
import copy
from typing import Dict

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


A		:   Tuple         =    logging.get_logger(__name__)



class       a_   ( _a						):
			a  : Any				      =       '''semantic'''






			def __init__(			self							, __UpperCamelCase=10_000							, __UpperCamelCase=True							, __UpperCamelCase=768							, __UpperCamelCase=False							, __UpperCamelCase=False							, __UpperCamelCase=False							, __UpperCamelCase=False							, __UpperCamelCase=0.7							, __UpperCamelCase=True							, __UpperCamelCase=10_048							, __UpperCamelCase=129_595							, __UpperCamelCase=129_599							, __UpperCamelCase=10_000							, __UpperCamelCase=256							, __UpperCamelCase=49.9							, **__UpperCamelCase							, ):
									super().__init__(
									    temperature=__UpperCamelCase							, do_sample=__UpperCamelCase							, eos_token_id=__UpperCamelCase							, renormalize_logits=__UpperCamelCase							, max_new_tokens=__UpperCamelCase							, output_scores=__UpperCamelCase							, return_dict_in_generate=__UpperCamelCase							, output_hidden_states=__UpperCamelCase							, output_attentions=__UpperCamelCase							, **__UpperCamelCase							, )

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







class       a_   ( _a						):
			a  : Dict				      =       '''coarse_acoustics'''






			def __init__(			self							, __UpperCamelCase=True							, __UpperCamelCase=False							, __UpperCamelCase=False							, __UpperCamelCase=False							, __UpperCamelCase=False							, __UpperCamelCase=0.7							, __UpperCamelCase=True							, __UpperCamelCase=12_048							, __UpperCamelCase=75							, __UpperCamelCase=2							, __UpperCamelCase=12_050							, __UpperCamelCase=256							, __UpperCamelCase = 630							, __UpperCamelCase = 60							, **__UpperCamelCase							, ):
									super().__init__(
									    temperature=__UpperCamelCase							, do_sample=__UpperCamelCase							, renormalize_logits=__UpperCamelCase							, output_scores=__UpperCamelCase							, return_dict_in_generate=__UpperCamelCase							, output_hidden_states=__UpperCamelCase							, output_attentions=__UpperCamelCase							, **__UpperCamelCase							, )

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







class       a_   ( _a						):
			a  : str				      =       '''fine_acoustics'''






			def __init__(			self							, __UpperCamelCase=0.5							, __UpperCamelCase=512							, __UpperCamelCase=1_024							, __UpperCamelCase=8							, **__UpperCamelCase							, ):
									super().__init__(temperature=__UpperCamelCase     )

									_lowercase									=				max_fine_history_length
									_lowercase									=				max_fine_input_length
									_lowercase									=				n_fine_codebooks







class       a_   ( _a						):
			a  : Optional[Any]				      =       '''bark'''
			a  : str				      =       True






			def __init__(			self							, __UpperCamelCase = None							, __UpperCamelCase = None							, __UpperCamelCase = None							, __UpperCamelCase=24_000							, __UpperCamelCase=1_024							, **__UpperCamelCase							, ):
									if semantic_config is None:
															_lowercase									=				{}
															logger.info("""semantic_config is None. initializing the semantic model with default values."""     )

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

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

									_lowercase									=				BarkSemanticGenerationConfig(**__UpperCamelCase     )
									_lowercase									=				BarkCoarseGenerationConfig(**__UpperCamelCase     )
									_lowercase									=				BarkFineGenerationConfig(**__UpperCamelCase     )

									_lowercase									=				sample_rate
									_lowercase									=				codebook_size






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






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				copy.deepcopy(self.__dict__     )

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

									_lowercase									=				self.__class__.model_type
									return output

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A		:   int         =    {'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   int         =    ['''ViltFeatureExtractor''']
		A		:   List[Any]         =    ['''ViltImageProcessor''']
		A		:   Dict         =    ['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Tuple         =    [
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		A		:   Dict         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)