





"""simple docstring"""



import copy
from typing import Dict

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


a_				=    logging.get_logger(__name__)






class 	__snake_case      (	lowerCAmelCase_  ):





	"""simple docstring"""


	_lowerCamelCase				 =  'semantic'

	def __init__(					self      ,		__lowerCamelCase=1_0000      ,		__lowerCamelCase=True      ,		__lowerCamelCase=768      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=0.7      ,		__lowerCamelCase=True      ,		__lowerCamelCase=1_0048      ,		__lowerCamelCase=12_9595      ,		__lowerCamelCase=12_9599      ,		__lowerCamelCase=1_0000      ,		__lowerCamelCase=256      ,		__lowerCamelCase=49.9      ,		**__lowerCamelCase      ,		):



					'''simple docstring'''




					super().__init__(
					    temperature=lowerCamelCase_      ,		do_sample=lowerCamelCase_      ,		eos_token_id=lowerCamelCase_      ,		renormalize_logits=lowerCamelCase_      ,		max_new_tokens=lowerCamelCase_      ,		output_scores=lowerCamelCase_      ,		return_dict_in_generate=lowerCamelCase_      ,		output_hidden_states=lowerCamelCase_      ,		output_attentions=lowerCamelCase_      ,		**lowerCamelCase_      ,		)

					__A					:		Tuple						=  text_encoding_offset
					__A					:		Any						=  text_pad_token
					__A					:		Any						=  eos_token_id
					__A					:		Optional[int]						=  semantic_infer_token
					__A					:		List[Any]						=  semantic_vocab_size
					__A					:		Any						=  max_input_semantic_length
					__A					:		Dict						=  semantic_rate_hz






class 	__snake_case      (	lowerCAmelCase_  ):





	"""simple docstring"""


	_lowerCamelCase				 =  'coarse_acoustics'

	def __init__(					self      ,		__lowerCamelCase=True      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=0.7      ,		__lowerCamelCase=True      ,		__lowerCamelCase=1_2048      ,		__lowerCamelCase=75      ,		__lowerCamelCase=2      ,		__lowerCamelCase=1_2050      ,		__lowerCamelCase=256      ,		__lowerCamelCase = 630      ,		__lowerCamelCase = 60      ,		**__lowerCamelCase      ,		):



					'''simple docstring'''




					super().__init__(
					    temperature=lowerCamelCase_      ,		do_sample=lowerCamelCase_      ,		renormalize_logits=lowerCamelCase_      ,		output_scores=lowerCamelCase_      ,		return_dict_in_generate=lowerCamelCase_      ,		output_hidden_states=lowerCamelCase_      ,		output_attentions=lowerCamelCase_      ,		**lowerCamelCase_      ,		)

					__A					:		int						=  coarse_semantic_pad_token
					__A					:		List[str]						=  coarse_rate_hz
					__A					:		Tuple						=  n_coarse_codebooks
					__A					:		int						=  coarse_infer_token
					__A					:		Tuple						=  max_coarse_input_length
					__A					:		List[str]						=  max_coarse_history
					__A					:		Any						=  sliding_window_len






class 	__snake_case      (	lowerCAmelCase_  ):





	"""simple docstring"""


	_lowerCamelCase				 =  'fine_acoustics'

	def __init__(					self      ,		__lowerCamelCase=0.5      ,		__lowerCamelCase=512      ,		__lowerCamelCase=1024      ,		__lowerCamelCase=8      ,		**__lowerCamelCase      ,		):



					'''simple docstring'''




					super().__init__(temperature=lowerCamelCase_						)

					__A					:		List[str]						=  max_fine_history_length
					__A					:		Dict						=  max_fine_input_length
					__A					:		List[Any]						=  n_fine_codebooks






class 	__snake_case      (	lowerCAmelCase_  ):





	"""simple docstring"""


	_lowerCamelCase				 =  'bark'
	_lowerCamelCase				 =  True

	def __init__(					self      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase=2_4000      ,		__lowerCamelCase=1024      ,		**__lowerCamelCase      ,		):



					'''simple docstring'''




					if semantic_config is None:
									__A					:		List[Any]						=  {}
									logger.info('''semantic_config is None. initializing the semantic model with default values.'''						)

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

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

					__A					:		Optional[Any]						=  BarkSemanticGenerationConfig(**lowerCamelCase_						)
					__A					:		Optional[int]						=  BarkCoarseGenerationConfig(**lowerCamelCase_						)
					__A					:		Tuple						=  BarkFineGenerationConfig(**lowerCamelCase_						)

					__A					:		Optional[int]						=  sample_rate
					__A					:		int						=  codebook_size

	@classmethod
	def       UpperCamelCase__(					cls      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		**__lowerCamelCase      ,		):



					'''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()      ,		**lowerCamelCase_      ,		)







	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					__A					:		Optional[Any]						=  copy.deepcopy(self.__dict__						)

					__A					:		Tuple						=  self.semantic_config.to_dict()
					__A					:		Any						=  self.coarse_acoustics_config.to_dict()
					__A					:		Optional[Any]						=  self.fine_acoustics_config.to_dict()

					__A					:		int						=  self.__class__.model_type
					return output











"""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 	__lowercase  (     snake_case_						:						Dict=2       ,snake_case_						:						str=True       ,snake_case_						:						Tuple=False		)     ->Any:



    '''simple docstring'''







    __A					:		List[str]						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Dict						=  torch.nn.Linear(2       ,4		)
    __A					:		Tuple						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		int						=  torch.optim.lr_scheduler.OneCycleLR(snake_case_       ,max_lr=0.01       ,steps_per_epoch=2       ,epochs=1		)
    __A				, __A				, __A					:		Optional[int]						=  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 	__lowercase  (     snake_case_						:						str=2       ,snake_case_						:						Dict=True       ,snake_case_						:						Tuple=False		)     ->List[Any]:



    '''simple docstring'''







    __A					:		int						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Any						=  torch.nn.Linear(2       ,4		)
    __A					:		Optional[int]						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		Tuple						=  torch.optim.lr_scheduler.LambdaLR(snake_case_       ,lr_lambda=lambda snake_case_		: 1 - n / 10		)
    __A				, __A				, __A					:		Tuple						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    __A					:		List[Any]						=  False
    scheduler.step()
    __A					:		List[Any]						=  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
    __A					:		List[str]						=  True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __A					:		List[Any]						=  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 	__lowercase  (     snake_case_						:						int = 2		)     ->str:



    '''simple docstring'''







    from transformers import get_linear_schedule_with_warmup

    __A					:		Any						=  [1, 2, 4]
    for num_steps in steps:
        __A					:		Tuple						=  GradientAccumulationPlugin(num_steps=snake_case_       ,adjust_scheduler=num_steps > 1		)
        __A					:		Optional[Any]						=  Accelerator(gradient_accumulation_plugin=snake_case_		)
        __A					:		Optional[int]						=  torch.nn.Linear(2       ,4		)
        __A					:		Any						=  torch.optim.AdamW(model.parameters()       ,lr=10.0		)
        __A					:		Dict						=  get_linear_schedule_with_warmup(optimizer=snake_case_       ,num_warmup_steps=0       ,num_training_steps=20		)

        __A				, __A				, __A					:		Union[str, Any]						=  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 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     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						):



     '''simple docstring'''




     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						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)







 @require_huggingface_suite
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)						)
     debug_launcher(__lowerCamelCase						)




