'''simple docstring'''

import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


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

from .logging import get_logger
from .state import PartialState


lowercase_  		=			get_logger(__name__)



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__ = None      ,   ):


						'''simple docstring'''






						for i, state in enumerate(a__   ):
												_lowerCAmelCase					    =F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												save(a__      ,   a__   )
												logger.info(F'''Model weights saved in {output_model_file}'''   )
						# Optimizer states
						for i, opt in enumerate(a__   ):
												_lowerCAmelCase					    =opt.state_dict()
												_lowerCAmelCase					    =F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												save(a__      ,   a__   )
												logger.info(F'''Optimizer state saved in {output_optimizer_file}'''   )
						# Scheduler states
						for i, scheduler in enumerate(a__   ):
												_lowerCAmelCase					    =scheduler.state_dict()
												_lowerCAmelCase					    =F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												save(a__      ,   a__   )
												logger.info(F'''Scheduler state saved in {output_scheduler_file}'''   )
						# GradScaler state
						if scaler is not None:
												_lowerCAmelCase					    =scaler.state_dict()
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												torch.save(a__      ,   a__   )
												logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''   )
						# Random number generator states
						_lowerCAmelCase					    ={}
						_lowerCAmelCase					    =F'''{RNG_STATE_NAME}_{process_index}.pkl'''
						_lowerCAmelCase					    =random.getstate()
						_lowerCAmelCase					    =np.random.get_state()
						_lowerCAmelCase					    =torch.get_rng_state()
						if is_xpu_available():
												_lowerCAmelCase					    =torch.xpu.get_rng_state_all()
						else:
												_lowerCAmelCase					    =torch.cuda.get_rng_state_all()
						if is_tpu_available():
												_lowerCAmelCase					    =xm.get_rng_state()
						_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
						torch.save(a__      ,   a__   )
						logger.info(F'''Random states saved in {output_states_file}'''   )
						return output_dir



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__=None      ,   a__=None      ,   **a__      ,   ):


						'''simple docstring'''






						if map_location not in [None, "cpu", "on_device"]:
												raise TypeError(
												    'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'   )
						if map_location is None:
												_lowerCAmelCase					    ='cpu'
						elif map_location == "on_device":
												_lowerCAmelCase					    =PartialState().device
						# Model states
						for i, model in enumerate(a__   ):
												_lowerCAmelCase					    =F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												models[i].load_state_dict(torch.load(a__      ,   map_location=a__   )      ,   **a__   )
						logger.info('All model weights loaded successfully'   )

						# Optimizer states
						for i, opt in enumerate(a__   ):
												_lowerCAmelCase					    =F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												_lowerCAmelCase					    =torch.load(a__      ,   map_location=a__   )
												optimizers[i].load_state_dict(a__   )
						logger.info('All optimizer states loaded successfully'   )

						# Scheduler states
						for i, scheduler in enumerate(a__   ):
												_lowerCAmelCase					    =F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												scheduler.load_state_dict(torch.load(a__   )   )
						logger.info('All scheduler states loaded successfully'   )

						# GradScaler state
						if scaler is not None:
												_lowerCAmelCase					    =os.path.join(a__      ,   a__   )
												scaler.load_state_dict(torch.load(a__   )   )
												logger.info('GradScaler state loaded successfully'   )

						# Random states
						try:
												_lowerCAmelCase					    =torch.load(os.path.join(a__      ,   F'''{RNG_STATE_NAME}_{process_index}.pkl'''   )   )
												random.setstate(states['random_state']   )
												np.random.set_state(states['numpy_random_seed']   )
												torch.set_rng_state(states['torch_manual_seed']   )
												if is_xpu_available():
																		torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']   )
												else:
																		torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']   )
												if is_tpu_available():
																		xm.set_rng_state(states['xm_seed']   )
												logger.info('All random states loaded successfully'   )
						except Exception:
												logger.info('Could not load random states'   )



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__ = 0   ):


						'''simple docstring'''






						_lowerCAmelCase					    =Path(a__   ) / F'''custom_checkpoint_{index}.pkl'''
						logger.info(F'''Saving the state of {get_pretty_name(a__   )} to {save_location}'''   )
						torch.save(obj.state_dict()      ,   a__   )



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__ = 0   ):


						'''simple docstring'''






						_lowerCAmelCase					    =F'''{path}/custom_checkpoint_{index}.pkl'''
						logger.info(F'''Loading the state of {get_pretty_name(a__   )} from {load_location}'''   )
						obj.load_state_dict(torch.load(a__      ,   map_location='cpu'   )   )






'''simple docstring'''

import copy
from typing import Dict

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


lowercase_  		=			logging.get_logger(__name__)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'semantic'






		def __init__( self		,    __A=1_0000		,    __A=True		,    __A=768		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_0048		,    __A=12_9595		,    __A=12_9599		,    __A=1_0000		,    __A=256		,    __A=49.9		,    **__A		,    )			->  Any:
								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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'coarse_acoustics'






		def __init__( self		,    __A=True		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_2048		,    __A=75		,    __A=2		,    __A=1_2050		,    __A=256		,    __A = 630		,    __A = 60		,    **__A		,    )			->  Union[str, Any]:
								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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   str							=				'fine_acoustics'






		def __init__( self		,    __A=0.5		,    __A=512		,    __A=1024		,    __A=8		,    **__A		,    )			->  int:
								super().__init__(temperature=__A					)

								_lowerCAmelCase					    =max_fine_history_length
								_lowerCAmelCase					    =max_fine_input_length
								_lowerCAmelCase					    =n_fine_codebooks

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   int							=				'bark'
		lowercase :   Optional[Any]							=				True






		def __init__( self		,    __A = None		,    __A = None		,    __A = None		,    __A=2_4000		,    __A=1024		,    **__A		,    )			->  Optional[int]:
								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 		UpperCamelCase__							( cls		,    __A		,    __A		,    __A		,    **__A		,    )			->  List[Any]:
								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 		UpperCamelCase__							( self					)			->  Optional[int]:
								_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





