

'''simple docstring'''





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      'ylacombe/bark-small'
							lowerCamelCase_  				=      tempfile.mkdtemp()
							lowerCamelCase_  				=      'en_speaker_1'
							lowerCamelCase_  				=      'This is a test string'
							lowerCamelCase_  				=      'speaker_embeddings_path.json'
							lowerCamelCase_  				=      'speaker_embeddings'
		def 			UpperCamelCase(					self   ,			**SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							return AutoTokenizer.from_pretrained(self.checkpoint   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							shutil.rmtree(self.tmpdirname		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.get_tokenizer()

							lowerCamelCase_  				=      BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_		)

							processor.save_pretrained(self.tmpdirname		)
							lowerCamelCase_  				=      BarkProcessor.from_pretrained(self.tmpdirname		)

							self.assertEqual(processor.tokenizer.get_vocab()   ,			tokenizer.get_vocab()		)
		@slow
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint   ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path   ,			)
							processor.save_pretrained(
							    self.tmpdirname   ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path   ,			speaker_embeddings_directory=self.speaker_embeddings_directory   ,			)

							lowerCamelCase_  				=      self.get_tokenizer(bos_token='(BOS)'   ,			eos_token='(EOS)'		)

							lowerCamelCase_  				=      BarkProcessor.from_pretrained(
							    self.tmpdirname   ,			self.speaker_embeddings_dict_path   ,			bos_token='(BOS)'   ,			eos_token='(EOS)'   ,			)

							self.assertEqual(processor.tokenizer.get_vocab()   ,			tokenizer_add_kwargs.get_vocab()		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint   ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path   ,			)

							lowerCamelCase_  				=      35
							lowerCamelCase_  				=      2
							lowerCamelCase_  				=      8

							lowerCamelCase_  				=      {
							    'semantic_prompt': np.ones(SCREAMING_SNAKE_CASE_		),
							    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)		),
							    'fine_prompt': np.ones((nb_codebooks_total, seq_len)		),
							}

							# test providing already loaded voice_preset
							lowerCamelCase_  				=      processor(text=self.input_string   ,			voice_preset=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      inputs['history_prompt']
							for key in voice_preset:
												self.assertListEqual(voice_preset[key].tolist()   ,			processed_voice_preset.get(SCREAMING_SNAKE_CASE_   ,			np.array([]		)		).tolist()		)

							# test loading voice preset from npz file
							lowerCamelCase_  				=      os.path.join(self.tmpdirname   ,			'file.npz'		)
							np.savez(SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      processor(text=self.input_string   ,			voice_preset=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      inputs['history_prompt']

							for key in voice_preset:
												self.assertListEqual(voice_preset[key].tolist()   ,			processed_voice_preset.get(SCREAMING_SNAKE_CASE_   ,			np.array([]		)		).tolist()		)

							# test loading voice preset from the hub
							lowerCamelCase_  				=      processor(text=self.input_string   ,			voice_preset=self.voice_preset		)






		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.get_tokenizer()

							lowerCamelCase_  				=      BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      processor(text=self.input_string		)

							lowerCamelCase_  				=      tokenizer(
							    self.input_string   ,			padding='max_length'   ,			max_length=256   ,			add_special_tokens=SCREAMING_SNAKE_CASE_   ,			return_attention_mask=SCREAMING_SNAKE_CASE_   ,			return_token_type_ids=SCREAMING_SNAKE_CASE_   ,			)

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







__a	 =      256
# Modulus to hash a string
__a	 =      100_0003
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   if p_len > t_len:
      return False

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  1

   # Calculating the hash of pattern and substring of text
   for i in range(lowerCAmelCase_ ):
      UpperCAmelCase      					=  (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      UpperCAmelCase      					=  (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      UpperCAmelCase      					=  (modulus_power * alphabet_size) % modulus

   for i in range(0	,    t_len - p_len + 1 ):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      UpperCAmelCase      					=  (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 	_UpperCamelCase					(    )    ->None:
   UpperCAmelCase      					=  """abc1abc12"""
   UpperCAmelCase      					=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
   UpperCAmelCase      					=  """alskfjaldsk23adsfabcabc"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ ) and not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 2)
   UpperCAmelCase      					=  """ABABX"""
   UpperCAmelCase      					=  """ABABZABABYABABX"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 3)
   UpperCAmelCase      					=  """AAAB"""
   UpperCAmelCase      					=  """ABAAAAAB"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 4)
   UpperCAmelCase      					=  """abcdabcy"""
   UpperCAmelCase      					=  """abcxabcdabxabcdabcdabcy"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 5)
   UpperCAmelCase      					=  """Lü"""
   UpperCAmelCase      					=  """Lüsai"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  """Lue"""
   assert not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   print("""Success.""" )


if __name__ == "__main__":
 test_rabin_karp()



