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__	(       __A=2 ,			__A=True ,			__A=False						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    Accelerator(step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE ,			split_batches=_SCREAMING_SNAKE_CASE						)
    _lowerCamelCase  :		Union[str, Any]			    =    torch.nn.Linear(2 ,			4						)
    _lowerCamelCase  :		Any			    =    torch.optim.AdamW(model.parameters() ,			lr=1.0						)
    _lowerCamelCase  :		Any			    =    torch.optim.lr_scheduler.OneCycleLR(_SCREAMING_SNAKE_CASE ,			max_lr=0.01 ,			steps_per_epoch=2 ,			epochs=1						)
    _lowerCamelCase  :		Any			    =    accelerator.prepare(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_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     A__	(       __A=2 ,			__A=True ,			__A=False						):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    Accelerator(step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE ,			split_batches=_SCREAMING_SNAKE_CASE						)
    _lowerCamelCase  :		Dict			    =    torch.nn.Linear(2 ,			4						)
    _lowerCamelCase  :		List[Any]			    =    torch.optim.AdamW(model.parameters() ,			lr=1.0						)
    _lowerCamelCase  :		Optional[int]			    =    torch.optim.lr_scheduler.LambdaLR(_SCREAMING_SNAKE_CASE ,			lr_lambda=lambda __A						: 1 - n / 10						)
    _lowerCamelCase  :		List[str]			    =    accelerator.prepare(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE						)

    # Optimizer has stepped
    _lowerCamelCase  :		Tuple			    =    False
    scheduler.step()
    _lowerCamelCase  :		Dict			    =    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
    _lowerCamelCase  :		List[str]			    =    True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        _lowerCamelCase  :		Tuple			    =    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     A__	(       __A = 2						):







    '''simple docstring'''
    from transformers import get_linear_schedule_with_warmup

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

        _lowerCamelCase  :		Tuple			    =    accelerator.prepare(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE						)

        for i in range(10 * num_steps						):
            with accelerator.accumulate(_SCREAMING_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 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Optional[int]:


          """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 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->List[str]:


          """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 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->List[Any]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Union[str, Any]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Optional[Any]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Tuple:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->int:


          """simple docstring"""


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




      @require_huggingface_suite
      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Tuple:


          """simple docstring"""


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





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      : Dict       ={'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[str]       =[
           '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''SEWDForCTC''',
           '''SEWDForSequenceClassification''',
           '''SEWDModel''',
           '''SEWDPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase      : str       =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



