

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

   # 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_      (__A     :						List[Any]=2					,   __A     :						str=True					,   __A     :						List[Any]=False     )			->	Union[str, Any]:
   __lowerCAmelCase  :			int					    =						Accelerator(step_scheduler_with_optimizer=__A					,   split_batches=__A     )
   __lowerCAmelCase  :			str					    =						torch.nn.Linear(2					,   4     )
   __lowerCAmelCase  :			Optional[int]					    =						torch.optim.AdamW(model.parameters()					,   lr=1.0     )
   __lowerCAmelCase  :			Dict					    =						torch.optim.lr_scheduler.LambdaLR(__A					,   lr_lambda=lambda __A     : 1 - n / 1_0     )
   __lowerCAmelCase    ,__lowerCAmelCase    ,__lowerCAmelCase  :			Union[str, Any]					    =						accelerator.prepare(__A					,   __A					,   __A     )

   # Optimizer has stepped
   __lowerCAmelCase  :			List[str]					    =						False
   scheduler.step()
   __lowerCAmelCase  :			List[str]					    =						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
   __lowerCAmelCase  :			int					    =						True
   scheduler.step()
   if not step_scheduler_with_optimizer:
      __lowerCAmelCase  :			Union[str, Any]					    =						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 	snake_case_      (__A     :						int = 2     )			->	Tuple:
   from transformers import get_linear_schedule_with_warmup

   __lowerCAmelCase  :			Any					    =						[1, 2, 4]
   for num_steps in steps:
      __lowerCAmelCase  :			Dict					    =						GradientAccumulationPlugin(num_steps=__A					,   adjust_scheduler=num_steps > 1     )
      __lowerCAmelCase  :			str					    =						Accelerator(gradient_accumulation_plugin=__A     )
      __lowerCAmelCase  :			List[str]					    =						torch.nn.Linear(2					,   4     )
      __lowerCAmelCase  :			List[str]					    =						torch.optim.AdamW(model.parameters()					,   lr=10.0     )
      __lowerCAmelCase  :			Tuple					    =						get_linear_schedule_with_warmup(optimizer=__A					,   num_warmup_steps=0					,   num_training_steps=2_0     )

      __lowerCAmelCase    ,__lowerCAmelCase    ,__lowerCAmelCase  :			Optional[int]					    =						accelerator.prepare(__A					,   __A					,   __A     )

      for i in range(1_0 * num_steps     ):
         with accelerator.accumulate(__A     ):
            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 SCREAMING_SNAKE_CASE	(  unittest.TestCase ):
  """simple docstring"""






  def        SCREAMING_SNAKE_CASE    (			self :    str    )							->	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 :    Dict    )							->	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 :    Dict    )							->	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 :    Dict    )							->	str:






     """simple docstring"""







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







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






     """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 :    Optional[int]    )							->	int:






     """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 :    Dict    )							->	Dict:






     """simple docstring"""







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







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






     """simple docstring"""







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







  @require_huggingface_suite
  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Dict:






     """simple docstring"""







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





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


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )


