


"""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    :      Any=2				,    _UpperCAmelCase    :      List[Any]=True				,    _UpperCAmelCase    :      List[str]=False	)							->	Dict:

	'''simple docstring'''


	snake_case__  :						List[str]		=      Accelerator(step_scheduler_with_optimizer=_UpperCAmelCase				,    split_batches=_UpperCAmelCase	)
	snake_case__  :						str		=      torch.nn.Linear(2				,    4	)
	snake_case__  :						Tuple		=      torch.optim.AdamW(model.parameters()				,    lr=1.0	)
	snake_case__  :						List[str]		=      torch.optim.lr_scheduler.OneCycleLR(_UpperCAmelCase				,    max_lr=0.0_1				,    steps_per_epoch=2				,    epochs=1	)
	snake_case__,					snake_case__,					snake_case__  :						str		=      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    :      Optional[int]=2				,    _UpperCAmelCase    :      Tuple=True				,    _UpperCAmelCase    :      Union[str, Any]=False	)							->	Union[str, Any]:

	'''simple docstring'''


	snake_case__  :						Any		=      Accelerator(step_scheduler_with_optimizer=_UpperCAmelCase				,    split_batches=_UpperCAmelCase	)
	snake_case__  :						Union[str, Any]		=      torch.nn.Linear(2				,    4	)
	snake_case__  :						int		=      torch.optim.AdamW(model.parameters()				,    lr=1.0	)
	snake_case__  :						str		=      torch.optim.lr_scheduler.LambdaLR(_UpperCAmelCase				,    lr_lambda=lambda _UpperCAmelCase	: 1 - n / 10	)
	snake_case__,					snake_case__,					snake_case__  :						Any		=      accelerator.prepare(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)

	# Optimizer has stepped
	snake_case__  :						str		=      False
	scheduler.step()
	snake_case__  :						List[Any]		=      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
	snake_case__  :						Optional[Any]		=      True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		snake_case__  :						Optional[Any]		=      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    :      int = 2	)							->	int:

	'''simple docstring'''


	from transformers import get_linear_schedule_with_warmup

	snake_case__  :						Union[str, Any]		=      [1, 2, 4]
	for num_steps in steps:
		snake_case__  :						Union[str, Any]		=      GradientAccumulationPlugin(num_steps=_UpperCAmelCase				,    adjust_scheduler=num_steps > 1	)
		snake_case__  :						Tuple		=      Accelerator(gradient_accumulation_plugin=_UpperCAmelCase	)
		snake_case__  :						str		=      torch.nn.Linear(2				,    4	)
		snake_case__  :						Optional[int]		=      torch.optim.AdamW(model.parameters()				,    lr=1_0.0	)
		snake_case__  :						str		=      get_linear_schedule_with_warmup(optimizer=_UpperCAmelCase				,    num_warmup_steps=0				,    num_training_steps=20	)

		snake_case__,					snake_case__,					snake_case__  :						List[Any]		=      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 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




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




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







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




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







							@require_huggingface_suite
							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




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









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowercase  		=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileViTV2ForImageClassification""",
					    """MobileViTV2ForSemanticSegmentation""",
					    """MobileViTV2Model""",
					    """MobileViTV2PreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileViTVaConfig,
					    MobileViTVaOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevitva import (
										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTVaForImageClassification,
										    MobileViTVaForSemanticSegmentation,
										    MobileViTVaModel,
										    MobileViTVaPreTrainedModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





