


"""simple docstring"""






import functools
import operator

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


UpperCAmelCase    :     Dict   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Dict   						=		{
    'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							"""wav2vec2"""






					def __init__(       self    : Any ,       UpperCamelCase    : List[str]=32 ,       UpperCamelCase    : Tuple=768 ,       UpperCamelCase    : List[Any]=12 ,       UpperCamelCase    : Optional[Any]=12 ,       UpperCamelCase    : Union[str, Any]=3_072 ,       UpperCamelCase    : Optional[Any]="gelu" ,       UpperCamelCase    : Dict=0.1 ,       UpperCamelCase    : Dict=0.1 ,       UpperCamelCase    : Any=0.1 ,       UpperCamelCase    : Any=0.0 ,       UpperCamelCase    : Dict=0.0 ,       UpperCamelCase    : int=0.1 ,       UpperCamelCase    : Union[str, Any]=0.1 ,       UpperCamelCase    : Optional[Any]=0.02 ,       UpperCamelCase    : Any=1e-5 ,       UpperCamelCase    : Union[str, Any]="group" ,       UpperCamelCase    : Dict="gelu" ,       UpperCamelCase    : Tuple=(512, 512, 512, 512, 512, 512, 512) ,       UpperCamelCase    : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) ,       UpperCamelCase    : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) ,       UpperCamelCase    : int=False ,       UpperCamelCase    : int=128 ,       UpperCamelCase    : Tuple=16 ,       UpperCamelCase    : str=False ,       UpperCamelCase    : Optional[int]=True ,       UpperCamelCase    : Optional[int]=0.05 ,       UpperCamelCase    : List[str]=10 ,       UpperCamelCase    : List[str]=2 ,       UpperCamelCase    : List[str]=0.0 ,       UpperCamelCase    : Optional[int]=10 ,       UpperCamelCase    : Optional[int]=0 ,       UpperCamelCase    : Optional[Any]=320 ,       UpperCamelCase    : Optional[int]=2 ,       UpperCamelCase    : Optional[int]=0.1 ,       UpperCamelCase    : Optional[int]=100 ,       UpperCamelCase    : List[str]=256 ,       UpperCamelCase    : Optional[int]=256 ,       UpperCamelCase    : Union[str, Any]=0.1 ,       UpperCamelCase    : int="sum" ,       UpperCamelCase    : List[Any]=False ,       UpperCamelCase    : Dict=False ,       UpperCamelCase    : Dict=256 ,       UpperCamelCase    : Union[str, Any]=(512, 512, 512, 512, 1_500) ,       UpperCamelCase    : Optional[int]=(5, 3, 3, 1, 1) ,       UpperCamelCase    : Union[str, Any]=(1, 2, 3, 1, 1) ,       UpperCamelCase    : Optional[Any]=512 ,       UpperCamelCase    : Any=0 ,       UpperCamelCase    : Tuple=1 ,       UpperCamelCase    : Dict=2 ,       UpperCamelCase    : int=False ,       UpperCamelCase    : List[str]=3 ,       UpperCamelCase    : List[str]=2 ,       UpperCamelCase    : Any=3 ,       UpperCamelCase    : Dict=None ,       UpperCamelCase    : Optional[int]=None ,       **UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






												super().__init__(**UpperCamelCase ,       pad_token_id=UpperCamelCase ,       bos_token_id=UpperCamelCase ,       eos_token_id=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					hidden_size
												__UpperCAmelCase   :						Union[str, Any]      							=					feat_extract_norm
												__UpperCAmelCase   :						List[Any]      							=					feat_extract_activation
												__UpperCAmelCase   :						str      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					conv_bias
												__UpperCAmelCase   :						int      							=					num_conv_pos_embeddings
												__UpperCAmelCase   :						Any      							=					num_conv_pos_embedding_groups
												__UpperCAmelCase   :						Optional[Any]      							=					len(self.conv_dim  )
												__UpperCAmelCase   :						List[str]      							=					num_hidden_layers
												__UpperCAmelCase   :						List[Any]      							=					intermediate_size
												__UpperCAmelCase   :						Union[str, Any]      							=					hidden_act
												__UpperCAmelCase   :						str      							=					num_attention_heads
												__UpperCAmelCase   :						Optional[int]      							=					hidden_dropout
												__UpperCAmelCase   :						Dict      							=					attention_dropout
												__UpperCAmelCase   :						int      							=					activation_dropout
												__UpperCAmelCase   :						Any      							=					feat_proj_dropout
												__UpperCAmelCase   :						Tuple      							=					final_dropout
												__UpperCAmelCase   :						List[str]      							=					layerdrop
												__UpperCAmelCase   :						Optional[Any]      							=					layer_norm_eps
												__UpperCAmelCase   :						Tuple      							=					initializer_range
												__UpperCAmelCase   :						Tuple      							=					vocab_size
												__UpperCAmelCase   :						List[Any]      							=					do_stable_layer_norm
												__UpperCAmelCase   :						str      							=					use_weighted_layer_sum

