





'''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    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _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 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							List[str]:
     return AutoTokenizer.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




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




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      self.get_tokenizer()

     _lowerCAmelCase				=      BarkProcessor(tokenizer=_lowerCAmelCase		)

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

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




 @slow
 def 							_snake_case   (	self		)    ->							Tuple:
     _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 							_snake_case   (	self		)    ->							Optional[Any]:
     _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(_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
     _lowerCAmelCase				=      processor(text=self.input_string			,     voice_preset=_lowerCAmelCase		)

     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      os.path.join(self.tmpdirname			,     "file.npz"		)
     np.savez(_lowerCAmelCase			,     **_lowerCAmelCase		)
     _lowerCAmelCase				=      processor(text=self.input_string			,     voice_preset=_lowerCAmelCase		)
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      processor(text=self.input_string			,     voice_preset=self.voice_preset		)





 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.get_tokenizer()

     _lowerCAmelCase				=      BarkProcessor(tokenizer=_lowerCAmelCase		)

     _lowerCAmelCase				=      processor(text=self.input_string		)

     _lowerCAmelCase				=      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'''


from ...configuration_utils import PretrainedConfig

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"bert-generation"




 def __init__(	self			,     _lowerCAmelCase=50358			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=24			,     _lowerCAmelCase=16			,     _lowerCAmelCase=4096			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     **_lowerCAmelCase			,     )    ->							Tuple:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache


