


'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      '''laion/clap-htsat-unfused'''
														lowerCAmelCase__			  =      tempfile.mkdtemp()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Optional[int]:
														return RobertaTokenizer.from_pretrained(self.checkpoint   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Optional[int]:
														return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														shutil.rmtree(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      self.get_tokenizer()
														lowerCAmelCase__			  =      self.get_feature_extractor()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														processor.save_pretrained(self.tmpdirname  )
														lowerCAmelCase__			  =      ClapProcessor.from_pretrained(self.tmpdirname  )

														self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer.get_vocab()  )
														self.assertIsInstance(processor.tokenizer   ,       lowerCamelCase_  )

														self.assertEqual(processor.feature_extractor.to_json_string()   ,       feature_extractor.to_json_string()  )
														self.assertIsInstance(processor.feature_extractor   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      ClapProcessor(tokenizer=self.get_tokenizer()   ,       feature_extractor=self.get_feature_extractor()  )
														processor.save_pretrained(self.tmpdirname  )

														lowerCAmelCase__			  =      self.get_tokenizer(bos_token='''(BOS)'''   ,       eos_token='''(EOS)'''  )
														lowerCAmelCase__			  =      self.get_feature_extractor(do_normalize=lowerCamelCase_   ,       padding_value=1.0  )

														lowerCAmelCase__			  =      ClapProcessor.from_pretrained(
														    self.tmpdirname   ,       bos_token='''(BOS)'''   ,       eos_token='''(EOS)'''   ,       do_normalize=lowerCamelCase_   ,       padding_value=1.0  )

														self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer_add_kwargs.get_vocab()  )
														self.assertIsInstance(processor.tokenizer   ,       lowerCamelCase_  )

														self.assertEqual(processor.feature_extractor.to_json_string()   ,       feature_extractor_add_kwargs.to_json_string()  )
														self.assertIsInstance(processor.feature_extractor   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														lowerCAmelCase__			  =      floats_list((3, 10_00)  )

														lowerCAmelCase__			  =      feature_extractor(lowerCamelCase_   ,       return_tensors='''np'''  )
														lowerCAmelCase__			  =      processor(audios=lowerCamelCase_   ,       return_tensors='''np'''  )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()   ,       input_processor[key].sum()   ,       delta=1e-2  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''This is a test string'''

														lowerCAmelCase__			  =      processor(text=lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer(lowerCamelCase_  )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]   ,       encoded_processor[key]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														lowerCAmelCase__			  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCAmelCase__			  =      processor.batch_decode(lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.batch_decode(lowerCamelCase_  )

														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														self.assertListEqual(
														    processor.model_input_names[2:]   ,       feature_extractor.model_input_names   ,       msg='''`processor` and `feature_extractor` model input names do not match'''   ,       )





'''simple docstring'''





from __future__ import annotations

import collections
import pprint
from pathlib import Path





def       __a      (  _UpperCamelCase:       str       )		->					str:

     """simple docstring"""




     return "".join(sorted(_UpperCamelCase       )       )





def       __a      (  _UpperCamelCase:       str       )		->					list[str]:

     """simple docstring"""




     return word_by_signature[signature(_UpperCamelCase       )]


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

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

if __name__ == "__main__":
   UpperCamelCase_		:     List[str]          =		{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))