												if (
												    (len(self.conv_stride  ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
												):
																			raise ValueError(
																			    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																			    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																			    f''' {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,'''
																			    f''' `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__UpperCAmelCase   :						List[Any]      							=					apply_spec_augment
												__UpperCAmelCase   :						Dict      							=					mask_time_prob
												__UpperCAmelCase   :						Dict      							=					mask_time_length
												__UpperCAmelCase   :						Tuple      							=					mask_time_min_masks
												__UpperCAmelCase   :						Optional[int]      							=					mask_feature_prob
												__UpperCAmelCase   :						str      							=					mask_feature_length
												__UpperCAmelCase   :						str      							=					mask_feature_min_masks

												# parameters for pretraining with codevector quantized representations
												__UpperCAmelCase   :						List[str]      							=					num_codevectors_per_group
												__UpperCAmelCase   :						Union[str, Any]      							=					num_codevector_groups
												__UpperCAmelCase   :						str      							=					contrastive_logits_temperature
												__UpperCAmelCase   :						Tuple      							=					feat_quantizer_dropout
												__UpperCAmelCase   :						Optional[Any]      							=					num_negatives
												__UpperCAmelCase   :						List[str]      							=					codevector_dim
												__UpperCAmelCase   :						Tuple      							=					proj_codevector_dim
												__UpperCAmelCase   :						Optional[int]      							=					diversity_loss_weight

												# ctc loss
												__UpperCAmelCase   :						Optional[Any]      							=					ctc_loss_reduction
												__UpperCAmelCase   :						Union[str, Any]      							=					ctc_zero_infinity

												# adapter
												__UpperCAmelCase   :						int      							=					add_adapter
												__UpperCAmelCase   :						Any      							=					adapter_kernel_size
												__UpperCAmelCase   :						str      							=					adapter_stride
												__UpperCAmelCase   :						List[str]      							=					num_adapter_layers
												__UpperCAmelCase   :						Tuple      							=					output_hidden_size or hidden_size
												__UpperCAmelCase   :						Union[str, Any]      							=					adapter_attn_dim

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												__UpperCAmelCase   :						Tuple      							=					classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												__UpperCAmelCase   :						int      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					xvector_output_dim






					@property
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												return functools.reduce(operator.mul ,       self.conv_stride ,       1  )




"""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				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					"""ylacombe/bark-small"""
												__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()
												__UpperCAmelCase   :						Optional[Any]      							=					"""en_speaker_1"""
												__UpperCAmelCase   :						Union[str, Any]      							=					"""This is a test string"""
												__UpperCAmelCase   :						Dict      							=					"""speaker_embeddings_path.json"""
												__UpperCAmelCase   :						Any      							=					"""speaker_embeddings"""






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												return AutoTokenizer.from_pretrained(self.checkpoint ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												processor.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(self.tmpdirname  )

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






					@slow
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					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   :						Tuple      							=					self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""  )

												__UpperCAmelCase   :						Any      							=					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 lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )

												__UpperCAmelCase   :						List[str]      							=					35
												__UpperCAmelCase   :						Tuple      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					8

												__UpperCAmelCase   :						Optional[Any]      							=					{
												    """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   :						Dict      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					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   :						Union[str, Any]      							=					os.path.join(self.tmpdirname ,       """file.npz"""  )
												np.savez(UpperCamelCase ,       **UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					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   :						Dict      							=					processor(text=self.input_string ,       voice_preset=self.voice_preset  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												__UpperCAmelCase   :						List[str]      							=					processor(text=self.input_string  )

												__UpperCAmelCase   :						Tuple      							=					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()  )
