
import copy
from typing import Dict

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


lowercase__						:str					    =       logging.get_logger(__name__)







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			int			   ='''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__  ,)

												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 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Optional[Any]			   ='''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__  ,)

												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 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Any			   ='''fine_acoustics'''




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

												lowercase		      =		max_fine_history_length
												lowercase		      =		max_fine_input_length
												lowercase		      =		n_fine_codebooks







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			int			   ='''bark'''
							lowercase_			:			str			   =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:
																	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(**A__)
												lowercase		      =		BarkCoarseGenerationConfig(**A__)
												lowercase		      =		BarkFineGenerationConfig(**A__)

												lowercase		      =		sample_rate
												lowercase		      =		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):
												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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =['''image_processor''', '''tokenizer''']
							lowercase_			:			List[str]			   ='''AutoImageProcessor'''
							lowercase_			:			Optional[Any]			   ='''AutoTokenizer'''




							def __init__(							self  ,A__=None  ,A__=None  ,**A__):
												lowercase		      =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''  ,A__  ,)
																	lowercase		      =		kwargs.pop('''feature_extractor''')

												lowercase		      =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.''')
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.''')

												super().__init__(A__  ,A__)
												lowercase		      =		self.image_processor
												lowercase		      =		False




							def __call__(							self  ,*A__  ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*A__  ,**A__)

												lowercase		      =		kwargs.pop('''images'''  ,A__)
												lowercase		      =		kwargs.pop('''text'''  ,A__)
												if len(A__) > 0:
																	lowercase		      =		args[0]
																	lowercase		      =		args[1:]

												if images is None and text is None:
																	raise ValueError('''You need to specify either an `images` or `text` input to process.''')

												if images is not None:
																	lowercase		      =		self.image_processor(A__  ,*A__  ,**A__)
												if text is not None:
																	lowercase		      =		self.tokenizer(A__  ,**A__)

												if text is None:
																	return inputs
												elif images is None:
																	return encodings
												else:
																	lowercase		      =		encodings['''input_ids''']
																	return inputs




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.batch_decode(*A__  ,**A__)




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.decode(*A__  ,**A__)




							@contextmanager
							def 					A__ (							self):
												warnings.warn(
												    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
												    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
												    '''your images inputs, or in a separate call.''')
												lowercase		      =		True
												lowercase		      =		self.tokenizer
												yield
												lowercase		      =		self.image_processor
												lowercase		      =		False




							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,A__  ,)
												return self.image_processor_class





							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,A__  ,)
												return self.image_processor




