


"""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		(		lowercase=2			,       lowercase=True			,       lowercase=False						):







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

    # 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		(		lowercase=2			,       lowercase=True			,       lowercase=False						):







    """simple docstring"""
    a											=Accelerator(step_scheduler_with_optimizer=lowercase			,       split_batches=lowercase						)
    a											=torch.nn.Linear(2			,       4						)
    a											=torch.optim.AdamW(model.parameters()			,       lr=1.0						)
    a											=torch.optim.lr_scheduler.LambdaLR(lowercase			,       lr_lambda=lambda lowercase						: 1 - n / 10						)
    a						, a						, a											=accelerator.prepare(lowercase			,       lowercase			,       lowercase						)

    # 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 _A		(		lowercase = 2						):







    """simple docstring"""
    from transformers import get_linear_schedule_with_warmup

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

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

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






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     debug_launcher(partial(__A					,       num_processes=1		)					,       num_processes=1		)
     debug_launcher(partial(__A					,       num_processes=1					,       split_batches=__A		)					,       num_processes=1		)


 def  SCREAMING_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  SCREAMING_SNAKE_CASE				(						self		)   ->	Any:
     debug_launcher(partial(__A					,       num_processes=1					,       step_scheduler_with_optimizer=__A		)					,       num_processes=1		)


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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     AcceleratorState._reset_state(__A		)
     debug_launcher(__A		)
     debug_launcher(partial(__A					,       num_processes=1					,       split_batches=__A		)					,       num_processes=1		)


 def  SCREAMING_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  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     AcceleratorState._reset_state(__A		)
     debug_launcher(partial(__A					,       step_scheduler_with_optimizer=__A		)		)


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





 @require_huggingface_suite
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     AcceleratorState._reset_state(__A		)
     debug_launcher(partial(__A					,       num_processes=1		)		)
     debug_launcher(__A		)



"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.input_proj(__A		)

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask