






"""simple docstring"""



import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any=2						,   __snake_case     :    Dict=True						,   __snake_case     :    str=False       ):





  '''simple docstring'''





  lowercase				       =			Accelerator(step_scheduler_with_optimizer=__snake_case						,   split_batches=__snake_case       )
  lowercase				       =			torch.nn.Linear(2						,   4       )
  lowercase				       =			torch.optim.AdamW(model.parameters()						,   lr=1.0       )
  lowercase				       =			torch.optim.lr_scheduler.OneCycleLR(__snake_case						,   max_lr=0.01						,   steps_per_epoch=2						,   epochs=1       )
  lowercase       ,	lowercase       ,	lowercase				       =			accelerator.prepare(__snake_case						,   __snake_case						,   __snake_case       )

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int=2						,   __snake_case     :    Optional[int]=True						,   __snake_case     :    List[str]=False       ):





  '''simple docstring'''





  lowercase				       =			Accelerator(step_scheduler_with_optimizer=__snake_case						,   split_batches=__snake_case       )
  lowercase				       =			torch.nn.Linear(2						,   4       )
  lowercase				       =			torch.optim.AdamW(model.parameters()						,   lr=1.0       )
  lowercase				       =			torch.optim.lr_scheduler.LambdaLR(__snake_case						,   lr_lambda=lambda __snake_case       : 1 - n / 10       )
  lowercase       ,	lowercase       ,	lowercase				       =			accelerator.prepare(__snake_case						,   __snake_case						,   __snake_case       )

  # Optimizer has stepped
  lowercase				       =			False
  scheduler.step()
  lowercase				       =			1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'

  # Optimizer has not stepped
  lowercase				       =			True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				       =			1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int = 2       ):





  '''simple docstring'''





  from transformers import get_linear_schedule_with_warmup

  lowercase				       =			[1, 2, 4]
  for num_steps in steps:
    lowercase				       =			GradientAccumulationPlugin(num_steps=__snake_case						,   adjust_scheduler=num_steps > 1       )
    lowercase				       =			Accelerator(gradient_accumulation_plugin=__snake_case       )
    lowercase				       =			torch.nn.Linear(2						,   4       )
    lowercase				       =			torch.optim.AdamW(model.parameters()						,   lr=10.0       )
    lowercase				       =			get_linear_schedule_with_warmup(optimizer=__snake_case						,   num_warmup_steps=0						,   num_training_steps=20       )

    lowercase       ,	lowercase       ,	lowercase				       =			accelerator.prepare(__snake_case						,   __snake_case						,   __snake_case       )

    for i in range(10 * num_steps       ):
      with accelerator.accumulate(__snake_case       ):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
    GradientState._reset_state()



@require_cpu
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1			)				,						num_processes=1			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1			)				,						num_processes=1			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						step_scheduler_with_optimizer=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						step_scheduler_with_optimizer=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						step_scheduler_with_optimizer=_lowerCamelCase			)			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						step_scheduler_with_optimizer=_lowerCamelCase			)			)







    @require_huggingface_suite
    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1			)			)
      debug_launcher(_lowerCamelCase			)










import copy
from typing import Dict

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


__lowerCamelCase						= logging.get_logger(__name__)

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''semantic'''




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

													_a  :    Optional[int]			=						text_encoding_offset
													_a  :    str			=						text_pad_token
													_a  :    List[str]			=						eos_token_id
													_a  :    Tuple			=						semantic_infer_token
													_a  :    int			=						semantic_vocab_size
													_a  :    List[str]			=						max_input_semantic_length
													_a  :    Union[str, Any]			=						semantic_rate_hz

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''coarse_acoustics'''




						def __init__(    self		,	lowercase=True		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=12_048		,	lowercase=75		,	lowercase=2		,	lowercase=12_050		,	lowercase=256		,	lowercase = 630		,	lowercase = 60		,	**lowercase		,	)			->						List[Any]:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	renormalize_logits=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    str			=						coarse_semantic_pad_token
													_a  :    Any			=						coarse_rate_hz
													_a  :    Dict			=						n_coarse_codebooks
													_a  :    str			=						coarse_infer_token
													_a  :    List[Any]			=						max_coarse_input_length
													_a  :    Union[str, Any]			=						max_coarse_history
													_a  :    Any			=						sliding_window_len

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''fine_acoustics'''




						def __init__(    self		,	lowercase=0.5		,	lowercase=512		,	lowercase=1_024		,	lowercase=8		,	**lowercase		,	)			->						str:
													super().__init__(temperature=lowercase       )

													_a  :    Tuple			=						max_fine_history_length
													_a  :    Tuple			=						max_fine_input_length
													_a  :    Any			=						n_fine_codebooks

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''bark'''
						lowercase									=  True




						def __init__(    self		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase=24_000		,	lowercase=1_024		,	**lowercase		,	)			->						List[str]:
													if semantic_config is None:
																				_a  :    str			=						{}
																				logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

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

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

													_a  :    List[Any]			=						BarkSemanticGenerationConfig(**lowercase       )
													_a  :    Union[str, Any]			=						BarkCoarseGenerationConfig(**lowercase       )
													_a  :    str			=						BarkFineGenerationConfig(**lowercase       )

													_a  :    List[str]			=						sample_rate
													_a  :    str			=						codebook_size




						@classmethod
						def      snake_case__(    cls		,	lowercase		,	lowercase		,	lowercase		,	**lowercase		,	)			->						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()		,	**lowercase		,	)







						def      snake_case__(    self       )			->						List[str]:
													_a  :    Any			=						copy.deepcopy(self.__dict__       )

													_a  :    str			=						self.semantic_config.to_dict()
													_a  :    Any			=						self.coarse_acoustics_config.to_dict()
													_a  :    Union[str, Any]			=						self.fine_acoustics_config.to_dict()

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