
'''simple docstring'''





def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				int												=       word.upper()
  UpperCAmelCase__			:				Dict												=       0
  UpperCAmelCase__			:				int												=       {}
  UpperCAmelCase__			:				str												=       []

  for letter in word:
    if letter not in letter_nums:
      UpperCAmelCase__			:				Dict												=       str(lowercase__		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(lowercase__		)


if __name__ == "__main__":
   import pprint
   import time

   SCREAMING_SNAKE_CASE    =					time.time()
   with open("""dictionary.txt""") as in_file:
      SCREAMING_SNAKE_CASE    =					in_file.read().splitlines()

   SCREAMING_SNAKE_CASE    =					{}
   for word in word_list:
      SCREAMING_SNAKE_CASE    =					get_word_pattern(word)
      if pattern in all_patterns:
         all_patterns[pattern].append(word)
      else:
         SCREAMING_SNAKE_CASE    =					[word]

   with open("""word_patterns.txt""", """w""") as out_file:
      out_file.write(pprint.pformat(all_patterns))

   SCREAMING_SNAKE_CASE    =					round(time.time() - start_time, 2)
   print(F'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
   # Done!  9,581 word patterns found in 0.58 seconds.





'''simple docstring'''





import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
      pass

if is_speech_available():
      from transformers import EncodecFeatureExtractor, MusicgenProcessor


SCREAMING_SNAKE_CASE    =					random.Random()







def snake_case_    (				lowercase__      ,					lowercase__=1.0      ,					lowercase__=None      ,					lowercase__=None		):
  if rng is None:
    UpperCAmelCase__			:				Optional[int]												=       global_rng

  UpperCAmelCase__			:				List[str]												=       []
  for batch_idx in range(shape[0]		):
    values.append([]		)
    for _ in range(shape[1]		):
      values[-1].append(rng.random() * scale		)

  return values






@require_torch
@require_sentencepiece
class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       "facebook/musicgen-small"
   UpperCAmelCase__			:				Union[str, Any]												=       tempfile.mkdtemp()







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							**snake_case__		:     Dict   ):



   '''simple docstring'''




   return TaTokenizer.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							**snake_case__		:     str   ):



   '''simple docstring'''




   return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   shutil.rmtree(self.tmpdirname   )







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_tokenizer()
   UpperCAmelCase__			:				Tuple												=       self.get_feature_extractor()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   processor.save_pretrained(self.tmpdirname   )
   UpperCAmelCase__			:				Dict												=       MusicgenProcessor.from_pretrained(self.tmpdirname   )

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

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







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=self.get_tokenizer() ,							feature_extractor=self.get_feature_extractor()   )
   processor.save_pretrained(self.tmpdirname   )

   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"   )
   UpperCAmelCase__			:				int												=       self.get_feature_extractor(do_normalize=snake_case__ ,							padding_value=1.0   )

   UpperCAmelCase__			:				Optional[Any]												=       MusicgenProcessor.from_pretrained(
       self.tmpdirname ,							bos_token="(BOS)" ,							eos_token="(EOS)" ,							do_normalize=snake_case__ ,							padding_value=1.0   )

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

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







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor()
   UpperCAmelCase__			:				int												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[str]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       floats_list((3, 10_00)   )

   UpperCAmelCase__			:				List[Any]												=       feature_extractor(snake_case__ ,							return_tensors="np"   )
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__ ,							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        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				Union[str, Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Optional[int]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       "This is a test string"

   UpperCAmelCase__			:				Dict												=       processor(text=snake_case__   )

   UpperCAmelCase__			:				Any												=       tokenizer(snake_case__   )

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







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[int]												=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   UpperCAmelCase__			:				Any												=       processor.batch_decode(sequences=snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.batch_decode(snake_case__   )

   self.assertListEqual(snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_feature_extractor()
   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Union[str, Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   self.assertListEqual(
       processor.model_input_names ,							feature_extractor.model_input_names ,							msg="`processor` and `feature_extractor` model input names do not match" ,							)







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor(padding_side="left"   )
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       [floats_list((1, x)   )[0] for x in range(5 ,							20 ,							5   )]
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__   ).padding_mask

   UpperCAmelCase__			:				Dict												=       np.asarray(floats_list((3, 20)   )   )[:, None, :]
   UpperCAmelCase__			:				Optional[Any]												=       processor.batch_decode(snake_case__ ,							padding_mask=snake_case__   )

   self.assertIsInstance(snake_case__ ,							snake_case__   )

   for audio in decoded_audios:
     self.assertIsInstance(snake_case__ ,							np.ndarray   )

   self.assertTrue(decoded_audios[0].shape == (1, 10)   )
   self.assertTrue(decoded_audios[1].shape == (1, 15)   )
   self.assertTrue(decoded_audios[2].shape == (1, 20)   )



