

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__		      =       logging.get_logger(__name__)


if is_vision_available():
	import PIL

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				['pixel_values']


			def __init__(			self					,					lowercase = True					,					lowercase = None					,					lowercase = PILImageResampling.BICUBIC					,					lowercase = True					,					lowercase = None					,					lowercase = True					,					lowercase = 1 / 255					,					lowercase = True					,					lowercase = None					,					lowercase = None					,					lowercase = True					,					**lowercase					,					)     ->  None:
							'''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					,					default_to_square=lowercase					,					param_name="crop_size"				)

							A__						=	do_resize
							A__						=	size
							A__						=	resample
							A__						=	do_center_crop
							A__						=	crop_size
							A__						=	do_rescale
							A__						=	rescale_factor
							A__						=	do_normalize
							A__						=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							A__						=	image_std if image_std is not None else OPENAI_CLIP_STD
							A__						=	do_convert_rgb


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = PILImageResampling.BICUBIC					,					lowercase = None					,					**lowercase					,					)     ->  np.ndarray:
							'''simple docstring'''






							A__						=	get_size_dict(lowercase					,					default_to_square=lowercase				)
							if "shortest_edge" not in size:
											raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'				)
							A__						=	get_resize_output_image_size(lowercase					,					size=size["shortest_edge"]					,					default_to_square=lowercase				)
							return resize(lowercase					,					size=lowercase					,					resample=lowercase					,					data_format=lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = None					,					**lowercase					,					)     ->  np.ndarray:
							'''simple docstring'''






							A__						=	get_size_dict(lowercase				)
							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(lowercase					,					size=(size["height"], size["width"])					,					data_format=lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = None					,					**lowercase					,					)     ->  int:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase = None					,					**lowercase					,					)     ->  np.ndarray:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = ChannelDimension.FIRST					,					**lowercase					,					)     ->  PIL.Image.Image:
							'''simple docstring'''






							A__						=	do_resize if do_resize is not None else self.do_resize
							A__						=	size if size is not None else self.size
							A__						=	get_size_dict(lowercase					,					param_name="size"					,					default_to_square=lowercase				)
							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__						=	crop_size if crop_size is not None else self.crop_size
							A__						=	get_size_dict(lowercase					,					param_name="crop_size"					,					default_to_square=lowercase				)
							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__						=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							A__						=	make_list_of_images(lowercase				)

							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."				)

							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."				)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
											A__						=	[convert_to_rgb(lowercase				) for image in images]

							# All transformations expect numpy arrays.
							A__						=	[to_numpy_array(lowercase				) for image in images]

							if do_resize:
											A__						=	[self.resize(image=lowercase					,					size=lowercase					,					resample=lowercase				) for image in images]

							if do_center_crop:
											A__						=	[self.center_crop(image=lowercase					,					size=lowercase				) for image in images]

							if do_rescale:
											A__						=	[self.rescale(image=lowercase					,					scale=lowercase				) for image in images]

							if do_normalize:
											A__						=	[self.normalize(image=lowercase					,					mean=lowercase					,					std=lowercase				) for image in images]

							A__						=	[to_channel_dimension_format(lowercase					,					lowercase				) for image in images]

							A__						=	{"pixel_values": images}
							return BatchFeature(data=lowercase					,					tensor_type=lowercase				)






"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= [0] * len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Tuple								= [1] * len(lowerCAmelCase__						)

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowerCAmelCase__						)						):
    if indegree[i] == 0:
      queue.append(lowerCAmelCase__						)

  while queue:
    lowerCAmelCase_   :				Union[str, Any]								= queue.pop(0						)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCAmelCase_   :				Any								= long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(lowerCAmelCase__						)

  print(max(lowerCAmelCase__						)						)


# Adjacency list of Graph
lowercase__		:				Any          =	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)





