





'''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"""







	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  __a			(  UpperCAmelCase=2							,      UpperCAmelCase=True							,      UpperCAmelCase=False     )   ->Tuple:



	"""simple docstring"""







	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  __a			(  UpperCAmelCase = 2     )   ->Optional[int]:



	"""simple docstring"""







	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 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		def 							A  (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 							A  (self    :   List[str]   ):
			debug_launcher(partial(__A   ,   num_processes=1   )   ,   num_processes=1   )
			debug_launcher(partial(__A   ,   num_processes=1   ,   split_batches=__A   )   ,   num_processes=1   )







		def 							A  (self    :   Tuple   ):
			debug_launcher(partial(__A   ,   num_processes=1   ,   step_scheduler_with_optimizer=__A   )   ,   num_processes=1   )







		def 							A  (self    :   Optional[Any]   ):
			debug_launcher(partial(__A   ,   num_processes=1   ,   step_scheduler_with_optimizer=__A   )   ,   num_processes=1   )







		def 							A  (self    :   Optional[int]   ):
			AcceleratorState._reset_state(__A   )
			debug_launcher(__A   )
			debug_launcher(partial(__A   ,   num_processes=1   ,   split_batches=__A   )   ,   num_processes=1   )







		def 							A  (self    :   int   ):
			AcceleratorState._reset_state(__A   )
			debug_launcher(__A   )
			debug_launcher(partial(__A   ,   num_processes=1   ,   split_batches=__A   )   ,   num_processes=1   )







		def 							A  (self    :   List[str]   ):
			AcceleratorState._reset_state(__A   )
			debug_launcher(partial(__A   ,   step_scheduler_with_optimizer=__A   )   )







		def 							A  (self    :   Union[str, Any]   ):
			AcceleratorState._reset_state(__A   )
			debug_launcher(partial(__A   ,   step_scheduler_with_optimizer=__A   )   )







		@require_huggingface_suite
		def 							A  (self    :   Optional[Any]   ):
			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