"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
						import PIL

lowerCAmelCase__						:						str		  =			logging.get_logger(__name__)



def 						a_		(      lowerCamelCase ):
				if isinstance(lowerCamelCase      ,    (list, tuple) ) and isinstance(videos[0]      ,    (list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

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

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

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







class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["pixel_values"]







					def __init__(       self     :			Any					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Union[int, float] = 1 / 255					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,**lowerCamelCase__     :			int					,):
									super().__init__(**lowerCamelCase__    )
									UpperCAmelCase__    =							size if size is not None else {'shortest_edge': 256}
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__    )
									UpperCAmelCase__    =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,param_name='crop_size'    )

									UpperCAmelCase__    =							do_resize
									UpperCAmelCase__    =							size
									UpperCAmelCase__    =							do_center_crop
									UpperCAmelCase__    =							crop_size
									UpperCAmelCase__    =							resample
									UpperCAmelCase__    =							do_rescale
									UpperCAmelCase__    =							rescale_factor
									UpperCAmelCase__    =							offset
									UpperCAmelCase__    =							do_normalize
									UpperCAmelCase__    =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCAmelCase__    =							image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Optional[Any]					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__    )
									if "shortest_edge" in size:
													UpperCAmelCase__    =							get_resize_output_image_size(lowerCamelCase__					,size['shortest_edge']					,default_to_square=lowerCamelCase__    )
									elif "height" in size and "width" in size:
													UpperCAmelCase__    =							(size['height'], size['width'])
									else:
													raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''    )
									return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Optional[Any]					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__    )
									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(lowerCamelCase__					,size=(size['height'], size['width'])					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Union[int, float]					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			List[str]					,):
									UpperCAmelCase__    =							image.astype(np.floataa    )
									if offset:
													UpperCAmelCase__    =							image - (scale / 2)
									return rescale(lowerCamelCase__					,scale=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







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







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			ImageInput					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			float = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[ChannelDimension] = ChannelDimension.FIRST					,):
									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.'    )

									if offset and not do_rescale:
													raise ValueError('For offset, do_rescale must also be set to True.'    )

									# All transformations expect numpy arrays.
									UpperCAmelCase__    =							to_numpy_array(lowerCamelCase__    )

									if do_resize:
													UpperCAmelCase__    =							self.resize(image=lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__    )

									if do_center_crop:
													UpperCAmelCase__    =							self.center_crop(lowerCamelCase__					,size=lowerCamelCase__    )

									if do_rescale:
													UpperCAmelCase__    =							self.rescale(image=lowerCamelCase__					,scale=lowerCamelCase__					,offset=lowerCamelCase__    )

									if do_normalize:
													UpperCAmelCase__    =							self.normalize(image=lowerCamelCase__					,mean=lowerCamelCase__					,std=lowerCamelCase__    )

									UpperCAmelCase__    =							to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__    )
									return image







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			ImageInput					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			float = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[str, TensorType]] = None					,lowerCamelCase__     :			ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__     :			Dict					,):
									UpperCAmelCase__    =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__    =							resample if resample is not None else self.resample
									UpperCAmelCase__    =							do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__    =							do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__    =							rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__    =							offset if offset is not None else self.offset
									UpperCAmelCase__    =							do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__    =							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__    =							image_std if image_std is not None else self.image_std

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

									if not valid_images(lowerCamelCase__    ):
													raise ValueError(
													    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
													    'torch.Tensor, tf.Tensor or jax.ndarray.'    )

									UpperCAmelCase__    =							make_batched(lowerCamelCase__    )

									UpperCAmelCase__    =							[
									    [
									        self._preprocess_image(
									            image=lowerCamelCase__					,do_resize=lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,do_center_crop=lowerCamelCase__					,crop_size=lowerCamelCase__					,do_rescale=lowerCamelCase__					,rescale_factor=lowerCamelCase__					,offset=lowerCamelCase__					,do_normalize=lowerCamelCase__					,image_mean=lowerCamelCase__					,image_std=lowerCamelCase__					,data_format=lowerCamelCase__					,)
									        for img in video
									    ]
									    for video in videos
									]

									UpperCAmelCase__    =							{'pixel_values': videos}
									return BatchFeature(data=lowerCamelCase__					,tensor_type=lowerCamelCase__    )




"""simple docstring"""






from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 						a_		(      lowerCamelCase ):
				return "".join(sorted(lowerCamelCase ) )



def 						a_		(      lowerCamelCase ):
				return word_by_signature[signature(lowerCamelCase )]


lowerCAmelCase__						:						str		  =			Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
lowerCAmelCase__						:						str		  =			sorted({word.strip().lower() for word in data.splitlines()})

lowerCAmelCase__						:						Optional[Any]		  =			collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



