

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCAmelCase__		      =       logging.get_logger(__name__)

class     a__  (    snake_case__	):
			"""simple docstring"""







			__lowerCamelCase       				=				['pixel_values']


			def __init__(			self					,					lowercase = True					,					lowercase = 1 / 255					,					lowercase = True					,					lowercase = 8					,					**lowercase					,					)     ->  List[str]:
							'''simple docstring'''






							super().__init__(**lowercase				)

							A__						=	do_rescale
							A__						=	rescale_factor
							A__						=	do_pad
							A__						=	pad_size


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = None					,					**lowercase				)     ->  int:
							'''simple docstring'''






							return rescale(lowercase					,					scale=lowercase					,					data_format=lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = None				)     ->  Any:
							'''simple docstring'''






							A__			, A__						=	get_image_size(lowercase				)
							A__						=	(old_height // size + 1) * size - old_height
							A__						=	(old_width // size + 1) * size - old_width

							return pad(lowercase					,					((0, pad_height), (0, pad_width))					,					mode="symmetric"					,					data_format=lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = ChannelDimension.FIRST					,					**lowercase					,					)     ->  Optional[int]:
							'''simple docstring'''






							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_pad if do_pad is not None else self.do_pad
							A__						=	pad_size if pad_size is not None else self.pad_size

							A__						=	make_list_of_images(lowercase				)

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

							if do_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."				)

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

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

							if do_pad:
											A__						=	[self.pad(lowercase					,					size=lowercase				) for image in images]

							A__						=	[to_channel_dimension_format(lowercase					,					lowercase				) for image in images]

							A__						=	{"pixel_values": images}
							return BatchFeature(data=lowercase					,					tensor_type=lowercase				)









from __future__ import annotations

import collections
import pprint
from pathlib import Path


def __A    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						return "".join(sorted(__lowerCAmelCase	)	)


def __A    (      __lowerCAmelCase	)->  list[str]:





						"""simple docstring"""



						return word_by_signature[signature(__lowerCAmelCase	)]


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

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

if __name__ == "__main__":
		_a      	=  {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))
