






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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__A      =				logging.get_logger(__name__)




def      __A					(     _lowercase ):

					'''simple docstring'''







					if isinstance(__snake_case					,			(list, tuple) ) and isinstance(videos[0]					,			(list, tuple) ) and is_valid_image(videos[0][0] ):
										return videos

					elif isinstance(__snake_case					,			(list, tuple) ) and is_valid_image(videos[0] ):
										return [videos]

					elif is_valid_image(__snake_case ):
										return [[videos]]

					raise ValueError(f"""Could not make batched video from {videos}""" )





class 						SCREAMING_SNAKE_CASE				( _A					):





							"""simple docstring"""

							A_						  =					["pixel_values"]

							def __init__(  self:   List[str]		,     __A:   int = True		,     __A:   List[Any] = None		,     __A:   Dict = PILImageResampling.BILINEAR		,     __A:   List[str] = True		,     __A:   int = None		,     __A:   Optional[int] = True		,     __A:   Optional[Any] = 1 / 2_55		,     __A:   Optional[Any] = True		,     __A:   List[Any] = None		,     __A:   Optional[int] = None		,     **__A:   List[str]		,     )    ->				None:
												super().__init__(**__A   )
												_A								= size if size is not None else {"shortest_edge": 2_24}
												_A								= get_size_dict(__A		,     default_to_square=__A   )
												_A								= crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
												_A								= get_size_dict(__A		,     param_name='''crop_size'''   )

												_A								= do_resize
												_A								= size
												_A								= do_center_crop
												_A								= crop_size
												_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:   str		,     __A:   Dict		,     __A:   int		,     __A:   str = PILImageResampling.BILINEAR		,     __A:   Any = None		,     **__A:   List[Any]		,     )    ->				np.ndarray:
												_A								= get_size_dict(__A		,     default_to_square=__A   )
												if "shortest_edge" in size:
																	_A								= get_resize_output_image_size(__A		,     size['''shortest_edge''']		,     default_to_square=__A   )
												elif "height" in size and "width" in size:
																	_A								= (size["height"], size["width"])
												else:
																	raise ValueError(f"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""   )
												return resize(__A		,     size=__A		,     resample=__A		,     data_format=__A		,     **__A   )

							def       __A (  self:   Optional[Any]		,     __A:   Union[str, Any]		,     __A:   Dict		,     __A:   Tuple = None		,     **__A:   int		,     )    ->				np.ndarray:
												_A								= get_size_dict(__A   )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""Size must have \'height\' and \'width\' as keys. Got {size.keys()}"""   )
												return center_crop(__A		,     size=(size['''height'''], size['''width'''])		,     data_format=__A		,     **__A   )

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   List[str]		,     __A:   str = None		,     **__A:   List[str]		,     )    ->				Tuple:
												return rescale(__A		,     scale=__A		,     data_format=__A		,     **__A   )

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

							def       __A (  self:   List[Any]		,     __A:   Optional[int]		,     __A:   Tuple = None		,     __A:   List[Any] = None		,     __A:   int = None		,     __A:   Optional[Any] = None		,     __A:   Any = None		,     __A:   List[str] = None		,     __A:   Optional[int] = None		,     __A:   List[str] = None		,     __A:   Dict = None		,     __A:   Union[str, Any] = None		,     __A:   Optional[int] = ChannelDimension.FIRST		,     )    ->				np.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_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.
												_A								= to_numpy_array(__A   )

												if do_resize:
																	_A								= self.resize(image=__A		,     size=__A		,     resample=__A   )

												if do_center_crop:
																	_A								= self.center_crop(__A		,     size=__A   )

												if do_rescale:
																	_A								= self.rescale(image=__A		,     scale=__A   )

												if do_normalize:
																	_A								= self.normalize(image=__A		,     mean=__A		,     std=__A   )

												_A								= to_channel_dimension_format(__A		,     __A   )
												return image



							def       __A (  self:   Optional[Any]		,     __A:   List[Any]		,     __A:   Union[str, Any] = None		,     __A:   Optional[int] = None		,     __A:   Optional[int] = None		,     __A:   Optional[Any] = None		,     __A:   List[str] = None		,     __A:   Union[str, Any] = None		,     __A:   str = None		,     __A:   Union[str, Any] = None		,     __A:   Dict = None		,     __A:   Dict = None		,     __A:   List[Any] = None		,     __A:   Optional[int] = ChannelDimension.FIRST		,     **__A:   Union[str, Any]		,     )    ->				PIL.Image.Image:
												_A								= do_resize if do_resize is not None else self.do_resize
												_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								= 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								= size if size is not None else self.size
												_A								= get_size_dict(__A		,     default_to_square=__A   )
												_A								= crop_size if crop_size is not None else self.crop_size
												_A								= get_size_dict(__A		,     param_name='''crop_size'''   )

												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.'''   )

												_A								= make_batched(__A   )

												_A								= [
												    [
												        self._preprocess_image(
												            image=__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=__A		,     image_std=__A		,     data_format=__A		,     )
												        for img in video
												    ]
												    for video in videos
												]

												_A								= {"pixel_values": videos}
												return BatchFeature(data=__A		,     tensor_type=__A   )














def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= word.split()

					def justify(_lowercase					,			_lowercase					,			_lowercase ) -> str:
										_A								= max_width - width
										_A								= len(_lowercase )
										if len(_lowercase ) == 1:
															# if there is only word in line
															# just insert overall_spaces_count for the remainder of line
															return line[0] + " " * overall_spaces_count
										else:
															_A								= words_count - 1
															# num_spaces_between_words_list[i] : tells you to insert
															# num_spaces_between_words_list[i] spaces
															# after word on line[i]
															_A								= spaces_to_insert_between_words * [
															    overall_spaces_count // spaces_to_insert_between_words
															]
															_A								= (
															    overall_spaces_count % spaces_to_insert_between_words
															)
															# distribute spaces via round robin to the left words
															for i in range(_lowercase ):
																				num_spaces_between_words_list[i] += 1
															_A								= []
															for i in range(_lowercase ):
																				# add the word
																				aligned_words_list.append(line[i] )
																				# add the spaces to insert
																				aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
															# just add the last word to the sentence
															aligned_words_list.append(line[-1] )
															# join the aligned words list to form a justified line
															return "".join(_lowercase )

					_A								= []
					_A								= []
					_A								= 0
					for word in words:
										if width + len(_lowercase ) + len(_lowercase ) <= max_width:
															# keep adding words until we can fill out max_width
															# width = sum of length of all words (without overall_spaces_count)
															# len(word) = length of current word
															# len(line) = number of overall_spaces_count to insert between words
															line.append(_lowercase )
															width += len(_lowercase )
										else:
															# justify the line and add it to result
															answer.append(justify(_lowercase					,			_lowercase					,			_lowercase ) )
															# reset new line and new width
															_A							,_A								= [word], len(_lowercase )
					_A								= max_width - width - len(_lowercase )
					answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
					return answer


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

				testmod()



