



'''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        snake_case_   (	lowerCAmelCase_=2			,  lowerCAmelCase_=True			,  lowerCAmelCase_=False						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_			,  split_batches=lowerCAmelCase_						)
       _UpperCAmelCase    :		Union[str, Any]     	=					torch.nn.Linear(2			,  4						)
       _UpperCAmelCase    :		Tuple     	=					torch.optim.AdamW(model.parameters()			,  lr=1.0						)
       _UpperCAmelCase    :		List[Any]     	=					torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase_			,  max_lr=0.0_1			,  steps_per_epoch=2			,  epochs=1						)
       _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		str     	=					accelerator.prepare(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)

       # 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        snake_case_   (	lowerCAmelCase_=2			,  lowerCAmelCase_=True			,  lowerCAmelCase_=False						)->						List[Any]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[Any]     	=					Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_			,  split_batches=lowerCAmelCase_						)
       _UpperCAmelCase    :		str     	=					torch.nn.Linear(2			,  4						)
       _UpperCAmelCase    :		List[str]     	=					torch.optim.AdamW(model.parameters()			,  lr=1.0						)
       _UpperCAmelCase    :		Union[str, Any]     	=					torch.optim.lr_scheduler.LambdaLR(lowerCAmelCase_			,  lr_lambda=lambda lowerCAmelCase_						: 1 - n / 10						)
       _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		int     	=					accelerator.prepare(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)

       # Optimizer has stepped
       _UpperCAmelCase    :		int     	=					False
       scheduler.step()
       _UpperCAmelCase    :		int     	=					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
       _UpperCAmelCase    :		Any     	=					True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              _UpperCAmelCase    :		Optional[int]     	=					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        snake_case_   (	lowerCAmelCase_ = 2						)->						Tuple:





       '''simple docstring'''


       from transformers import get_linear_schedule_with_warmup

       _UpperCAmelCase    :		Dict     	=					[1, 2, 4]
       for num_steps in steps:
              _UpperCAmelCase    :		Union[str, Any]     	=					GradientAccumulationPlugin(num_steps=lowerCAmelCase_			,  adjust_scheduler=num_steps > 1						)
              _UpperCAmelCase    :		Optional[int]     	=					Accelerator(gradient_accumulation_plugin=lowerCAmelCase_						)
              _UpperCAmelCase    :		Any     	=					torch.nn.Linear(2			,  4						)
              _UpperCAmelCase    :		Optional[Any]     	=					torch.optim.AdamW(model.parameters()			,  lr=1_0.0						)
              _UpperCAmelCase    :		Optional[int]     	=					get_linear_schedule_with_warmup(optimizer=lowerCAmelCase_			,  num_warmup_steps=0			,  num_training_steps=20						)

              _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		Tuple     	=					accelerator.prepare(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)

              for i in range(10 * num_steps						):
                     with accelerator.accumulate(lowerCAmelCase_						):
                            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 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  Union[str, Any]:
        debug_launcher(partial(a_    ,num_processes=1				)    ,num_processes=1				)
        debug_launcher(partial(a_    ,num_processes=1    ,split_batches=a_				)    ,num_processes=1				)







 def _snake_case					(				self				)						->  Tuple:
        debug_launcher(partial(a_    ,num_processes=1				)    ,num_processes=1				)
        debug_launcher(partial(a_    ,num_processes=1    ,split_batches=a_				)    ,num_processes=1				)







 def _snake_case					(				self				)						->  List[Any]:
        debug_launcher(partial(a_    ,num_processes=1    ,step_scheduler_with_optimizer=a_				)    ,num_processes=1				)







 def _snake_case					(				self				)						->  Optional[int]:
        debug_launcher(partial(a_    ,num_processes=1    ,step_scheduler_with_optimizer=a_				)    ,num_processes=1				)







 def _snake_case					(				self				)						->  List[Any]:
        AcceleratorState._reset_state(a_				)
        debug_launcher(a_				)
        debug_launcher(partial(a_    ,num_processes=1    ,split_batches=a_				)    ,num_processes=1				)







 def _snake_case					(				self				)						->  int:
        AcceleratorState._reset_state(a_				)
        debug_launcher(a_				)
        debug_launcher(partial(a_    ,num_processes=1    ,split_batches=a_				)    ,num_processes=1				)







 def _snake_case					(				self				)						->  Optional[int]:
        AcceleratorState._reset_state(a_				)
        debug_launcher(partial(a_    ,step_scheduler_with_optimizer=a_				)				)







 def _snake_case					(				self				)						->  Dict:
        AcceleratorState._reset_state(a_				)
        debug_launcher(partial(a_    ,step_scheduler_with_optimizer=a_				)				)







 @require_huggingface_suite
 def _snake_case					(				self				)						->  Any:
        AcceleratorState._reset_state(a_				)
        debug_launcher(partial(a_    ,num_processes=1				)				)
        debug_launcher(a_				)




