



"""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 	snake_case__  (						__lowerCamelCase							:       str=2				,						__lowerCamelCase							:       Optional[int]=True				,						__lowerCamelCase							:       List[str]=False      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =Accelerator(step_scheduler_with_optimizer=snake_case_				,						split_batches=snake_case_      )
			lowerCamelCase__						:						Optional[Any]						      =torch.nn.Linear(2				,						4      )
			lowerCamelCase__						:						Optional[Any]						      =torch.optim.AdamW(model.parameters()				,						lr=1.0      )
			lowerCamelCase__						:						Optional[int]						      =torch.optim.lr_scheduler.OneCycleLR(snake_case_				,						max_lr=0.01				,						steps_per_epoch=2				,						epochs=1      )
			lowerCamelCase__						:						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 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]=2				,						__lowerCamelCase							:       int=True				,						__lowerCamelCase							:       str=False      ):
			"""simple docstring"""
			lowerCamelCase__						:						Any						      =Accelerator(step_scheduler_with_optimizer=snake_case_				,						split_batches=snake_case_      )
			lowerCamelCase__						:						Union[str, Any]						      =torch.nn.Linear(2				,						4      )
			lowerCamelCase__						:						Optional[int]						      =torch.optim.AdamW(model.parameters()				,						lr=1.0      )
			lowerCamelCase__						:						List[Any]						      =torch.optim.lr_scheduler.LambdaLR(snake_case_				,						lr_lambda=lambda __lowerCamelCase      : 1 - n / 10      )
			lowerCamelCase__						:						List[str]						      =accelerator.prepare(snake_case_				,						snake_case_				,						snake_case_      )

			# Optimizer has stepped
			lowerCamelCase__						:						Union[str, Any]						      =False
			scheduler.step()
			lowerCamelCase__						:						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
			lowerCamelCase__						:						Dict						      =True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						lowerCamelCase__						:						Optional[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 	snake_case__  (						__lowerCamelCase							:       int = 2      ):
			"""simple docstring"""
			from transformers import get_linear_schedule_with_warmup

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

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

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


					'''simple docstring'''





					def 						snake_case			(			self				:    str	)->   List[str]:
								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 						snake_case			(			self				:    List[Any]	)->   str:
								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 						snake_case			(			self				:    List[Any]	)->   Optional[Any]:
								debug_launcher(partial(__snake_case,    num_processes=1,    step_scheduler_with_optimizer=__snake_case	),    num_processes=1	)



					def 						snake_case			(			self				:    List[str]	)->   Optional[Any]:
								debug_launcher(partial(__snake_case,    num_processes=1,    step_scheduler_with_optimizer=__snake_case	),    num_processes=1	)



					def 						snake_case			(			self				:    Optional[int]	)->   Any:
								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 						snake_case			(			self				:    Optional[Any]	)->   str:
								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 						snake_case			(			self				:    Optional[Any]	)->   Dict:
								AcceleratorState._reset_state(__snake_case	)
								debug_launcher(partial(__snake_case,    step_scheduler_with_optimizer=__snake_case	)	)



					def 						snake_case			(			self				:    int	)->   Any:
								AcceleratorState._reset_state(__snake_case	)
								debug_launcher(partial(__snake_case,    step_scheduler_with_optimizer=__snake_case	)	)




					@require_huggingface_suite
					def 						snake_case			(			self				:    List[Any]	)->   Union[str, Any]:
								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_tokenizers_available, is_torch_available


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

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