
"""simple docstring"""
import copy
from typing import Dict

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


lowercase		:						List[Any] =					logging.get_logger(__name__)




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''semantic'''






							def __init__(					self						:			Optional[int]						, snake_case__						:			Any=1_0_0_0_0						, snake_case__						:			Optional[int]=True						, snake_case__						:			List[str]=7_6_8						, snake_case__						:			Optional[int]=False						, snake_case__						:			List[Any]=False						, snake_case__						:			List[str]=False						, snake_case__						:			Any=False						, snake_case__						:			Dict=0.7						, snake_case__						:			Tuple=True						, snake_case__						:			str=1_0_0_4_8						, snake_case__						:			Optional[int]=1_2_9_5_9_5						, snake_case__						:			Optional[int]=1_2_9_5_9_9						, snake_case__						:			Optional[Any]=1_0_0_0_0						, snake_case__						:			Union[str, Any]=2_5_6						, snake_case__						:			Dict=49.9						, **snake_case__						:			Optional[Any]						, ):






												"""simple docstring"""



												super().__init__(
												    temperature=snake_case__						, do_sample=snake_case__						, eos_token_id=snake_case__						, renormalize_logits=snake_case__						, max_new_tokens=snake_case__						, output_scores=snake_case__						, return_dict_in_generate=snake_case__						, output_hidden_states=snake_case__						, output_attentions=snake_case__						, **snake_case__						, )

												__a:						Any		    =					text_encoding_offset
												__a:						Optional[int]		    =					text_pad_token
												__a:						List[Any]		    =					eos_token_id
												__a:						List[Any]		    =					semantic_infer_token
												__a:						Optional[int]		    =					semantic_vocab_size
												__a:						Optional[int]		    =					max_input_semantic_length
												__a:						int		    =					semantic_rate_hz






class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''coarse_acoustics'''






							def __init__(					self						:			List[str]						, snake_case__						:			Tuple=True						, snake_case__						:			Optional[Any]=False						, snake_case__						:			Dict=False						, snake_case__						:			str=False						, snake_case__						:			int=False						, snake_case__						:			List[Any]=0.7						, snake_case__						:			Optional[int]=True						, snake_case__						:			Optional[Any]=1_2_0_4_8						, snake_case__						:			List[str]=7_5						, snake_case__						:			Dict=2						, snake_case__						:			Union[str, Any]=1_2_0_5_0						, snake_case__						:			str=2_5_6						, snake_case__						:			int = 6_3_0						, snake_case__						:			int = 6_0						, **snake_case__						:			int						, ):






												"""simple docstring"""



												super().__init__(
												    temperature=snake_case__						, do_sample=snake_case__						, renormalize_logits=snake_case__						, output_scores=snake_case__						, return_dict_in_generate=snake_case__						, output_hidden_states=snake_case__						, output_attentions=snake_case__						, **snake_case__						, )

												__a:						int		    =					coarse_semantic_pad_token
												__a:						List[Any]		    =					coarse_rate_hz
												__a:						Optional[int]		    =					n_coarse_codebooks
												__a:						Tuple		    =					coarse_infer_token
												__a:						Any		    =					max_coarse_input_length
												__a:						Optional[int]		    =					max_coarse_history
												__a:						List[str]		    =					sliding_window_len






class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''fine_acoustics'''






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Optional[Any]=0.5						, snake_case__						:			Union[str, Any]=5_1_2						, snake_case__						:			Optional[Any]=1_0_2_4						, snake_case__						:			Optional[int]=8						, **snake_case__						:			Optional[Any]						, ):






												"""simple docstring"""



												super().__init__(temperature=snake_case__    )

												__a:						int		    =					max_fine_history_length
												__a:						int		    =					max_fine_input_length
												__a:						int		    =					n_fine_codebooks






class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''bark'''
							SCREAMING_SNAKE_CASE		     =						True






							def __init__(					self						:			Any						, snake_case__						:			Dict = None						, snake_case__						:			Dict = None						, snake_case__						:			Dict = None						, snake_case__						:			Any=2_4_0_0_0						, snake_case__						:			Union[str, Any]=1_0_2_4						, **snake_case__						:			Optional[int]						, ):






												"""simple docstring"""



												if semantic_config is None:
																	__a:						Optional[int]		    =					{}
																	logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

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

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

												__a:						str		    =					BarkSemanticGenerationConfig(**snake_case__    )
												__a:						Any		    =					BarkCoarseGenerationConfig(**snake_case__    )
												__a:						Optional[int]		    =					BarkFineGenerationConfig(**snake_case__    )

												__a:						str		    =					sample_rate
												__a:						Dict		    =					codebook_size






							@classmethod
							def 							__lowercase  (					cls						:			Dict						, snake_case__						:			BarkSemanticGenerationConfig						, snake_case__						:			BarkCoarseGenerationConfig						, snake_case__						:			BarkFineGenerationConfig						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



												return cls(
												    semantic_config=semantic_config.to_dict()						, coarse_acoustics_config=coarse_acoustics_config.to_dict()						, fine_acoustics_config=fine_acoustics_config.to_dict()						, **snake_case__						, )






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					copy.deepcopy(self.__dict__    )

												__a:						Optional[int]		    =					self.semantic_config.to_dict()
												__a:						List[str]		    =					self.coarse_acoustics_config.to_dict()
												__a:						List[str]		    =					self.fine_acoustics_config.to_dict()

												__a:						List[str]		    =					self.__class__.model_type
												return output


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
