



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__						(							_SCREAMING_SNAKE_CASE       :			List[Any]    ):
				"""simple docstring"""







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

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

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

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






class    SCREAMING_SNAKE_CASE			(							lowercase__					):
							__lowerCamelCase	:	List[Any]    =['pixel_values']





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Tuple = True      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		List[Any] = PILImageResampling.BILINEAR      ,				__lowercase		:		List[str] = True      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[Any] = True      ,				__lowercase		:		Optional[Any] = 1 / 255      ,				__lowercase		:		Optional[Any] = True      ,				__lowercase		:		Tuple = None      ,				__lowercase		:		str = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)
											__a       =		size if size is not None else {"""shortest_edge""": 224}
											__a       =		get_size_dict(__lowercase      ,				default_to_square=__lowercase	)
											__a       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

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





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		int      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Any = PILImageResampling.BILINEAR      ,				__lowercase		:		str = None      ,				**__lowercase		:		Tuple      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase      ,				default_to_square=__lowercase	)
											if "shortest_edge" in size:
															__a       =		get_resize_output_image_size(__lowercase      ,				size["""shortest_edge"""]      ,				default_to_square=__lowercase	)
											elif "height" in size and "width" in size:
															__a       =		(size["""height"""], size["""width"""])
											else:
															raise ValueError(F"Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"	)
											return resize(__lowercase      ,				size=__lowercase      ,				resample=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		List[str]      ,				__lowercase		:		Any = None      ,				**__lowercase		:		Optional[Any]      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											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(__lowercase      ,				size=(size["""height"""], size["""width"""])      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		Tuple      ,				__lowercase		:		Dict      ,				__lowercase		:		Any = None      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											return rescale(__lowercase      ,				scale=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Dict      ,				__lowercase		:		List[str]      ,				__lowercase		:		Optional[int] = None      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											return normalize(__lowercase      ,				mean=__lowercase      ,				std=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		List[Any] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = None      ,				__lowercase		:		Any = None      ,				__lowercase		:		str = None      ,				__lowercase		:		List[str] = None      ,				__lowercase		:		str = None      ,				__lowercase		:		Union[str, Any] = None      ,				__lowercase		:		int = None      ,				__lowercase		:		Optional[Any] = ChannelDimension.FIRST      ,				):

											'''simple docstring'''
											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.
											__a       =		to_numpy_array(__lowercase	)

											if do_resize:
															__a       =		self.resize(image=__lowercase      ,				size=__lowercase      ,				resample=__lowercase	)

											if do_center_crop:
															__a       =		self.center_crop(__lowercase      ,				size=__lowercase	)

											if do_rescale:
															__a       =		self.rescale(image=__lowercase      ,				scale=__lowercase	)

											if do_normalize:
															__a       =		self.normalize(image=__lowercase      ,				mean=__lowercase      ,				std=__lowercase	)

											__a       =		to_channel_dimension_format(__lowercase      ,				__lowercase	)
											return image







							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Dict = None      ,				__lowercase		:		Any = None      ,				__lowercase		:		str = None      ,				__lowercase		:		Optional[Any] = None      ,				__lowercase		:		str = None      ,				__lowercase		:		Union[str, Any] = None      ,				__lowercase		:		List[str] = None      ,				__lowercase		:		List[str] = None      ,				__lowercase		:		str = None      ,				__lowercase		:		Dict = None      ,				__lowercase		:		Dict = None      ,				__lowercase		:		Dict = ChannelDimension.FIRST      ,				**__lowercase		:		Dict      ,				):

											'''simple docstring'''
											__a       =		do_resize if do_resize is not None else self.do_resize
											__a       =		resample if resample is not None else self.resample
											__a       =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__a       =		do_rescale if do_rescale is not None else self.do_rescale
											__a       =		rescale_factor if rescale_factor is not None else self.rescale_factor
											__a       =		do_normalize if do_normalize is not None else self.do_normalize
											__a       =		image_mean if image_mean is not None else self.image_mean
											__a       =		image_std if image_std is not None else self.image_std

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

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

											__a       =		make_batched(__lowercase	)

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

											__a       =		{"""pixel_values""": videos}
											return BatchFeature(data=__lowercase      ,				tensor_type=__lowercase	)





from __future__ import annotations

from collections import Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
