
from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_					:						str				= logging.get_logger(__name__)



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				["pixel_values"]




			def __init__(				self						:					int       ,   _snake_case						:					bool = True       ,   _snake_case						:					int = 32       ,   _snake_case						:					Tuple=PILImageResampling.BILINEAR       ,   _snake_case						:					bool = True       ,   **_snake_case						:					List[Any]       ,   )      -> None:







								"""simple docstring"""
								A_				 =						do_resize
								A_				 =						do_rescale
								A_				 =						size_divisor
								A_				 =						resample
								super().__init__(**_snake_case	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					np.ndarray       ,   _snake_case						:					int       ,   _snake_case						:					List[str]       ,   _snake_case						:					Optional[ChannelDimension] = None       ,   **_snake_case						:					Dict	)      -> np.ndarray:







								"""simple docstring"""
								A_ ,    A_				 =						get_image_size(_snake_case	)
								# Rounds the height and width down to the closest multiple of size_divisor
								A_				 =						height // size_divisor * size_divisor
								A_				 =						width // size_divisor * size_divisor
								A_				 =						resize(_snake_case       ,   (new_h, new_w)       ,   resample=_snake_case       ,   data_format=_snake_case       ,   **_snake_case	)
								return image




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					np.ndarray       ,   _snake_case						:					float       ,   _snake_case						:					Optional[ChannelDimension] = None       ,   **_snake_case						:					str	)      -> np.ndarray:







								"""simple docstring"""
								return rescale(image=_snake_case       ,   scale=_snake_case       ,   data_format=_snake_case       ,   **_snake_case	)






			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   _snake_case						:					Optional[bool] = None       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					int=None       ,   _snake_case						:					Optional[bool] = None       ,   _snake_case						:					Optional[Union[TensorType, str]] = None       ,   _snake_case						:					ChannelDimension = ChannelDimension.FIRST       ,   **_snake_case						:					Tuple       ,   )      -> BatchFeature:







								"""simple docstring"""
								A_				 =						do_resize if do_resize is not None else self.do_resize
								A_				 =						do_rescale if do_rescale is not None else self.do_rescale
								A_				 =						size_divisor if size_divisor is not None else self.size_divisor
								A_				 =						resample if resample is not None else self.resample

								if do_resize and size_divisor is None:
													raise ValueError("size_divisor is required for resizing"	)

								A_				 =						make_list_of_images(_snake_case	)

								if not valid_images(_snake_case	):
													raise ValueError("Invalid image(s)"	)

								# All transformations expect numpy arrays.
								A_				 =						[to_numpy_array(_snake_case	) for img in images]

								if do_resize:
													A_				 =						[self.resize(_snake_case       ,   size_divisor=_snake_case       ,   resample=_snake_case	) for image in images]

								if do_rescale:
													A_				 =						[self.rescale(_snake_case       ,   scale=1 / 255	) for image in images]

								A_				 =						[to_channel_dimension_format(_snake_case       ,   _snake_case	) for image in images]

								A_				 =						{"pixel_values": images}
								return BatchFeature(data=_snake_case       ,   tensor_type=_snake_case	)






"""simple docstring"""





from __future__ import annotations

from collections import Counter
from random import random



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str	)      -> None:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					float	)      -> None:







								"""simple docstring"""
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								A_				 =						probability




			def      lowerCamelCase__     (				self						:					Tuple	)      -> list[str]:







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






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str	)      -> 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 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						MarkovChainGraphUndirectedUnweighted()

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

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

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

					return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()




