


from __future__ import annotations

from collections import Counter
from random import random







class        UpperCamelCase__   :

     def __init__(     self       :   Optional[Any]       ):






          '''simple docstring'''






          lowercase_  							=			{}

     def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   str       ):






          '''simple docstring'''






          lowercase_  							=			{}

     def   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   float       ):






          '''simple docstring'''






          if nodea not in self.connections:
               self.add_node(UpperCamelCase__       )
          if nodea not in self.connections:
               self.add_node(UpperCamelCase__       )
          lowercase_  							=			probability

     def   UpperCAmelCase__       (     self       :   Dict       ):






          '''simple docstring'''






          return list(self.connections       )







     def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   str       ):






          '''simple docstring'''






          lowercase_  							=			0
          lowercase_  							=			random()

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



def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
     lowercase_  							=			MarkovChainGraphUndirectedUnweighted()

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

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

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

     return visited


if __name__ == "__main__":
       import doctest

       doctest.testmod()






from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   ['pixel_values']

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   PILImageResampling = PILImageResampling.BICUBIC							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )
										lowercase_  							=			size if size is not None else {"""shortest_edge""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										lowercase_  							=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					param_name="""crop_size"""       )

										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 if image_mean is not None else IMAGENET_DEFAULT_MEAN
										lowercase_  							=			image_std if image_std is not None else IMAGENET_DEFAULT_STD

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   PILImageResampling = PILImageResampling.BICUBIC							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
										if "shortest_edge" in size:
															lowercase_  							=			int((256 / 224) * size["""shortest_edge"""]       )
															lowercase_  							=			get_resize_output_image_size(UpperCamelCase__							,					size=UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
															lowercase_  							=			{"""height""": output_size[0], """width""": output_size[1]}
										if "height" not in size_dict or "width" not in size_dict:
															raise ValueError(
															    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''       )
										return resize(
										    UpperCamelCase__							,					size=(size_dict["""height"""], size_dict["""width"""])							,					resample=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__       )
										if "height" not in size or "width" not in size:
															raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
										return center_crop(UpperCamelCase__							,					size=(size["""height"""], size["""width"""])							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Union[int, float]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										return rescale(UpperCamelCase__							,					scale=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										return normalize(UpperCamelCase__							,					mean=UpperCamelCase__							,					std=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   PILImageResampling = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = None							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = None							,					UpperCamelCase__       :   Optional[TensorType] = None							,					UpperCamelCase__       :   ChannelDimension = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										lowercase_  							=			do_resize if do_resize is not None else self.do_resize
										lowercase_  							=			resample if resample is not None else self.resample
										lowercase_  							=			do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_  							=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_  							=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_  							=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_  							=			image_mean if image_mean is not None else self.image_mean
										lowercase_  							=			image_std if image_std is not None else self.image_std

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

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															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."""       )

										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.
										lowercase_  							=			[to_numpy_array(UpperCamelCase__       ) for image in images]

										if do_resize:
															lowercase_  							=			[self.resize(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_center_crop:
															lowercase_  							=			[self.center_crop(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_rescale:
															lowercase_  							=			[self.rescale(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_normalize:
															lowercase_  							=			[self.normalize(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			{"""pixel_values""": images}
										return BatchFeature(data=UpperCamelCase__							,					tensor_type=UpperCamelCase__       )


