






import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]     ,			__lowercase				:     List[Any]      )  -> Optional[int]:





    """simple docstring"""

    if isinstance(__lowercase     ,			(list, tuple)      ):
        return honor_type(__lowercase     ,			(move_to_device(__lowercase     ,			__lowercase      ) for t in state)      )
    elif isinstance(__lowercase     ,			__lowercase      ):
        return type(__lowercase      )({k: move_to_device(__lowercase     ,			__lowercase      ) for k, v in state.items()}      )
    elif isinstance(__lowercase     ,			torch.Tensor      ):
        return state.to(__lowercase      )
    return state






class  __lowercase (		torch.optim.Optimizer					):







     '''simple docstring'''




     def __init__(			self    :     Union[str, Any]	,		UpperCamelCase_    :     List[str]	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     Tuple=None			):


         """simple docstring"""



         __A             =		optimizer
         __A             =		scaler
         __A             =		AcceleratorState()
         __A             =		GradientState()
         __A             =		device_placement
         __A             =		False
         __A             =		None

         # Handle device placement
         if device_placement:
             __A             =		self.optimizer.state_dict()
             if self.accelerator_state.distributed_type == DistributedType.TPU:
                 xm.send_cpu_data_to_device(UpperCamelCase_	,		self.accelerator_state.device			)
             else:
                 __A             =		move_to_device(UpperCamelCase_	,		self.accelerator_state.device			)
             self.optimizer.load_state_dict(UpperCamelCase_			)




     @property
     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         return self.optimizer.state




     @state.setter
     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		state




     @property
     def 						lowerCAmelCase_       (			self    :     List[Any]			):


         """simple docstring"""



         return self.optimizer.param_groups




     @param_groups.setter
     def 						lowerCAmelCase_       (			self    :     List[Any]	,		UpperCamelCase_    :     Any			):


         """simple docstring"""



         __A             =		param_groups




     @property
     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         return self.optimizer.defaults




     @defaults.setter
     def 						lowerCAmelCase_       (			self    :     List[Any]	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		defaults




     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         self.optimizer.add_param_group(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     Dict			):


         """simple docstring"""



         if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
             xm.send_cpu_data_to_device(UpperCamelCase_	,		self.accelerator_state.device			)
         self.optimizer.load_state_dict(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         return self.optimizer.state_dict()




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     List[Any]=None			):


         """simple docstring"""



         if self.gradient_state.sync_gradients:
             __A             =		"""set_to_none""" in inspect.signature(self.optimizer.zero_grad			).parameters
             if accept_arg:
                 if set_to_none is None:
                     __A             =		False
                 self.optimizer.zero_grad(set_to_none=UpperCamelCase_			)
             else:
                 if set_to_none is not None:
                     raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""			)
                 self.optimizer.zero_grad()




     def 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     List[Any]=None			):


         """simple docstring"""



         if self.gradient_state.sync_gradients:
             if self.accelerator_state.distributed_type == DistributedType.TPU:
                 __A             =		{"""closure""": closure} if closure is not None else {}
                 xm.optimizer_step(self.optimizer	,		optimizer_args=UpperCamelCase_			)
             elif self.scaler is not None:
                 __A             =		False
                 if self._last_scale is None:
                     # `get_scale` is an async operation requiring full synchronization
                     # on CPU and GPUs before finishing. As a result, we store away
                     # the prior one to reduce the call overhead
                     __A             =		self.scaler.get_scale()
                     __A             =		True
                 self.scaler.step(self.optimizer	,		UpperCamelCase_			)
                 self.scaler.update()
                 __A             =		self.scaler.get_scale()
                 if not new_scale:
                     # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                     __A             =		scale_after < self._last_scale
                 __A             =		scale_after

             else:
                 self.optimizer.step(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     Any			):


         """simple docstring"""



         for param_group in self.optimizer.param_groups:
             __A             =		[parameters_map.get(UpperCamelCase_	,		UpperCamelCase_			) for p in param_group["""params"""]]




     @property
     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         warnings.warn(
             """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
             """`optimizer.step_was_skipped` instead."""	,		UpperCamelCase_	,		)
         return self._is_overflow




     @property
     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         return self._is_overflow




     def __getstate__(			self    :     Dict			):


         """simple docstring"""



         return self.__dict__.copy()





     def __setstate__(			self    :     str	,		UpperCamelCase_    :     str			):


         """simple docstring"""



         self.__dict__.update(UpperCamelCase_			)












import copy
from typing import Dict

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


__a :    str								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"semantic"




     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     List[Any]=10_000	,		UpperCamelCase_    :     int=True	,		UpperCamelCase_    :     Dict=768	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     List[Any]=False	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     Dict=10_048	,		UpperCamelCase_    :     Tuple=129_595	,		UpperCamelCase_    :     List[str]=129_599	,		UpperCamelCase_    :     Optional[int]=10_000	,		UpperCamelCase_    :     Dict=256	,		UpperCamelCase_    :     int=49.9	,		**UpperCamelCase_    :     Any	,		):


         """simple docstring"""



         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_	,		)

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


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"coarse_acoustics"




     def __init__(			self    :     int	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     int=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Union[str, Any]=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     Optional[Any]=True	,		UpperCamelCase_    :     str=12_048	,		UpperCamelCase_    :     int=75	,		UpperCamelCase_    :     Dict=2	,		UpperCamelCase_    :     List[str]=12_050	,		UpperCamelCase_    :     str=256	,		UpperCamelCase_    :     int = 630	,		UpperCamelCase_    :     int = 60	,		**UpperCamelCase_    :     Union[str, Any]	,		):


         """simple docstring"""



         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_	,		)

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


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"fine_acoustics"




     def __init__(			self    :     Any	,		UpperCamelCase_    :     Dict=0.5	,		UpperCamelCase_    :     List[Any]=512	,		UpperCamelCase_    :     int=1_024	,		UpperCamelCase_    :     Union[str, Any]=8	,		**UpperCamelCase_    :     int	,		):


         """simple docstring"""



         super().__init__(temperature=UpperCamelCase_			)

         __A             =		max_fine_history_length
         __A             =		max_fine_input_length
         __A             =		n_fine_codebooks


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"bark"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     List[str]=24_000	,		UpperCamelCase_    :     Union[str, Any]=1_024	,		**UpperCamelCase_    :     List[str]	,		):


         """simple docstring"""



         if semantic_config is None:
             __A             =		{}
             logger.info("""semantic_config is None. initializing the semantic model with default values."""			)

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

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

         __A             =		BarkSemanticGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkCoarseGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkFineGenerationConfig(**UpperCamelCase_			)

         __A             =		sample_rate
         __A             =		codebook_size




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     BarkSemanticGenerationConfig	,		UpperCamelCase_    :     BarkCoarseGenerationConfig	,		UpperCamelCase_    :     BarkFineGenerationConfig	,		**UpperCamelCase_    :     Tuple	,		):


         """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()	,		**UpperCamelCase_	,		)





     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)

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

         __A             =		self.__class__.model_type
         return output




