


'''simple docstring'''

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


lowercase_     		= logging.get_logger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							['pixel_values']






		def __init__(   self		,						__A = True		,						__A = None		,						__A = PILImageResampling.BICUBIC		,						__A = True		,						__A = None		,						__A = True		,						__A = 1 / 255		,						__A = True		,						__A = IMAGENET_DEFAULT_MEAN		,						__A = IMAGENET_DEFAULT_STD		,						**__A		,						) ->     None:
								super().__init__(**__A				)
								_lowerCAmelCase										=size if size is not None else {'shortest_edge': 224}
								_lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
								_lowerCAmelCase										=crop_size if crop_size is not None else {'height': 224, 'width': 224}
								_lowerCAmelCase										=get_size_dict(__A		,						param_name='crop_size'				)

								_lowerCAmelCase										=do_resize
								_lowerCAmelCase										=size
								_lowerCAmelCase										=resample
								_lowerCAmelCase										=do_center_crop
								_lowerCAmelCase										=crop_size
								_lowerCAmelCase										=do_rescale
								_lowerCAmelCase										=rescale_factor
								_lowerCAmelCase										=do_normalize
								_lowerCAmelCase										=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								_lowerCAmelCase										=image_std if image_std is not None else IMAGENET_DEFAULT_STD






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = PILImageResampling.BICUBIC		,						__A = None		,						**__A		,						) ->     np.ndarray:
								_lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
														_lowerCAmelCase										=int((256 / 224) * size['shortest_edge']				)
														_lowerCAmelCase										=get_resize_output_image_size(__A		,						size=__A		,						default_to_square=__A				)
														_lowerCAmelCase										={'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(
								    __A		,						size=(size_dict['height'], size_dict['width'])		,						resample=__A		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
								_lowerCAmelCase										=get_size_dict(__A				)
								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(__A		,						size=(size['height'], size['width'])		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
								return rescale(__A		,						scale=__A		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
								return normalize(__A		,						mean=__A		,						std=__A		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = ChannelDimension.FIRST		,						**__A		,						) ->     BatchFeature:
								_lowerCAmelCase										=do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase										=resample if resample is not None else self.resample
								_lowerCAmelCase										=do_center_crop if do_center_crop is not None else self.do_center_crop
								_lowerCAmelCase										=do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase										=rescale_factor if rescale_factor is not None else self.rescale_factor
								_lowerCAmelCase										=do_normalize if do_normalize is not None else self.do_normalize
								_lowerCAmelCase										=image_mean if image_mean is not None else self.image_mean
								_lowerCAmelCase										=image_std if image_std is not None else self.image_std

								_lowerCAmelCase										=size if size is not None else self.size
								_lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
								_lowerCAmelCase										=crop_size if crop_size is not None else self.crop_size
								_lowerCAmelCase										=get_size_dict(__A		,						param_name='crop_size'				)

								_lowerCAmelCase										=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:
														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.
								_lowerCAmelCase										=[to_numpy_array(__A				) for image in images]

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

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

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

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

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

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






'''simple docstring'''

def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=set()
						# To detect a back edge, keep track of vertices currently in the recursion stack
						_lowerCAmelCase										=set()
						return any(
						    node not in visited and depth_first_search(a__			,      a__			,      a__			,      a__      )
						    for node in graph      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						visited.add(a__      )
						rec_stk.add(a__      )

						for node in graph[vertex]:
												if node not in visited:
																		if depth_first_search(a__			,      a__			,      a__			,      a__      ):
																								return True
												elif node in rec_stk:
																		return True

    # The node needs to be removed from recursion stack before function ends
						rec_stk.remove(a__      )
						return False


if __name__ == "__main__":
							from doctest import testmod

							testmod()





