






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


__A      =				logging.get_logger(__name__)




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					def constraint_to_multiple_of(_lowercase					,			_lowercase					,			_lowercase=0					,			_lowercase=None ):
										_A								= round(val / multiple ) * multiple

										if max_val is not None and x > max_val:
															_A								= math.floor(val / multiple ) * multiple

										if x < min_val:
															_A								= math.ceil(val / multiple ) * multiple

										return x

					_A								= (output_size, output_size) if isinstance(_lowercase					,			_lowercase ) else output_size

					_A							,_A								= get_image_size(_lowercase )
					_A							,_A								= output_size

					# determine new height and width
					_A								= output_height / input_height
					_A								= output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width ) < abs(1 - scale_height ):
															# fit width
															_A								= scale_width
										else:
															# fit height
															_A								= scale_height

					_A								= constraint_to_multiple_of(scale_height * input_height					,			multiple=_lowercase )
					_A								= constraint_to_multiple_of(scale_width * input_width					,			multiple=_lowercase )

					return (new_height, new_width)





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					["pixel_values"]

							def __init__(  self:   int		,     __A:   bool = True		,     __A:   Dict[str, int] = None		,     __A:   PILImageResampling = PILImageResampling.BILINEAR		,     __A:   bool = False		,     __A:   int = 1		,     __A:   bool = True		,     __A:   Union[int, float] = 1 / 2_55		,     __A:   bool = True		,     __A:   Optional[Union[float, List[float]]] = None		,     __A:   Optional[Union[float, List[float]]] = None		,     **__A:   Optional[int]		,     )    ->				None:
												super().__init__(**__A   )
												_A								= size if size is not None else {'''height''': 3_84, '''width''': 3_84}
												_A								= get_size_dict(__A   )
												_A								= do_resize
												_A								= size
												_A								= keep_aspect_ratio
												_A								= ensure_multiple_of
												_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       __A (  self:   Dict		,     __A:   np.ndarray		,     __A:   Dict[str, int]		,     __A:   bool = False		,     __A:   int = 1		,     __A:   PILImageResampling = PILImageResampling.BICUBIC		,     __A:   Optional[Union[str, ChannelDimension]] = None		,     **__A:   Union[str, Any]		,     )    ->				np.ndarray:
												_A								= get_size_dict(__A   )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""   )
												_A								= get_resize_output_image_size(
												    __A		,     output_size=(size['''height'''], size['''width'''])		,     keep_aspect_ratio=__A		,     multiple=__A		,     )
												return resize(__A		,     size=__A		,     resample=__A		,     data_format=__A		,     **__A   )

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

							def       __A (  self:   Optional[int]		,     __A:   np.ndarray		,     __A:   Union[float, List[float]]		,     __A:   Union[float, List[float]]		,     __A:   Optional[Union[str, ChannelDimension]] = None		,     **__A:   Optional[Any]		,     )    ->				np.ndarray:
												return normalize(__A		,     mean=__A		,     std=__A		,     data_format=__A		,     **__A   )

							def       __A (  self:   Any		,     __A:   ImageInput		,     __A:   bool = None		,     __A:   int = None		,     __A:   bool = None		,     __A:   int = None		,     __A:   PILImageResampling = None		,     __A:   bool = None		,     __A:   float = None		,     __A:   bool = None		,     __A:   Optional[Union[float, List[float]]] = None		,     __A:   Optional[Union[float, List[float]]] = None		,     __A:   Optional[Union[str, TensorType]] = None		,     __A:   ChannelDimension = ChannelDimension.FIRST		,     **__A:   Tuple		,     )    ->				PIL.Image.Image:
												_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(__A   )
												_A								= keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_A								= ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_A								= resample if resample is not None else self.resample
												_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								= make_list_of_images(__A   )

												if not valid_images(__A   ):
																	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 or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize 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(__A   ) for image in images]

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

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

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

												_A								= [to_channel_dimension_format(__A		,     __A   ) for image in images]

												_A								= {'''pixel_values''': images}
												return BatchFeature(data=__A		,     tensor_type=__A   )



							def       __A (  self:   Union[str, Any]		,     __A:   int		,     __A:   List[Tuple] = None   )    ->				str:
												_A								= outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																	if len(__A   ) != len(__A   ):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																	if is_torch_tensor(__A   ):
																						_A								= target_sizes.numpy()

																	_A								= []

																	for idx in range(len(__A   )   ):
																						_A								= torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0   )		,     size=target_sizes[idx]		,     mode='''bilinear'''		,     align_corners=__A   )
																						_A								= resized_logits[0].argmax(dim=0   )
																						semantic_segmentation.append(__A   )
												else:
																	_A								= logits.argmax(dim=1   )
																	_A								= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

												return semantic_segmentation











def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [0] * len(_lowercase )
					_A								= []
					_A								= []
					_A								= 0

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

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

					while queue:
										_A								= queue.pop(0 )
										cnt += 1
										topo.append(_lowercase )
										for x in graph[vertex]:
															indegree[x] -= 1
															if indegree[x] == 0:
																				queue.append(_lowercase )

					if cnt != len(_lowercase ):
										print('''Cycle exists''' )
					else:
										print(_lowercase )


# Adjacency List of Graph
__A      =				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}


topological_sort(graph)



