCase_            =  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  ,							param_name='''crop_size'''  )

        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size
        UpperCAmelCase_            =  do_center_crop
        UpperCAmelCase_            =  crop_size
        UpperCAmelCase_            =  resample
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  offset
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase_            =  image_std if image_std is not None else IMAGENET_STANDARD_STD






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Dict[str, int]  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BILINEAR  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       List[str]  ,							)				->np.ndarray:
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  ,							default_to_square=UpperCAmelCase__  )
        if "shortest_edge" in size:
              UpperCAmelCase_            =  get_resize_output_image_size(UpperCAmelCase__  ,							size['''shortest_edge''']  ,							default_to_square=UpperCAmelCase__  )
        elif "height" in size and "width" in size:
              UpperCAmelCase_            =  (size['''height'''], size['''width'''])
        else:
              raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""  )
        return resize(UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Dict[str, int]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       Dict  ,							)				->np.ndarray:
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        if "height" not in size or "width" not in size:
              raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""  )
        return center_crop(UpperCAmelCase__  ,							size=(size['''height'''], size['''width'''])  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[int, float]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       List[Any]  ,							)				->Optional[int]:
        UpperCAmelCase_            =  image.astype(np.floataa  )
        if offset:
              UpperCAmelCase_            =  image - (scale / 2)
        return rescale(UpperCAmelCase__  ,							scale=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[float, List[float]]  ,							UpperCAmelCase__			:       Union[float, List[float]]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       str  ,							)				->np.ndarray:
        return normalize(UpperCAmelCase__  ,							mean=UpperCAmelCase__  ,							std=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       ImageInput  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       PILImageResampling = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       float = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[ChannelDimension] = ChannelDimension.FIRST  ,							)				->np.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_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.'''  )

        if offset and not do_rescale:
              raise ValueError('''For offset, do_rescale must also be set to True.'''  )

        # All transformations expect numpy arrays.
        UpperCAmelCase_            =  to_numpy_array(UpperCAmelCase__  )

        if do_resize:
              UpperCAmelCase_            =  self.resize(image=UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  )

        if do_center_crop:
              UpperCAmelCase_            =  self.center_crop(UpperCAmelCase__  ,							size=UpperCAmelCase__  )

        if do_rescale:
              UpperCAmelCase_            =  self.rescale(image=UpperCAmelCase__  ,							scale=UpperCAmelCase__  ,							offset=UpperCAmelCase__  )

        if do_normalize:
              UpperCAmelCase_            =  self.normalize(image=UpperCAmelCase__  ,							mean=UpperCAmelCase__  ,							std=UpperCAmelCase__  )

        UpperCAmelCase_            =  to_channel_dimension_format(UpperCAmelCase__  ,							UpperCAmelCase__  )
        return image






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       ImageInput  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       PILImageResampling = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       float = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[str, TensorType]] = None  ,							UpperCAmelCase__			:       ChannelDimension = ChannelDimension.FIRST  ,							**UpperCAmelCase__			:       Union[str, Any]  ,							)				->PIL.Image.Image:
        UpperCAmelCase_            =  do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase_            =  resample if resample is not None else self.resample
        UpperCAmelCase_            =  do_center_crop if do_center_crop is not None else self.do_center_crop
        UpperCAmelCase_            =  do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase_            =  rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase_            =  offset if offset is not None else self.offset
        UpperCAmelCase_            =  do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase_            =  image_std if image_std is not None else self.image_std

        UpperCAmelCase_            =  size if size is not None else self.size
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  ,							default_to_square=UpperCAmelCase__  )
        UpperCAmelCase_            =  crop_size if crop_size is not None else self.crop_size
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  ,							param_name='''crop_size'''  )

        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.'''  )

        UpperCAmelCase_            =  make_batched(UpperCAmelCase__  )

        UpperCAmelCase_            =  [
            [
                self._preprocess_image(
                    image=UpperCAmelCase__  ,							do_resize=UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  ,							do_center_crop=UpperCAmelCase__  ,							crop_size=UpperCAmelCase__  ,							do_rescale=UpperCAmelCase__  ,							rescale_factor=UpperCAmelCase__  ,							offset=UpperCAmelCase__  ,							do_normalize=UpperCAmelCase__  ,							image_mean=UpperCAmelCase__  ,							image_std=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							)
                for img in video
            ]
            for video in videos
        ]

        UpperCAmelCase_            =  {'''pixel_values''': videos}
        return BatchFeature(data=UpperCAmelCase__  ,							tensor_type=UpperCAmelCase__  )






'''simple docstring'''
from heapq import heappop, heappush

import numpy as np



def  __lowerCamelCase       (      _UpperCamelCase  :  np.ndarray							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  bool							,    ):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  grid.shape
      UpperCAmelCase_            =  [-1, 1, 0, 0]
      UpperCAmelCase_            =  [0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      UpperCAmelCase_      ,		UpperCAmelCase_            =  [(0, source)], set()
      UpperCAmelCase_            =  np.full((rows, cols)							,    np.inf					)
      UpperCAmelCase_            =  0
      UpperCAmelCase_            =  np.empty((rows, cols)							,    dtype=_UpperCamelCase					)
      UpperCAmelCase_            =  None

      while queue:
            ((UpperCAmelCase_)      ,		(UpperCAmelCase_))            =  heappop(_UpperCamelCase					)
            if (x, y) in visited:
                  continue
            visited.add((x, y)					)

            if (x, y) == destination:
                  UpperCAmelCase_            =  []
                  while (x, y) != source:
                        path.append((x, y)					)
                        UpperCAmelCase_      ,		UpperCAmelCase_            =  predecessors[x, y]
                  path.append(_UpperCamelCase					)  # add the source manually
                  path.reverse()
                  return matrix[destination], path

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

      return np.inf, []


if __name__ == "__main__":
      import doctest

      doctest.testmod()