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "facebook/hubert-base-ls960": "https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[str]          =	"hubert"

       def __init__(							self					:     List[str]			,			_lowerCAmelCase					:     Union[str, Any]=32			,			_lowerCAmelCase					:     Any=7_68			,			_lowerCAmelCase					:     Optional[Any]=12			,			_lowerCAmelCase					:     Optional[int]=12			,			_lowerCAmelCase					:     int=30_72			,			_lowerCAmelCase					:     Optional[Any]="gelu"			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Any=0.1			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Dict=0.0			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Optional[int]=0.1			,			_lowerCAmelCase					:     Dict=0.02			,			_lowerCAmelCase					:     Any=1e-5			,			_lowerCAmelCase					:     str="group"			,			_lowerCAmelCase					:     Any="gelu"			,			_lowerCAmelCase					:     List[str]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,			_lowerCAmelCase					:     Any=(5, 2, 2, 2, 2, 2, 2)			,			_lowerCAmelCase					:     Dict=(10, 3, 3, 3, 3, 2, 2)			,			_lowerCAmelCase					:     List[str]=False			,			_lowerCAmelCase					:     Optional[Any]=1_28			,			_lowerCAmelCase					:     int=16			,			_lowerCAmelCase					:     Tuple=False			,			_lowerCAmelCase					:     int=True			,			_lowerCAmelCase					:     int=0.05			,			_lowerCAmelCase					:     Any=10			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Dict=0.0			,			_lowerCAmelCase					:     Optional[int]=10			,			_lowerCAmelCase					:     Union[str, Any]=0			,			_lowerCAmelCase					:     int="sum"			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=False			,			_lowerCAmelCase					:     Optional[Any]=2_56			,			_lowerCAmelCase					:     Any=0			,			_lowerCAmelCase					:     List[Any]=1			,			_lowerCAmelCase					:     List[str]=2			,			**_lowerCAmelCase					:     Optional[int]			,			):
           super().__init__(**_lowerCAmelCase			,			pad_token_id=_lowerCAmelCase			,			bos_token_id=_lowerCAmelCase			,			eos_token_id=_lowerCAmelCase			)
           __snake_case       :				Optional[int] 			=    hidden_size
           __snake_case       :				Optional[Any] 			=    feat_extract_norm
           __snake_case       :				Optional[int] 			=    feat_extract_activation
           __snake_case       :				List[str] 			=    list(_lowerCAmelCase			)
           __snake_case       :				Any 			=    list(_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    list(_lowerCAmelCase			)
           __snake_case       :				Any 			=    conv_bias
           __snake_case       :				Tuple 			=    num_conv_pos_embeddings
           __snake_case       :				List[Any] 			=    num_conv_pos_embedding_groups
           __snake_case       :				Union[str, Any] 			=    len(self.conv_dim			)
           __snake_case       :				List[Any] 			=    num_hidden_layers
           __snake_case       :				Tuple 			=    intermediate_size
           __snake_case       :				Optional[int] 			=    hidden_act
           __snake_case       :				Union[str, Any] 			=    num_attention_heads
           __snake_case       :				str 			=    hidden_dropout
           __snake_case       :				Union[str, Any] 			=    attention_dropout
           __snake_case       :				Dict 			=    activation_dropout
           __snake_case       :				List[Any] 			=    feat_proj_layer_norm
           __snake_case       :				List[str] 			=    feat_proj_dropout
           __snake_case       :				List[Any] 			=    final_dropout
           __snake_case       :				str 			=    layerdrop
           __snake_case       :				List[str] 			=    layer_norm_eps
           __snake_case       :				Optional[Any] 			=    initializer_range
           __snake_case       :				Optional[int] 			=    vocab_size
           __snake_case       :				Dict 			=    do_stable_layer_norm
           __snake_case       :				Tuple 			=    use_weighted_layer_sum
           __snake_case       :				List[Any] 			=    classifier_proj_size

           if (
               (len(self.conv_stride			) != self.num_feat_extract_layers)
               or (len(self.conv_kernel			) != self.num_feat_extract_layers)
               or (len(self.conv_dim			) != self.num_feat_extract_layers)
           ):
               raise ValueError(
                   """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                   """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                   f''' {len(self.conv_dim			)}`, `len(config.conv_stride) = {len(self.conv_stride			)}`,'''
                   f''' `len(config.conv_kernel) = {len(self.conv_kernel			)}`.'''			)

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           __snake_case       :				Union[str, Any] 			=    apply_spec_augment
           __snake_case       :				List[str] 			=    mask_time_prob
           __snake_case       :				Dict 			=    mask_time_length
           __snake_case       :				int 			=    mask_time_min_masks
           __snake_case       :				List[str] 			=    mask_feature_prob
           __snake_case       :				Tuple 			=    mask_feature_length
           __snake_case       :				int 			=    mask_feature_min_masks

           # ctc loss
           __snake_case       :				Dict 			=    ctc_loss_reduction
           __snake_case       :				List[str] 			=    ctc_zero_infinity





       @property
       def 			snake_case__    (							self					:     Any			):
           return functools.reduce(operator.mul			,			self.conv_stride			,			1			)



