


'''simple docstring'''

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 convert_to_rgb, 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


if is_vision_available():
							import PIL


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







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






		def __init__(   self		,						__A = True		,						__A = None		,						__A = PILImageResampling.BICUBIC		,						__A = True		,						__A = 1 / 255		,						__A = True		,						__A = None		,						__A = None		,						__A = True		,						**__A		,						) ->     str:
								super().__init__(**_UpperCAmelCase				)
								_lowerCAmelCase										=size if size is not None else {'''height''': 384, '''width''': 384}
								_lowerCAmelCase										=get_size_dict(_UpperCAmelCase		,						default_to_square=_UpperCAmelCase				)

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = PILImageResampling.BICUBIC		,						__A = None		,						**__A		,						) ->     Dict:
								_lowerCAmelCase										=get_size_dict(_UpperCAmelCase		,						default_to_square=_UpperCAmelCase				)
								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()}'''				)
								_lowerCAmelCase										=(size['''height'''], size['''width'''])
								return resize(_UpperCAmelCase		,						size=_UpperCAmelCase		,						resample=_UpperCAmelCase		,						data_format=_UpperCAmelCase		,						**_UpperCAmelCase				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     int:
								return rescale(_UpperCAmelCase		,						scale=_UpperCAmelCase		,						data_format=_UpperCAmelCase		,						**_UpperCAmelCase				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     str:
								return normalize(_UpperCAmelCase		,						mean=_UpperCAmelCase		,						std=_UpperCAmelCase		,						data_format=_UpperCAmelCase		,						**_UpperCAmelCase				)






		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 = ChannelDimension.FIRST		,						**__A		,						) ->     List[Any]:
								_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_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										=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_lowerCAmelCase										=size if size is not None else self.size
								_lowerCAmelCase										=get_size_dict(_UpperCAmelCase		,						default_to_square=_UpperCAmelCase				)

								_lowerCAmelCase										=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 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.'				)

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

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

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

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

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

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

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

								return encoded_outputs






'''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()





