'''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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				"ylacombe/bark-small"
       _snake_case     		=				tempfile.mkdtemp()
       _snake_case     		=				"en_speaker_1"
       _snake_case     		=				"This is a test string"
       _snake_case     		=				"speaker_embeddings_path.json"
       _snake_case     		=				"speaker_embeddings"

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoTokenizer.from_pretrained(self.checkpoint						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       shutil.rmtree(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BarkProcessor(tokenizer=lowerCamelCase	)

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

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

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				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						,   )

       _snake_case     		=				self.get_tokenizer(bos_token="(BOS)"						,   eos_token="(EOS)"	)

       _snake_case     		=				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	):
       _snake_case     		=				BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint						,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,   )

       _snake_case     		=				35
       _snake_case     		=				2
       _snake_case     		=				8

       _snake_case     		=				{
           "semantic_prompt": np.ones(lowerCamelCase	),
           "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)	),
           "fine_prompt": np.ones((nb_codebooks_total, seq_len)	),
       }

       # test providing already loaded voice_preset
       _snake_case     		=				processor(text=self.input_string						,   voice_preset=lowerCamelCase	)

       _snake_case     		=				inputs["history_prompt"]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()						,   processed_voice_preset.get(lowerCamelCase						,   np.array([]	)	).tolist()	)

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

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

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BarkProcessor(tokenizer=lowerCamelCase	)

       _snake_case     		=				processor(text=self.input_string	)

       _snake_case     		=				tokenizer(
           self.input_string						,   padding="max_length"						,   max_length=256						,   add_special_tokens=lowerCamelCase						,   return_attention_mask=lowerCamelCase						,   return_token_type_ids=lowerCamelCase						,   )

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









'''simple docstring'''



def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[0 for i in range(len(SCREAMING_SNAKE_CASE__       )       )]

  # initialize interval's left pointer and right pointer
  _snake_case		,		_snake_case     		=				0, 0

  for i in range(1					,	len(SCREAMING_SNAKE_CASE__       )       ):
    # case when current index is inside the interval
    if i <= right_pointer:
      _snake_case     		=				min(right_pointer - i + 1					,	z_result[i - left_pointer]       )
      _snake_case     		=				min_edge

    while go_next(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      _snake_case		,		_snake_case     		=				i, i + z_result[i] - 1

  return z_result




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return i + z_result[i] < len(SCREAMING_SNAKE_CASE__       ) and s[z_result[i]] == s[i + z_result[i]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  _snake_case     		=				z_function(pattern + input_str       )

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(SCREAMING_SNAKE_CASE__       ):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


