





"""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 							a__			(  _UpperCAmelCase=2	,     _UpperCAmelCase=True	,     _UpperCAmelCase=False	)    ->  Optional[Any]:

 '''simple docstring'''






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

 # 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 							a__			(  _UpperCAmelCase=2	,     _UpperCAmelCase=True	,     _UpperCAmelCase=False	)    ->  Union[str, Any]:

 '''simple docstring'''






 snake_case__    							=				Accelerator(step_scheduler_with_optimizer=_UpperCAmelCase	,     split_batches=_UpperCAmelCase	)
 snake_case__    							=				torch.nn.Linear(2	,     4	)
 snake_case__    							=				torch.optim.AdamW(model.parameters()	,     lr=1.0	)
 snake_case__    							=				torch.optim.lr_scheduler.LambdaLR(_UpperCAmelCase	,     lr_lambda=lambda _UpperCAmelCase	: 1 - n / 1_0	)
 snake_case__							,   snake_case__							,   snake_case__    							=				accelerator.prepare(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)

 # Optimizer has stepped
 snake_case__    							=				False
 scheduler.step()
 snake_case__    							=				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
 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
 snake_case__    							=				True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  snake_case__    							=				1 - 2 / 1_0
 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 							a__			(  _UpperCAmelCase = 2	)    ->  Tuple:

 '''simple docstring'''






 from transformers import get_linear_schedule_with_warmup

 snake_case__    							=				[1, 2, 4]
 for num_steps in steps:
  snake_case__    							=				GradientAccumulationPlugin(num_steps=_UpperCAmelCase	,     adjust_scheduler=num_steps > 1	)
  snake_case__    							=				Accelerator(gradient_accumulation_plugin=_UpperCAmelCase	)
  snake_case__    							=				torch.nn.Linear(2	,     4	)
  snake_case__    							=				torch.optim.AdamW(model.parameters()	,     lr=10.0	)
  snake_case__    							=				get_linear_schedule_with_warmup(optimizer=_UpperCAmelCase	,     num_warmup_steps=0	,     num_training_steps=2_0	)

  snake_case__							,   snake_case__							,   snake_case__    							=				accelerator.prepare(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)

  for i in range(1_0 * num_steps	):
   with accelerator.accumulate(_UpperCAmelCase	):
    optimizer.step()
    scheduler.step()

   if i == (1_0 * 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							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,					num_processes=1     )   ,					num_processes=1     )
        debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,					num_processes=1   ,					split_batches=__SCREAMING_SNAKE_CASE     )   ,					num_processes=1     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,					num_processes=1     )   ,					num_processes=1     )
        debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,					num_processes=1   ,					split_batches=__SCREAMING_SNAKE_CASE     )   ,					num_processes=1     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       @require_huggingface_suite
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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








"""simple docstring"""


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


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

