



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


_lowercase     							=		logging.get_logger(__name__)






def      _A				(UpperCamelCase		:      str )  ->List[List[ImageInput]]:



			'''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 				__A					( A_      ):
			UpperCamelCase					:int     =			['''pixel_values''']





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

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





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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		get_size_dict(__magic_name__				)
						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(__magic_name__							,    size=(size["""height"""], size["""width"""])							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return rescale(__magic_name__							,    scale=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = 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__ :      int								=		to_numpy_array(__magic_name__				)

						if do_resize:
									lowerCamelCase__ :      str								=		self.resize(image=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__				)

						if do_center_crop:
									lowerCamelCase__ :      Dict								=		self.center_crop(__magic_name__							,    size=__magic_name__				)

						if do_rescale:
									lowerCamelCase__ :      Optional[Any]								=		self.rescale(image=__magic_name__							,    scale=__magic_name__				)

						if do_normalize:
									lowerCamelCase__ :      List[Any]								=		self.normalize(image=__magic_name__							,    mean=__magic_name__							,    std=__magic_name__				)

						lowerCamelCase__ :      Union[str, Any]								=		to_channel_dimension_format(__magic_name__							,    __magic_name__				)
						return image





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

						lowerCamelCase__ :      Union[str, Any]								=		size if size is not None else self.size
						lowerCamelCase__ :      Union[str, Any]								=		get_size_dict(__magic_name__							,    default_to_square=__magic_name__				)
						lowerCamelCase__ :      Any								=		crop_size if crop_size is not None else self.crop_size
						lowerCamelCase__ :      int								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

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

						lowerCamelCase__ :      Optional[Any]								=		make_batched(__magic_name__				)

						lowerCamelCase__ :      Any								=		[
						    [
						        self._preprocess_image(
						            image=__magic_name__							,    do_resize=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__							,    do_center_crop=__magic_name__							,    crop_size=__magic_name__							,    do_rescale=__magic_name__							,    rescale_factor=__magic_name__							,    do_normalize=__magic_name__							,    image_mean=__magic_name__							,    image_std=__magic_name__							,    data_format=__magic_name__							,    )
						        for img in video
						    ]
						    for video in videos
						]

						lowerCamelCase__ :      Any								=		{"""pixel_values""": videos}
						return BatchFeature(data=__magic_name__							,    tensor_type=__magic_name__				)








# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowercase     							=						TypeVar('''T''')
class 				__A					( Generic[T]      ):





			def __init__(self							,    __magic_name__ = True				):
						lowerCamelCase__ :      dict[T, list[T]]								=		{}  # dictionary of lists
						lowerCamelCase__ :      List[str]								=		directed





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):

						if not self.directed:  # For undirected graphs
									# if both source vertex and destination vertex are both present in the
									# adjacency list, add destination vertex to source vertex list of adjacent
									# vertices and add source vertex to destination vertex list of adjacent
									# vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												self.adj_list[destination_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination vertex
									# to source vertex list of adjacent vertices, then create a new vertex with
									# destination vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      int								=		[source_vertex]
									# if only destination vertex is present in adjacency list, add source vertex
									# to destination vertex list of adjacent vertices, then create a new vertex
									# with source vertex as key and assign a list containing the source vertex
									# as it's first adjacent vertex.
									elif destination_vertex in self.adj_list:
												self.adj_list[destination_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Dict								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and assign a list
									# containing the destination vertex as it's first adjacent vertex also
									# create a new vertex with destination vertex as key and assign a list
									# containing the source vertex as it's first adjacent vertex.
									else:
												lowerCamelCase__ :      Any								=		[destination_vertex]
												lowerCamelCase__ :      Optional[Any]								=		[source_vertex]
						else:  # For directed graphs
									# if both source vertex and destination vertex are present in adjacency
									# list, add destination vertex to source vertex list of adjacent vertices.
									if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
									# if only source vertex is present in adjacency list, add destination
									# vertex to source vertex list of adjacent vertices and create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(__magic_name__				)
												lowerCamelCase__ :      Optional[Any]								=		[]
									# if only destination vertex is present in adjacency list, create a new
									# vertex with source vertex as key and assign a list containing destination
									# vertex as first adjacent vertex
									elif destination_vertex in self.adj_list:
												lowerCamelCase__ :      Tuple								=		[destination_vertex]
									# if both source vertex and destination vertex are not present in adjacency
									# list, create a new vertex with source vertex as key and a list containing
									# destination vertex as it's first adjacent vertex. Then create a new vertex
									# with destination vertex as key, which has no adjacent vertex
									else:
												lowerCamelCase__ :      Optional[int]								=		[destination_vertex]
												lowerCamelCase__ :      List[Any]								=		[]

						return self





			def __repr__(self				):
						return pformat(self.adj_list				)



