

import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


lowerCamelCase   					=  logging.get_logger(__name__)





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""semantic"""




							def __init__(			self       ,    _A=1_0_0_0_0       ,    _A=True       ,    _A=7_6_8       ,    _A=False       ,    _A=False       ,    _A=False       ,    _A=False       ,    _A=0.7       ,    _A=True       ,    _A=1_0_0_4_8       ,    _A=1_2_9_5_9_5       ,    _A=1_2_9_5_9_9       ,    _A=1_0_0_0_0       ,    _A=2_5_6       ,    _A=49.9       ,    **_A       ,    ):
												super().__init__(
												    temperature=_A       ,    do_sample=_A       ,    eos_token_id=_A       ,    renormalize_logits=_A       ,    max_new_tokens=_A       ,    output_scores=_A       ,    return_dict_in_generate=_A       ,    output_hidden_states=_A       ,    output_attentions=_A       ,    **_A       ,    )

												__lowerCAmelCase       =				text_encoding_offset
												__lowerCAmelCase       =				text_pad_token
												__lowerCAmelCase       =				eos_token_id
												__lowerCAmelCase       =				semantic_infer_token
												__lowerCAmelCase       =				semantic_vocab_size
												__lowerCAmelCase       =				max_input_semantic_length
												__lowerCAmelCase       =				semantic_rate_hz





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""coarse_acoustics"""




							def __init__(			self       ,    _A=True       ,    _A=False       ,    _A=False       ,    _A=False       ,    _A=False       ,    _A=0.7       ,    _A=True       ,    _A=1_2_0_4_8       ,    _A=7_5       ,    _A=2       ,    _A=1_2_0_5_0       ,    _A=2_5_6       ,    _A = 6_3_0       ,    _A = 6_0       ,    **_A       ,    ):
												super().__init__(
												    temperature=_A       ,    do_sample=_A       ,    renormalize_logits=_A       ,    output_scores=_A       ,    return_dict_in_generate=_A       ,    output_hidden_states=_A       ,    output_attentions=_A       ,    **_A       ,    )

												__lowerCAmelCase       =				coarse_semantic_pad_token
												__lowerCAmelCase       =				coarse_rate_hz
												__lowerCAmelCase       =				n_coarse_codebooks
												__lowerCAmelCase       =				coarse_infer_token
												__lowerCAmelCase       =				max_coarse_input_length
												__lowerCAmelCase       =				max_coarse_history
												__lowerCAmelCase       =				sliding_window_len





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""fine_acoustics"""




							def __init__(			self       ,    _A=0.5       ,    _A=5_1_2       ,    _A=1_0_2_4       ,    _A=8       ,    **_A       ,    ):
												super().__init__(temperature=_A				)

												__lowerCAmelCase       =				max_fine_history_length
												__lowerCAmelCase       =				max_fine_input_length
												__lowerCAmelCase       =				n_fine_codebooks





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""bark"""
							__UpperCAmelCase      =True




							def __init__(			self       ,    _A = None       ,    _A = None       ,    _A = None       ,    _A=2_4_0_0_0       ,    _A=1_0_2_4       ,    **_A       ,    ):
												if semantic_config is None:
																	__lowerCAmelCase       =				{}
																	logger.info('semantic_config is None. initializing the semantic model with default values.'				)

												if coarse_acoustics_config is None:
																	__lowerCAmelCase       =				{}
																	logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'				)

												if fine_acoustics_config is None:
																	__lowerCAmelCase       =				{}
																	logger.info('fine_acoustics_config is None. initializing the fine model with default values.'				)

												__lowerCAmelCase       =				BarkSemanticGenerationConfig(**_A				)
												__lowerCAmelCase       =				BarkCoarseGenerationConfig(**_A				)
												__lowerCAmelCase       =				BarkFineGenerationConfig(**_A				)

												__lowerCAmelCase       =				sample_rate
												__lowerCAmelCase       =				codebook_size




							@classmethod
							def 							A__	(			cls       ,    _A       ,    _A       ,    _A       ,    **_A       ,    ):
												return cls(
												    semantic_config=semantic_config.to_dict()       ,    coarse_acoustics_config=coarse_acoustics_config.to_dict()       ,    fine_acoustics_config=fine_acoustics_config.to_dict()       ,    **_A       ,    )




							def 							A__	(			self				):
												__lowerCAmelCase       =				copy.deepcopy(self.__dict__				)

												__lowerCAmelCase       =				self.semantic_config.to_dict()
												__lowerCAmelCase       =				self.coarse_acoustics_config.to_dict()
												__lowerCAmelCase       =				self.fine_acoustics_config.to_dict()

												__lowerCAmelCase       =				self.__class__.model_type
												return output









from typing import Callable, Optional, Union

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


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""prophetnet"""
							__UpperCAmelCase      =["""past_key_values"""]
							__UpperCAmelCase      ={
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(			self       ,    _A = 0.1       ,    _A = "gelu"       ,    _A = 3_0_5_2_2       ,    _A = 1_0_2_4       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 0.1       ,    _A = 0.1       ,    _A = 5_1_2       ,    _A = 0.02       ,    _A = True       ,    _A = True       ,    _A = 0       ,    _A = 2       ,    _A = 3_2       ,    _A = 1_2_8       ,    _A = False       ,    _A = 0.0       ,    _A = True       ,    _A = 0       ,    _A = 1       ,    _A = 2       ,    **_A       ,    ):
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				hidden_size
												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				num_encoder_layers
												__lowerCAmelCase       =				num_encoder_attention_heads
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				num_decoder_layers
												__lowerCAmelCase       =				num_decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												# parameters for prophetnet
												__lowerCAmelCase       =				ngram
												__lowerCAmelCase       =				num_buckets
												__lowerCAmelCase       =				relative_max_distance
												__lowerCAmelCase       =				disable_ngram_loss
												__lowerCAmelCase       =				eps

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache

												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    is_encoder_decoder=_A       ,    add_cross_attention=_A       ,    decoder_start_token_id=_A       ,    **_A       ,    )




							@property
							def 							A__	(			self				):
												return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 							A__	(			self       ,    _A				):
												raise NotImplementedError(
												    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
												    ' `num_decoder_layers`.'				)






