




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   ):

      """simple docstring"""

      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						'''ylacombe/bark-small'''
          __UpperCamelCase  		=						tempfile.mkdtemp()
          __UpperCamelCase  		=						'''en_speaker_1'''
          __UpperCamelCase  		=						'''This is a test string'''
          __UpperCamelCase  		=						'''speaker_embeddings_path.json'''
          __UpperCamelCase  		=						'''speaker_embeddings'''







      def       snake_case	(     self					: Dict ,       **snake_case					: Dict				):
          return AutoTokenizer.from_pretrained(self.checkpoint ,       **UpperCAmelCase_				)







      def       snake_case	(     self					: int				):
          shutil.rmtree(self.tmpdirname				)







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						self.get_tokenizer()

          __UpperCamelCase  		=						BarkProcessor(tokenizer=UpperCAmelCase_				)

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

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







      @slow
      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						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 ,       )

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

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

          __UpperCamelCase  		=						35
          __UpperCamelCase  		=						2
          __UpperCamelCase  		=						8

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

          # test providing already loaded voice_preset
          __UpperCamelCase  		=						processor(text=self.input_string ,       voice_preset=UpperCAmelCase_				)

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

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

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

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







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						self.get_tokenizer()

          __UpperCamelCase  		=						BarkProcessor(tokenizer=UpperCAmelCase_				)

          __UpperCamelCase  		=						processor(text=self.input_string				)

          __UpperCamelCase  		=						tokenizer(
              self.input_string ,       padding='''max_length''' ,       max_length=256 ,       add_special_tokens=UpperCAmelCase_ ,       return_attention_mask=UpperCAmelCase_ ,       return_token_type_ids=UpperCAmelCase_ ,       )

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








from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					"mctct"







      def __init__(     self					: List[Any] ,       snake_case					: Optional[Any]=8065 ,       snake_case					: Optional[int]=1536 ,       snake_case					: Any=36 ,       snake_case					: List[str]=6144 ,       snake_case					: Dict=4 ,       snake_case					: str=384 ,       snake_case					: List[str]=920 ,       snake_case					: Dict=1E-5 ,       snake_case					: Union[str, Any]=0.3 ,       snake_case					: Optional[Any]="relu" ,       snake_case					: str=0.02 ,       snake_case					: Optional[int]=0.3 ,       snake_case					: int=0.3 ,       snake_case					: Any=1 ,       snake_case					: int=0 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: List[Any]=1 ,       snake_case					: Dict=0.3 ,       snake_case					: int=1 ,       snake_case					: Optional[int]=(7,) ,       snake_case					: List[Any]=(3,) ,       snake_case					: Optional[int]=80 ,       snake_case					: List[str]=1 ,       snake_case					: int=None ,       snake_case					: List[str]="sum" ,       snake_case					: Tuple=False ,       **snake_case					: List[str] ,       ):
          super().__init__(**snake_case ,       pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case				)
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						attention_head_dim
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						layerdrop
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						pad_token_id
          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id
          __UpperCamelCase  		=						conv_glu_dim
          __UpperCamelCase  		=						conv_dropout
          __UpperCamelCase  		=						num_conv_layers
          __UpperCamelCase  		=						input_feat_per_channel
          __UpperCamelCase  		=						input_channels
          __UpperCamelCase  		=						conv_channels
          __UpperCamelCase  		=						ctc_loss_reduction
          __UpperCamelCase  		=						ctc_zero_infinity

          # prevents config testing fail with exporting to json
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)

          if len(self.conv_kernel				) != self.num_conv_layers:
              raise ValueError(
                  '''Configuration for convolutional module is incorrect. '''
                  '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
                  F"but is `len(config.conv_kernel) = {len(self.conv_kernel				)}`, "
                  F"`config.num_conv_layers = {self.num_conv_layers}`."				)






