



'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
							import PIL

if is_torch_available():
							import torch


__lowerCAmelCase						      =       logging.get_logger(__name__)

class      __magic_name__						(   _lowerCAmelCase       ):
	lowerCAmelCase	:   Optional[int]           = ['pixel_values']







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

			_a					:				Tuple				=							do_resize
			_a					:				Dict				=							size
			_a					:				Dict				=							resample
			_a					:				List[str]				=							do_rescale
			_a					:				Tuple				=							rescale_factor
			_a					:				Tuple				=							do_center_crop
			_a					:				List[Any]				=							crop_size
			_a					:				Tuple				=							do_flip_channel_order







	def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Optional[int] = PIL.Image.BILINEAR     ,_UpperCAmelCase :	Optional[int] = None     ,**_UpperCAmelCase :	Any     ,):
			_a					:				Tuple				=							get_size_dict(SCREAMING_SNAKE_CASE_     ,default_to_square=SCREAMING_SNAKE_CASE_		)
			if "shortest_edge" not in size:
					raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""		)
			_a					:				Optional[Any]				=							get_resize_output_image_size(SCREAMING_SNAKE_CASE_     ,size=size['shortest_edge']     ,default_to_square=SCREAMING_SNAKE_CASE_		)
			return resize(SCREAMING_SNAKE_CASE_     ,size=SCREAMING_SNAKE_CASE_     ,resample=SCREAMING_SNAKE_CASE_     ,data_format=SCREAMING_SNAKE_CASE_     ,**SCREAMING_SNAKE_CASE_		)







	def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	int = None     ,**_UpperCAmelCase :	Optional[int]     ,):
			_a					:				Optional[Any]				=							get_size_dict(SCREAMING_SNAKE_CASE_		)
			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()}"""		)
			return center_crop(SCREAMING_SNAKE_CASE_     ,size=(size['height'], size['width'])     ,data_format=SCREAMING_SNAKE_CASE_     ,**SCREAMING_SNAKE_CASE_		)







	def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Optional[int] = None     ,**_UpperCAmelCase :	Any     ,):
			return rescale(SCREAMING_SNAKE_CASE_     ,scale=SCREAMING_SNAKE_CASE_     ,data_format=SCREAMING_SNAKE_CASE_     ,**SCREAMING_SNAKE_CASE_		)







	def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	str = None		):
			return flip_channel_order(SCREAMING_SNAKE_CASE_     ,data_format=SCREAMING_SNAKE_CASE_		)







	def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Dict = None     ,_UpperCAmelCase :	List[Any] = None     ,_UpperCAmelCase :	int = None     ,_UpperCAmelCase :	List[str] = None     ,_UpperCAmelCase :	Optional[int] = None     ,_UpperCAmelCase :	Optional[Any] = None     ,_UpperCAmelCase :	str = None     ,_UpperCAmelCase :	Dict = None     ,_UpperCAmelCase :	List[str] = None     ,_UpperCAmelCase :	int = ChannelDimension.FIRST     ,**_UpperCAmelCase :	Union[str, Any]     ,):
			_a					:				Union[str, Any]				=							do_resize if do_resize is not None else self.do_resize
			_a					:				int				=							resample if resample is not None else self.resample
			_a					:				List[Any]				=							do_rescale if do_rescale is not None else self.do_rescale
			_a					:				int				=							rescale_factor if rescale_factor is not None else self.rescale_factor
			_a					:				Optional[int]				=							do_center_crop if do_center_crop is not None else self.do_center_crop
			_a					:				Any				=							(
			    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
			)

			_a					:				Tuple				=							size if size is not None else self.size
			_a					:				str				=							get_size_dict(SCREAMING_SNAKE_CASE_     ,default_to_square=SCREAMING_SNAKE_CASE_		)
			_a					:				int				=							crop_size if crop_size is not None else self.crop_size
			_a					:				Dict				=							get_size_dict(SCREAMING_SNAKE_CASE_     ,param_name='crop_size'		)

			_a					:				Dict				=							make_list_of_images(SCREAMING_SNAKE_CASE_		)

			if not valid_images(SCREAMING_SNAKE_CASE_		):
					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_rescale and rescale_factor is None:
					raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

			if do_center_crop and crop_size is None:
					raise ValueError('Crop size must be specified if do_center_crop is True.'		)

			# All transformations expect numpy arrays.
			_a					:				Dict				=							[to_numpy_array(SCREAMING_SNAKE_CASE_		) for image in images]

			if do_resize:
					_a					:				List[Any]				=							[self.resize(image=SCREAMING_SNAKE_CASE_     ,size=SCREAMING_SNAKE_CASE_     ,resample=SCREAMING_SNAKE_CASE_		) for image in images]

			if do_center_crop:
					_a					:				str				=							[self.center_crop(image=SCREAMING_SNAKE_CASE_     ,size=SCREAMING_SNAKE_CASE_		) for image in images]

			if do_rescale:
					_a					:				Dict				=							[self.rescale(image=SCREAMING_SNAKE_CASE_     ,scale=SCREAMING_SNAKE_CASE_		) for image in images]

			# the pretrained checkpoints assume images are BGR, not RGB
			if do_flip_channel_order:
					_a					:				str				=							[self.flip_channel_order(image=SCREAMING_SNAKE_CASE_		) for image in images]

			_a					:				Dict				=							[to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_		) for image in images]

			_a					:				str				=							{"""pixel_values""": images}
			return BatchFeature(data=SCREAMING_SNAKE_CASE_     ,tensor_type=SCREAMING_SNAKE_CASE_		)







	def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Tuple = None		):
			_a					:				Optional[int]				=							outputs.logits

			# Resize logits and compute semantic segmentation maps
			if target_sizes is not None:
					if len(SCREAMING_SNAKE_CASE_		) != len(SCREAMING_SNAKE_CASE_		):
							raise ValueError(
							    'Make sure that you pass in as many target sizes as the batch dimension of the logits'		)

					if is_torch_tensor(SCREAMING_SNAKE_CASE_		):
							_a					:				Optional[int]				=							target_sizes.numpy()

					_a					:				Any				=							[]

					for idx in range(len(SCREAMING_SNAKE_CASE_		)		):
							_a					:				int				=							torch.nn.functional.interpolate(
							    logits[idx].unsqueeze(dim=0		)     ,size=target_sizes[idx]     ,mode='bilinear'     ,align_corners=SCREAMING_SNAKE_CASE_		)
							_a					:				Union[str, Any]				=							resized_logits[0].argmax(dim=0		)
							semantic_segmentation.append(SCREAMING_SNAKE_CASE_		)
			else:
					_a					:				List[str]				=							logits.argmax(dim=1		)
					_a					:				List[str]				=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

			return semantic_segmentation









'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


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

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

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




