




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


if is_vision_available():
     import PIL


lowerCAmelCase_					    = logging.get_logger(__name__)



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[List[ImageInput]]:
  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     (  _lowercase	):
      A__		     =		['pixel_values']

      def __init__(   self      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = PILImageResampling.BILINEAR      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = 1 / 255      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        super().__init__(**__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     size if size is not None else {'''shortest_edge''': 224}
        lowerCAmelCase__				:  Union[str, Any]		       =     get_size_dict(__UpperCAmelCase      ,				default_to_square=__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        lowerCAmelCase__				:  Any		       =     get_size_dict(__UpperCAmelCase      ,				param_name='''crop_size'''		)

        lowerCAmelCase__				:  Any		       =     do_resize
        lowerCAmelCase__				:  Dict		       =     size
        lowerCAmelCase__				:  Optional[int]		       =     do_center_crop
        lowerCAmelCase__				:  Optional[Any]		       =     crop_size
        lowerCAmelCase__				:  List[Any]		       =     resample
        lowerCAmelCase__				:  Tuple		       =     do_rescale
        lowerCAmelCase__				:  Tuple		       =     rescale_factor
        lowerCAmelCase__				:  Optional[int]		       =     do_normalize
        lowerCAmelCase__				:  Any		       =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCAmelCase__				:  int		       =     image_std if image_std is not None else IMAGENET_STANDARD_STD

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = PILImageResampling.BILINEAR      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        lowerCAmelCase__				:  Union[str, Any]		       =     get_size_dict(__UpperCAmelCase      ,				default_to_square=__UpperCAmelCase		)
        if "shortest_edge" in size:
          lowerCAmelCase__				:  Union[str, Any]		       =     get_resize_output_image_size(__UpperCAmelCase      ,				size['''shortest_edge''']      ,				default_to_square=__UpperCAmelCase		)
        elif "height" in size and "width" in size:
          lowerCAmelCase__				:  Any		       =     (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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        lowerCAmelCase__				:  int		       =     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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        return rescale(__UpperCAmelCase      ,				scale=__UpperCAmelCase      ,				data_format=__UpperCAmelCase      ,				**__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        return normalize(__UpperCAmelCase      ,				mean=__UpperCAmelCase      ,				std=__UpperCAmelCase      ,				data_format=__UpperCAmelCase      ,				**__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = ChannelDimension.FIRST      ,				):
        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.'''		)

        # All transformations expect numpy arrays.
        lowerCAmelCase__				:  Optional[int]		       =     to_numpy_array(__UpperCAmelCase		)

        if do_resize:
          lowerCAmelCase__				:  List[Any]		       =     self.resize(image=__UpperCAmelCase      ,				size=__UpperCAmelCase      ,				resample=__UpperCAmelCase		)

        if do_center_crop:
          lowerCAmelCase__				:  Optional[int]		       =     self.center_crop(__UpperCAmelCase      ,				size=__UpperCAmelCase		)

        if do_rescale:
          lowerCAmelCase__				:  int		       =     self.rescale(image=__UpperCAmelCase      ,				scale=__UpperCAmelCase		)

        if do_normalize:
          lowerCAmelCase__				:  str		       =     self.normalize(image=__UpperCAmelCase      ,				mean=__UpperCAmelCase      ,				std=__UpperCAmelCase		)

        lowerCAmelCase__				:  List[str]		       =     to_channel_dimension_format(__UpperCAmelCase      ,				__UpperCAmelCase		)
        return image







      def 	__magic_name__(   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      ,				):
        lowerCAmelCase__				:  Tuple		       =     do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__				:  int		       =     resample if resample is not None else self.resample
        lowerCAmelCase__				:  Tuple		       =     do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase__				:  Tuple		       =     do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__				:  Dict		       =     rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__				:  str		       =     do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__				:  Dict		       =     image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__				:  Optional[Any]		       =     image_std if image_std is not None else self.image_std

        lowerCAmelCase__				:  List[str]		       =     size if size is not None else self.size
        lowerCAmelCase__				:  List[str]		       =     get_size_dict(__UpperCAmelCase      ,				default_to_square=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase__				:  Optional[int]		       =     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.'''		)

        lowerCAmelCase__				:  Any		       =     make_batched(__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[Any]		       =     [
            [
                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      ,				do_normalize=__UpperCAmelCase      ,				image_mean=__UpperCAmelCase      ,				image_std=__UpperCAmelCase      ,				data_format=__UpperCAmelCase      ,				)
                for img in video
            ]
            for video in videos
        ]

        lowerCAmelCase__				:  Optional[int]		       =     {'''pixel_values''': videos}
        return BatchFeature(data=__UpperCAmelCase      ,				tensor_type=__UpperCAmelCase		)







from collections.abc import Callable






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase = None		):
        # Stores actual heap items.
        lowerCAmelCase__				:  list		       =     []
        # Stores indexes of each item for supporting updates and deletion.
        lowerCAmelCase__				:  dict		       =     {}
        # Stores current size of heap.
        lowerCAmelCase__				:  Any		       =     0
        # Stores function used to evaluate the score of an item on which basis ordering
        # will be done.
        lowerCAmelCase__				:  Union[str, Any]		       =     key or (lambda __UpperCAmelCase		: x)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return int((i - 1) / 2		) if i > 0 else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     int(2 * i + 1		)
        return left if 0 < left < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     int(2 * i + 2		)
        return right if 0 < right < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     (
            self.pos_map[self.arr[j][0]],
            self.pos_map[self.arr[i][0]],
        )
        # Then swap the items in the list.
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Any		       =     self.arr[j], self.arr[i]

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        return self.arr[i][1] < self.arr[j][1]

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     self._left(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     self._right(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     i

        if left is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Dict		       =     left
        if right is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     right

        return valid_parent

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     self._parent(__UpperCAmelCase		)
        while parent is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  Dict		       =     parent, self._parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     self._get_valid_parent(__UpperCAmelCase		)
        while valid_parent != index:
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     valid_parent, self._get_valid_parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        lowerCAmelCase__				:  Any		       =     [item, self.key(__UpperCAmelCase		)]
        # Make sure heap is right in both up and down direction.
        # Ideally only one of them will make any change.
        self._heapify_up(__UpperCAmelCase		)
        self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        del self.pos_map[item]
        lowerCAmelCase__				:  Tuple		       =     self.arr[self.size - 1]
        lowerCAmelCase__				:  int		       =     index
        self.size -= 1
        # Make sure heap is right in both up and down direction. Ideally only one
        # of them will make any change- so no performance loss in calling both.
        if self.size > index:
          self._heapify_up(__UpperCAmelCase		)
          self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     len(self.arr		)
        if arr_len == self.size:
          self.arr.append([item, self.key(__UpperCAmelCase		)]		)
        else:
          lowerCAmelCase__				:  str		       =     [item, self.key(__UpperCAmelCase		)]
        lowerCAmelCase__				:  Optional[Any]		       =     self.size
        self.size += 1
        self._heapify_up(self.size - 1		)

      def 	__magic_name__(   self		):
        return self.arr[0] if self.size else None







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.get_top()
        if top_item_tuple:
          self.delete_item(top_item_tuple[0]		)
        return top_item_tuple



def      __lowerCAmelCase			(					)     ->	None:
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

