"""simple docstring"""






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class        snake_case (				lowercase_   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				DebertaTokenizer
					snake_case__       					=				True
					snake_case__       					=				DebertaTokenizerFast







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									UpperCAmelCase__    =							[
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '[UNK]',
									]
									UpperCAmelCase__    =							dict(zip(lowerCamelCase__					,range(len(lowerCamelCase__    )    )    )    )
									UpperCAmelCase__    =							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									UpperCAmelCase__    =							{'unk_token': '[UNK]'}

									UpperCAmelCase__    =							os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file']    )
									UpperCAmelCase__    =							os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['merges_file']    )
									with open(self.vocab_file					,'w'					,encoding='utf-8'    ) as fp:
													fp.write(json.dumps(lowerCamelCase__    ) + '\n'    )
									with open(self.merges_file					,'w'					,encoding='utf-8'    ) as fp:
													fp.write('\n'.join(lowerCamelCase__    )    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,**lowerCamelCase__     :			Optional[int]    ):
									kwargs.update(self.special_tokens_map    )
									return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]    ):
									UpperCAmelCase__    =							'lower newer'
									UpperCAmelCase__    =							'lower newer'
									return input_text, output_text







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.get_tokenizer()
									UpperCAmelCase__    =							'lower newer'
									UpperCAmelCase__    =							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
									UpperCAmelCase__    =							tokenizer.tokenize(lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__					,lowerCamelCase__    )

									UpperCAmelCase__    =							tokens + [tokenizer.unk_token]
									UpperCAmelCase__    =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__    )					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							self.get_tokenizer()
									UpperCAmelCase__    =							tokenizer('Hello'					,'World'    )
									UpperCAmelCase__    =							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd['token_type_ids']					,lowerCamelCase__    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.tokenizer_class.from_pretrained('microsoft/deberta-base'    )

									UpperCAmelCase__    =							tokenizer.encode('sequence builders'					,add_special_tokens=lowerCamelCase__    )
									UpperCAmelCase__    =							tokenizer.encode('multi-sequence build'					,add_special_tokens=lowerCamelCase__    )

									UpperCAmelCase__    =							tokenizer.encode(
									    'sequence builders'					,add_special_tokens=lowerCamelCase__					,add_prefix_space=lowerCamelCase__    )
									UpperCAmelCase__    =							tokenizer.encode(
									    'sequence builders'					,'multi-sequence build'					,add_special_tokens=lowerCamelCase__					,add_prefix_space=lowerCamelCase__    )

									UpperCAmelCase__    =							tokenizer.build_inputs_with_special_tokens(lowerCamelCase__    )
									UpperCAmelCase__    =							tokenizer.build_inputs_with_special_tokens(lowerCamelCase__					,lowerCamelCase__    )

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode







					@slow
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							[self.tokenizer_class]
									if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class    )

									for tokenizer_class in tokenizer_classes:
													UpperCAmelCase__    =							tokenizer_class.from_pretrained('microsoft/deberta-base'    )

													UpperCAmelCase__    =							[
													    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
													    'ALBERT incorporates two parameter reduction techniques',
													    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
													    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
													    ' vocabulary embedding.',
													]

													UpperCAmelCase__    =							tokenizer(lowerCamelCase__					,padding=lowerCamelCase__    )
													UpperCAmelCase__    =							[tokenizer.decode(lowerCamelCase__					,skip_special_tokens=lowerCamelCase__    ) for seq in encoding['input_ids']]

													# fmt: off
													UpperCAmelCase__    =							{
													    'input_ids': [
													        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
													    ],
													    'token_type_ids': [
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
													    ],
													    'attention_mask': [
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
													    ]
													}
													# fmt: on

													UpperCAmelCase__    =							[
													    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
													    'ALBERT incorporates two parameter reduction techniques',
													    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
													    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
													    ' vocabulary embedding.',
													]

													self.assertDictEqual(encoding.data					,lowerCamelCase__    )

													for expected, decoded in zip(lowerCamelCase__					,lowerCamelCase__    ):
																	self.assertEqual(lowerCamelCase__					,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))



