



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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A__								=						logging.get_logger(__name__)


class       a							(			lowerCAmelCase_				):
		__lowerCAmelCase	:    Optional[int]											=					['pixel_values']



		def __init__(     self     :Dict   ,__lowercase     :bool = True   ,__lowercase     :Optional[Dict[str, int]] = None   ,__lowercase     :PILImageResampling = PILImageResampling.BICUBIC   ,__lowercase     :bool = True   ,__lowercase     :bool = True   ,__lowercase     :Union[int, float] = 1 / 2_5_5   ,__lowercase     :Dict[str, int] = None   ,__lowercase     :bool = True   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,**__lowercase     :Tuple   ,):
						super().__init__(**__lowerCAmelCase  )
						snake_case__	:      str     		=	size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						snake_case__	:      Union[str, Any]     		=	get_size_dict(__lowerCAmelCase  )
						snake_case__	:      Tuple     		=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						snake_case__	:      List[Any]     		=	get_size_dict(__lowerCAmelCase   ,default_to_square=__lowerCAmelCase   ,param_name='''crop_size'''  )

						snake_case__	:      List[Any]     		=	do_resize
						snake_case__	:      Tuple     		=	do_rescale
						snake_case__	:      Tuple     		=	do_normalize
						snake_case__	:      Any     		=	do_center_crop
						snake_case__	:      str     		=	crop_size
						snake_case__	:      Optional[Any]     		=	size
						snake_case__	:      List[Any]     		=	resample
						snake_case__	:      int     		=	rescale_factor
						snake_case__	:      Tuple     		=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						snake_case__	:      int     		=	image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :np.ndarray   ,__lowercase     :Dict[str, int]   ,__lowercase     :PILImageResampling = PILImageResampling.BILINEAR   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :Optional[Any]   ,):
						snake_case__	:      str     		=	get_size_dict(__lowerCAmelCase  )

						if "shortest_edge" in size:
										snake_case__	:      Dict     		=	get_resize_output_image_size(__lowerCAmelCase   ,size=size['''shortest_edge''']   ,default_to_square=__lowerCAmelCase  )
										# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
						elif "height" in size and "width" in size:
										snake_case__	:      Dict     		=	(size['''height'''], size['''width'''])
						else:
										raise ValueError(F"""Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}"""  )
						return resize(__lowerCAmelCase   ,size=__lowerCAmelCase   ,resample=__lowerCAmelCase   ,data_format=__lowerCAmelCase   ,**__lowerCAmelCase  )



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :np.ndarray   ,__lowercase     :Dict[str, int]   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :Any   ,):
						snake_case__	:      Optional[Any]     		=	get_size_dict(__lowerCAmelCase  )
						if "height" not in size or "width" not in size:
										raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
						return center_crop(__lowerCAmelCase   ,size=(size['''height'''], size['''width'''])   ,data_format=__lowerCAmelCase   ,**__lowerCAmelCase  )



		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :np.ndarray   ,__lowercase     :float   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :List[str]  ):
						return rescale(__lowerCAmelCase   ,scale=__lowerCAmelCase   ,data_format=__lowerCAmelCase   ,**__lowerCAmelCase  )



		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :np.ndarray   ,__lowercase     :Union[float, List[float]]   ,__lowercase     :Union[float, List[float]]   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :Dict   ,):
						return normalize(__lowerCAmelCase   ,mean=__lowerCAmelCase   ,std=__lowerCAmelCase   ,data_format=__lowerCAmelCase   ,**__lowerCAmelCase  )






		def 	__lowerCamelCase			(     self     :Dict   ,__lowercase     :ImageInput   ,__lowercase     :Optional[bool] = None   ,__lowercase     :Dict[str, int] = None   ,__lowercase     :PILImageResampling = None   ,__lowercase     :bool = None   ,__lowercase     :int = None   ,__lowercase     :Optional[bool] = None   ,__lowercase     :Optional[float] = None   ,__lowercase     :Optional[bool] = None   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,__lowercase     :Optional[Union[str, TensorType]] = None   ,__lowercase     :Union[str, ChannelDimension] = ChannelDimension.FIRST   ,**__lowercase     :Union[str, Any]   ,):
						snake_case__	:      Tuple     		=	do_resize if do_resize is not None else self.do_resize
						snake_case__	:      List[Any]     		=	do_rescale if do_rescale is not None else self.do_rescale
						snake_case__	:      Tuple     		=	do_normalize if do_normalize is not None else self.do_normalize
						snake_case__	:      int     		=	do_center_crop if do_center_crop is not None else self.do_center_crop
						snake_case__	:      int     		=	crop_size if crop_size is not None else self.crop_size
						snake_case__	:      List[Any]     		=	get_size_dict(__lowerCAmelCase   ,param_name='''crop_size'''   ,default_to_square=__lowerCAmelCase  )
						snake_case__	:      List[str]     		=	resample if resample is not None else self.resample
						snake_case__	:      Dict     		=	rescale_factor if rescale_factor is not None else self.rescale_factor
						snake_case__	:      str     		=	image_mean if image_mean is not None else self.image_mean
						snake_case__	:      int     		=	image_std if image_std is not None else self.image_std

						snake_case__	:      Optional[int]     		=	size if size is not None else self.size
						snake_case__	:      Tuple     		=	get_size_dict(__lowerCAmelCase  )

						if not is_batched(__lowerCAmelCase  ):
										snake_case__	:      List[Any]     		=	[images]

						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.'''  )

						if do_resize and size is None:
										raise ValueError('''Size 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.'''  )

						# All transformations expect numpy arrays.
						snake_case__	:      Optional[int]     		=	[to_numpy_array(__lowerCAmelCase  ) for image in images]

						if do_resize:
										snake_case__	:      Any     		=	[self.resize(image=__lowerCAmelCase   ,size=__lowerCAmelCase   ,resample=__lowerCAmelCase  ) for image in images]

						if do_center_crop:
										snake_case__	:      Any     		=	[self.center_crop(image=__lowerCAmelCase   ,size=__lowerCAmelCase  ) for image in images]

						if do_rescale:
										snake_case__	:      Union[str, Any]     		=	[self.rescale(image=__lowerCAmelCase   ,scale=__lowerCAmelCase  ) for image in images]

						if do_normalize:
										snake_case__	:      List[Any]     		=	[self.normalize(image=__lowerCAmelCase   ,mean=__lowerCAmelCase   ,std=__lowerCAmelCase  ) for image in images]

						snake_case__	:      Optional[int]     		=	[to_channel_dimension_format(__lowerCAmelCase   ,__lowerCAmelCase  ) for image in images]

						snake_case__	:      List[Any]     		=	{'''pixel_values''': images}
						return BatchFeature(data=__lowerCAmelCase   ,tensor_type=__lowerCAmelCase  )


"""simple docstring"""


from __future__ import annotations

from collections import Counter
from random import random


class 				a :


    def __init__(    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     float    ):
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      _UpperCAmelCase         =					probability


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return list(self.connections    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					0
      _UpperCAmelCase         =					random()

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






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					MarkovChainGraphUndirectedUnweighted()

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

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

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

  return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()
