
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      __UpperCAmelCase    (  snake_case_							:		str=2				,					snake_case_							:		Optional[int]=True				,					snake_case_							:		List[str]=False				):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     Accelerator(step_scheduler_with_optimizer=snake_case_				,					split_batches=snake_case_				)
	UpperCAmelCase:					Optional[Any] 						=     torch.nn.Linear(2				,					4				)
	UpperCAmelCase:					Optional[Any] 						=     torch.optim.AdamW(model.parameters()				,					lr=1.0				)
	UpperCAmelCase:					Optional[int] 						=     torch.optim.lr_scheduler.OneCycleLR(snake_case_				,					max_lr=0.01				,					steps_per_epoch=2				,					epochs=1				)
	UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Any 						=     accelerator.prepare(snake_case_				,					snake_case_				,					snake_case_				)

	# 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      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]=2				,					snake_case_							:		int=True				,					snake_case_							:		str=False				):



	'''simple docstring'''



	UpperCAmelCase:					Any 						=     Accelerator(step_scheduler_with_optimizer=snake_case_				,					split_batches=snake_case_				)
	UpperCAmelCase:					Union[str, Any] 						=     torch.nn.Linear(2				,					4				)
	UpperCAmelCase:					Optional[int] 						=     torch.optim.AdamW(model.parameters()				,					lr=1.0				)
	UpperCAmelCase:					List[Any] 						=     torch.optim.lr_scheduler.LambdaLR(snake_case_				,					lr_lambda=lambda snake_case_				: 1 - n / 1_0				)
	UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					List[str] 						=     accelerator.prepare(snake_case_				,					snake_case_				,					snake_case_				)

	# Optimizer has stepped
	UpperCAmelCase:					Union[str, Any] 						=     False
	scheduler.step()
	UpperCAmelCase:					Dict 						=     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
	UpperCAmelCase:					Dict 						=     True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		UpperCAmelCase:					Optional[int] 						=     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      __UpperCAmelCase    (  snake_case_							:		int = 2				):



	'''simple docstring'''



	from transformers import get_linear_schedule_with_warmup

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

		UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Any 						=     accelerator.prepare(snake_case_				,					snake_case_				,					snake_case_				)

		for i in range(1_0 * num_steps				):
			with accelerator.accumulate(snake_case_				):
				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 		__lowerCamelCase     (   unittest.TestCase		):



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






								"""simple docstring"""



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



							def     A__ (     self							)						->     str:






								"""simple docstring"""



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



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






								"""simple docstring"""



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



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






								"""simple docstring"""



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



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



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



							def     A__ (     self							)						->     str:






								"""simple docstring"""



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



							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



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



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



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




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






								"""simple docstring"""



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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_      :					Dict				      =     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['BigBirdTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     ['BigBirdTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BigBirdForCausalLM',
	    'BigBirdForMaskedLM',
	    'BigBirdForMultipleChoice',
	    'BigBirdForPreTraining',
	    'BigBirdForQuestionAnswering',
	    'BigBirdForSequenceClassification',
	    'BigBirdForTokenClassification',
	    'BigBirdLayer',
	    'BigBirdModel',
	    'BigBirdPreTrainedModel',
	    'load_tf_weights_in_big_bird',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     [
	    'FlaxBigBirdForCausalLM',
	    'FlaxBigBirdForMaskedLM',
	    'FlaxBigBirdForMultipleChoice',
	    'FlaxBigBirdForPreTraining',
	    'FlaxBigBirdForQuestionAnswering',
	    'FlaxBigBirdForSequenceClassification',
	    'FlaxBigBirdForTokenClassification',
	    'FlaxBigBirdModel',
	    'FlaxBigBirdPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird import BigBirdTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird_fast import BigBirdTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_big_bird import (
		    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BigBirdForCausalLM,
		    BigBirdForMaskedLM,
		    BigBirdForMultipleChoice,
		    BigBirdForPreTraining,
		    BigBirdForQuestionAnswering,
		    BigBirdForSequenceClassification,
		    BigBirdForTokenClassification,
		    BigBirdLayer,
		    BigBirdModel,
		    BigBirdPreTrainedModel,
		    load_tf_weights_in_big_bird,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		    FlaxBigBirdPreTrainedModel,
		)

else:
	import sys

	snake_case_      :					int				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
