

import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

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







			__lowerCamelCase       				=				'MCTCTFeatureExtractor'
			__lowerCamelCase       				=				'AutoTokenizer'


			def __init__(			self					,					lowercase					,					lowercase				)     ->  str:
							'''simple docstring'''






							super().__init__(lowercase					,					lowercase				)
							A__						=	self.feature_extractor
							A__						=	False


			def __call__(			self					,					*lowercase					,					**lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






							if self._in_target_context_manager:
											return self.current_processor(*lowercase					,					**lowercase				)

							if "raw_speech" in kwargs:
											warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."				)
											A__						=	kwargs.pop("raw_speech"				)
							else:
											A__						=	kwargs.pop("audio"					,					lowercase				)
							A__						=	kwargs.pop("sampling_rate"					,					lowercase				)
							A__						=	kwargs.pop("text"					,					lowercase				)
							if len(lowercase				) > 0:
											A__						=	args[0]
											A__						=	args[1:]

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

							if audio is not None:
											A__						=	self.feature_extractor(lowercase					,					*lowercase					,					sampling_rate=lowercase					,					**lowercase				)
							if text is not None:
											A__						=	self.tokenizer(lowercase					,					**lowercase				)

							if text is None:
											return inputs
							elif audio is None:
											return encodings
							else:
											A__						=	encodings["input_ids"]
											return inputs


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






							return self.tokenizer.batch_decode(*lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






							if self._in_target_context_manager:
											return self.current_processor.pad(*lowercase					,					**lowercase				)

							A__						=	kwargs.pop("input_features"					,					lowercase				)
							A__						=	kwargs.pop("labels"					,					lowercase				)
							if len(lowercase				) > 0:
											A__						=	args[0]
											A__						=	args[1:]

							if input_features is not None:
											A__						=	self.feature_extractor.pad(lowercase					,					*lowercase					,					**lowercase				)
							if labels is not None:
											A__						=	self.tokenizer.pad(lowercase					,					**lowercase				)

							if labels is None:
											return input_features
							elif input_features is None:
											return labels
							else:
											A__						=	labels["input_ids"]
											return input_features


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  str:
							'''simple docstring'''






							return self.tokenizer.decode(*lowercase					,					**lowercase				)


			@contextmanager
			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your audio inputs, or in a separate call."				)
							A__						=	True
							A__						=	self.tokenizer
							yield
							A__						=	self.feature_extractor
							A__						=	False









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))
