



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

							# 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     __lowerCamelCase							(   __magic_name__						:	str=2  ,			__magic_name__						:	Optional[int]=True  ,			__magic_name__						:	Any=False    ):
							a__:							Any									=Accelerator(step_scheduler_with_optimizer=__magic_name__  ,			split_batches=__magic_name__    )
							a__:							List[Any]									=torch.nn.Linear(2  ,			4    )
							a__:							Any									=torch.optim.AdamW(model.parameters()  ,			lr=1.0    )
							a__:							Any									=torch.optim.lr_scheduler.LambdaLR(__magic_name__  ,			lr_lambda=lambda __magic_name__    : 1 - n / 10    )
							a__				,      a__				,      a__:							Optional[int]									=accelerator.prepare(__magic_name__  ,			__magic_name__  ,			__magic_name__    )

							# Optimizer has stepped
							a__:							Union[str, Any]									=False
							scheduler.step()
							a__:							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
							a__:							Any									=True
							scheduler.step()
							if not step_scheduler_with_optimizer:
														a__:							int									=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     __lowerCamelCase							(   __magic_name__						:	int = 2    ):
							from transformers import get_linear_schedule_with_warmup

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

														a__				,      a__				,      a__:							str									=accelerator.prepare(__magic_name__  ,			__magic_name__  ,			__magic_name__    )

														for i in range(10 * num_steps    ):
																					with accelerator.accumulate(__magic_name__    ):
																												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 						lowerCamelCase__			(		unittest.TestCase ):


						def 	_lowerCamelCase      (			self    :	int      ):
													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    :	Any      ):
													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    :	List[Any]      ):
													debug_launcher(partial(_a   ,							num_processes=1   ,							step_scheduler_with_optimizer=_a      )   ,							num_processes=1      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													debug_launcher(partial(_a   ,							num_processes=1   ,							step_scheduler_with_optimizer=_a      )   ,							num_processes=1      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													AcceleratorState._reset_state(_a      )
													debug_launcher(_a      )
													debug_launcher(partial(_a   ,							num_processes=1   ,							split_batches=_a      )   ,							num_processes=1      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													AcceleratorState._reset_state(_a      )
													debug_launcher(_a      )
													debug_launcher(partial(_a   ,							num_processes=1   ,							split_batches=_a      )   ,							num_processes=1      )


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


						def 	_lowerCamelCase      (			self    :	Any      ):
													AcceleratorState._reset_state(_a      )
													debug_launcher(partial(_a   ,							step_scheduler_with_optimizer=_a      )      )







						@require_huggingface_suite
						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													AcceleratorState._reset_state(_a      )
													debug_launcher(partial(_a   ,							num_processes=1      )      )
													debug_launcher(_a      )










from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__UpperCAmelCase    	=  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase    	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





