


'''simple docstring'''






import math

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


__A						:						Optional[int]  =  logging.get_logger(__name__)

__A						:						Any  =  {
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class     __UpperCamelCase						(    UpperCAmelCase__				):
							lowercase      :		int 		=	'data2vec-audio'





							def __init__(	self		:int	,_UpperCamelCase		:str=3_2	,_UpperCamelCase		:Dict=7_6_8	,_UpperCamelCase		:Tuple=1_2	,_UpperCamelCase		:Tuple=1_2	,_UpperCamelCase		:Dict=3_0_7_2	,_UpperCamelCase		:Dict="gelu"	,_UpperCamelCase		:Dict=0.1	,_UpperCamelCase		:Any=0.1	,_UpperCamelCase		:Dict=0.1	,_UpperCamelCase		:str=0.0	,_UpperCamelCase		:List[Any]=0.1	,_UpperCamelCase		:str=0.1	,_UpperCamelCase		:Tuple=0.02	,_UpperCamelCase		:str=1E-5	,_UpperCamelCase		:List[str]="gelu"	,_UpperCamelCase		:str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)	,_UpperCamelCase		:int=(5, 2, 2, 2, 2, 2, 2)	,_UpperCamelCase		:Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2)	,_UpperCamelCase		:int=False	,_UpperCamelCase		:Any=1_6	,_UpperCamelCase		:Dict=1_9	,_UpperCamelCase		:Dict=5	,_UpperCamelCase		:Dict=0.05	,_UpperCamelCase		:Dict=1_0	,_UpperCamelCase		:Any=2	,_UpperCamelCase		:Optional[int]=0.0	,_UpperCamelCase		:Optional[int]=1_0	,_UpperCamelCase		:List[Any]=0	,_UpperCamelCase		:List[str]="sum"	,_UpperCamelCase		:Dict=False	,_UpperCamelCase		:Tuple=False	,_UpperCamelCase		:Dict=2_5_6	,_UpperCamelCase		:Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)	,_UpperCamelCase		:List[str]=(5, 3, 3, 1, 1)	,_UpperCamelCase		:Any=(1, 2, 3, 1, 1)	,_UpperCamelCase		:List[str]=5_1_2	,_UpperCamelCase		:Optional[int]=0	,_UpperCamelCase		:str=1	,_UpperCamelCase		:List[str]=2	,_UpperCamelCase		:List[Any]=False	,_UpperCamelCase		:Tuple=3	,_UpperCamelCase		:Any=2	,_UpperCamelCase		:Dict=3	,_UpperCamelCase		:List[str]=None	,**_UpperCamelCase		:Tuple	,):
									super().__init__(**_UpperCAmelCase	,pad_token_id=_UpperCAmelCase	,bos_token_id=_UpperCAmelCase	,eos_token_id=_UpperCAmelCase      )
									snake_case_		:				Union[str, Any]      							=    hidden_size
									snake_case_		:				int      							=    feat_extract_activation
									snake_case_		:				List[str]      							=    list(_UpperCAmelCase      )
									snake_case_		:				Union[str, Any]      							=    list(_UpperCAmelCase      )
									snake_case_		:				Dict      							=    list(_UpperCAmelCase      )
									snake_case_		:				Union[str, Any]      							=    conv_bias
									snake_case_		:				Dict      							=    num_conv_pos_embeddings
									snake_case_		:				Tuple      							=    num_conv_pos_embedding_groups
									snake_case_		:				Tuple      							=    conv_pos_kernel_size
									snake_case_		:				Union[str, Any]      							=    len(self.conv_dim      )
									snake_case_		:				int      							=    num_hidden_layers
									snake_case_		:				str      							=    intermediate_size
									snake_case_		:				Tuple      							=    hidden_act
									snake_case_		:				Any      							=    num_attention_heads
									snake_case_		:				List[Any]      							=    hidden_dropout
									snake_case_		:				Optional[Any]      							=    attention_dropout
									snake_case_		:				int      							=    activation_dropout
									snake_case_		:				Optional[Any]      							=    feat_proj_dropout
									snake_case_		:				List[str]      							=    final_dropout
									snake_case_		:				Optional[int]      							=    layerdrop
									snake_case_		:				List[str]      							=    layer_norm_eps
									snake_case_		:				Optional[int]      							=    initializer_range
									snake_case_		:				List[Any]      							=    vocab_size
									snake_case_		:				Any      							=    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
									snake_case_		:				Optional[Any]      							=    mask_time_prob
									snake_case_		:				Any      							=    mask_time_length
									snake_case_		:				Union[str, Any]      							=    mask_time_min_masks
									snake_case_		:				Any      							=    mask_feature_prob
									snake_case_		:				int      							=    mask_feature_length
									snake_case_		:				List[Any]      							=    mask_feature_min_masks

									# ctc loss
									snake_case_		:				Dict      							=    ctc_loss_reduction
									snake_case_		:				Optional[Any]      							=    ctc_zero_infinity

									# adapter
									snake_case_		:				str      							=    add_adapter
									snake_case_		:				Optional[Any]      							=    adapter_kernel_size
									snake_case_		:				int      							=    adapter_stride
									snake_case_		:				List[Any]      							=    num_adapter_layers
									snake_case_		:				List[Any]      							=    output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									snake_case_		:				List[str]      							=    classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									snake_case_		:				List[Any]      							=    list(_UpperCAmelCase      )
									snake_case_		:				Union[str, Any]      							=    list(_UpperCAmelCase      )
									snake_case_		:				Any      							=    list(_UpperCAmelCase      )
									snake_case_		:				Union[str, Any]      							=    xvector_output_dim





							@property
							def 		a__ (	self		:Optional[int]      ):
									return math.prod(self.conv_stride      )



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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						"""ylacombe/bark-small"""
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						"""en_speaker_1"""
							lowercase__	     =						"""This is a test string"""
							lowercase__	     =						"""speaker_embeddings_path.json"""
							lowercase__	     =						"""speaker_embeddings"""

					def     lowerCamelCase__	(self       :		str ,   **_UpperCAmelCase       :		Optional[int]						)		->  str:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							shutil.rmtree(self.tmpdirname						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

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

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

					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						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 ,   )

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

							lowercase__	     =						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       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


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

							lowercase__	     =						35
							lowercase__	     =						2
							lowercase__	     =						8

							lowercase__	     =						{
							    """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
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)

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


					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							lowercase__	     =						processor(text=self.input_string						)

							lowercase__	     =						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()						)

