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


if is_vision_available():
							import PIL


__magic_name__       =			logging.get_logger(__name__)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[str]						   =		['''pixel_values''']




		def __init__(     self							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = PILImageResampling.BILINEAR							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = 1 / 2_5_5							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = size if size is not None else {"""shortest_edge""": 2_2_4}
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  default_to_square=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  param_name="""crop_size""")

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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = PILImageResampling.BILINEAR							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  default_to_square=lowerCAmelCase__)
				if "shortest_edge" in size:
						__SCREAMING_SNAKE_CASE            = get_resize_output_image_size(lowerCAmelCase__							,  size["""shortest_edge"""]							,  default_to_square=lowerCAmelCase__)
				elif "height" in size and "width" in size:
						__SCREAMING_SNAKE_CASE            = (size["""height"""], size["""width"""])
				else:
						raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
				return resize(lowerCAmelCase__							,  size=lowerCAmelCase__							,  resample=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__)
				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(lowerCAmelCase__							,  size=(size["""height"""], size["""width"""])							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				return rescale(lowerCAmelCase__							,  scale=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				return normalize(lowerCAmelCase__							,  mean=lowerCAmelCase__							,  std=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 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.
				__SCREAMING_SNAKE_CASE            = to_numpy_array(lowerCAmelCase__)

				if do_resize:
						__SCREAMING_SNAKE_CASE            = self.resize(image=lowerCAmelCase__							,  size=lowerCAmelCase__							,  resample=lowerCAmelCase__)

				if do_center_crop:
						__SCREAMING_SNAKE_CASE            = self.center_crop(lowerCAmelCase__							,  size=lowerCAmelCase__)

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

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

				__SCREAMING_SNAKE_CASE            = to_channel_dimension_format(lowerCAmelCase__							,  lowerCAmelCase__)
				return image




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = ChannelDimension.FIRST							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = do_resize if do_resize is not None else self.do_resize
				__SCREAMING_SNAKE_CASE            = resample if resample is not None else self.resample
				__SCREAMING_SNAKE_CASE            = do_center_crop if do_center_crop is not None else self.do_center_crop
				__SCREAMING_SNAKE_CASE            = do_rescale if do_rescale is not None else self.do_rescale
				__SCREAMING_SNAKE_CASE            = rescale_factor if rescale_factor is not None else self.rescale_factor
				__SCREAMING_SNAKE_CASE            = do_normalize if do_normalize is not None else self.do_normalize
				__SCREAMING_SNAKE_CASE            = image_mean if image_mean is not None else self.image_mean
				__SCREAMING_SNAKE_CASE            = image_std if image_std is not None else self.image_std

				__SCREAMING_SNAKE_CASE            = size if size is not None else self.size
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  default_to_square=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = crop_size if crop_size is not None else self.crop_size
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  param_name="""crop_size""")

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

				__SCREAMING_SNAKE_CASE            = make_batched(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = [
				    [
				        self._preprocess_image(
				            image=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=lowerCAmelCase__							,  image_std=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  )
				        for img in video
				    ]
				    for video in videos
				]

				__SCREAMING_SNAKE_CASE            = {"""pixel_values""": videos}
				return BatchFeature(data=lowerCAmelCase__							,  tensor_type=lowerCAmelCase__)












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						TypeVar('''T''')






class      a						(					Generic[T]		):






    def __init__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						self
      lowerCamelCase_         =						0






class      a						(					Generic[T]		):






    def __init__(       self     :							Any					)     ->		None:
      # map from node name to the node object
      lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # create a new set with x as its member
      lowerCamelCase_         =						DisjointSetTreeNode(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		DisjointSetTreeNode[T]:
      # find the set x belongs to (with path-compression)
      lowerCamelCase_         =						self.map[data]
      if elem_ref != elem_ref.parent:
        lowerCamelCase_         =						self.find_set(elem_ref.parent.data					)
      return elem_ref.parent






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							DisjointSetTreeNode[T]       ,						__SCREAMING_SNAKE_CASE     :							DisjointSetTreeNode[T]					)     ->		None:
      # helper function for union operation
      if nodea.rank > nodea.rank:
        lowerCamelCase_         =						nodea
      else:
        lowerCamelCase_         =						nodea
        if nodea.rank == nodea.rank:
          nodea.rank += 1






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # merge 2 disjoint sets
      self.link(self.find_set(__SCREAMING_SNAKE_CASE					)       ,						self.find_set(__SCREAMING_SNAKE_CASE					)					)






class      a						(					Generic[T]		):






    def __init__(       self     :							Optional[int]					)     ->		None:
      # connections: map from the node to the neighbouring nodes (with weights)
      lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # add a node ONLY if its not present in the graph
      if node not in self.connections:
        lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		None:
      # add an edge with the given weight
      self.add_node(__SCREAMING_SNAKE_CASE					)
      self.add_node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						weight
      lowerCamelCase_         =						weight






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		GraphUndirectedWeighted[T]:
      lowerCamelCase_         =						[]
      lowerCamelCase_         =						set()
      for start in self.connections:
        for end in self.connections[start]:
          if (start, end) not in seen:
            seen.add((end, start)					)
            edges.append((start, end, self.connections[start][end])					)
      edges.sort(key=lambda __SCREAMING_SNAKE_CASE					: x[2]					)

      # creating the disjoint set
      lowerCamelCase_         =						DisjointSetTree[T]()
      for node in self.connections:
        disjoint_set.make_set(__SCREAMING_SNAKE_CASE					)

      # MST generation
      lowerCamelCase_         =						0
      lowerCamelCase_         =						0
      lowerCamelCase_         =						GraphUndirectedWeighted[T]()
      while num_edges < len(self.connections					) - 1:
        lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						edges[index]
        index += 1
        lowerCamelCase_         =						disjoint_set.find_set(__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						disjoint_set.find_set(__SCREAMING_SNAKE_CASE					)
        if parent_u != parent_v:
          num_edges += 1
          graph.add_edge(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          disjoint_set.union(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      return graph




