




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							(     __UpperCamelCase=2	,      __UpperCamelCase=True	,      __UpperCamelCase=False)    ->  str:
     a											=   Accelerator(step_scheduler_with_optimizer=__UpperCamelCase	,      split_batches=__UpperCamelCase)
     a											=   torch.nn.Linear(2	,      4)
     a											=   torch.optim.AdamW(model.parameters()	,      lr=1.0)
     a											=   torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase	,      max_lr=0.01	,      steps_per_epoch=2	,      epochs=1)
     a						,   a						,   a											=   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       SCREAMING_SNAKE_CASE							(     __UpperCamelCase=2	,      __UpperCamelCase=True	,      __UpperCamelCase=False)    ->  Optional[int]:
     a											=   Accelerator(step_scheduler_with_optimizer=__UpperCamelCase	,      split_batches=__UpperCamelCase)
     a											=   torch.nn.Linear(2	,      4)
     a											=   torch.optim.AdamW(model.parameters()	,      lr=1.0)
     a											=   torch.optim.lr_scheduler.LambdaLR(__UpperCamelCase	,      lr_lambda=lambda __UpperCamelCase: 1 - n / 10)
     a						,   a						,   a											=   accelerator.prepare(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)

     # Optimizer has stepped
     a											=   False
     scheduler.step()
     a											=   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											=   True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          a											=   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							(     __UpperCamelCase = 2)    ->  str:
     from transformers import get_linear_schedule_with_warmup

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

          a						,   a						,   a											=   accelerator.prepare(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)

          for i in range(10 * num_steps):
               with accelerator.accumulate(__UpperCamelCase):
                    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     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					List[Any]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					List[Any]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



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







     @require_huggingface_suite
     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__   :    str 					=      {
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    int 					=      [
       "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RwkvForCausalLM",
       "RwkvModel",
       "RwkvPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   lowercase__   :    List[Any] 					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

