

from __future__ import annotations

import collections
import pprint
from pathlib import Path






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				str:




	'''simple docstring'''




	return "".join(sorted(UpperCAmelCase_	)	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				list[str]:




	'''simple docstring'''




	return word_by_signature[signature(UpperCAmelCase_	)]


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))







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




