
"""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    lowercase	(  lowerCAmelCase__=2  ,lowerCAmelCase__=True  ,lowerCAmelCase__=False	):
			lowerCamelCase_ 		=     Accelerator(step_scheduler_with_optimizer=lowerCAmelCase__  ,split_batches=lowerCAmelCase__	)
			lowerCamelCase_ 		=     torch.nn.Linear(2  ,4	)
			lowerCamelCase_ 		=     torch.optim.AdamW(model.parameters()  ,lr=1.0	)
			lowerCamelCase_ 		=     torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase__  ,max_lr=0.01  ,steps_per_epoch=2  ,epochs=1	)
			lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     accelerator.prepare(lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	)

			# 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    lowercase	(  lowerCAmelCase__=2  ,lowerCAmelCase__=True  ,lowerCAmelCase__=False	):
			lowerCamelCase_ 		=     Accelerator(step_scheduler_with_optimizer=lowerCAmelCase__  ,split_batches=lowerCAmelCase__	)
			lowerCamelCase_ 		=     torch.nn.Linear(2  ,4	)
			lowerCamelCase_ 		=     torch.optim.AdamW(model.parameters()  ,lr=1.0	)
			lowerCamelCase_ 		=     torch.optim.lr_scheduler.LambdaLR(lowerCAmelCase__  ,lr_lambda=lambda lowerCAmelCase__	: 1 - n / 10	)
			lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     accelerator.prepare(lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	)

			# Optimizer has stepped
			lowerCamelCase_ 		=     False
			scheduler.step()
			lowerCamelCase_ 		=     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_ 		=     True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						lowerCamelCase_ 		=     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    lowercase	(  lowerCAmelCase__ = 2	):
			from transformers import get_linear_schedule_with_warmup

			lowerCamelCase_ 		=     [1, 2, 4]
			for num_steps in steps:
						lowerCamelCase_ 		=     GradientAccumulationPlugin(num_steps=lowerCAmelCase__  ,adjust_scheduler=num_steps > 1	)
						lowerCamelCase_ 		=     Accelerator(gradient_accumulation_plugin=lowerCAmelCase__	)
						lowerCamelCase_ 		=     torch.nn.Linear(2  ,4	)
						lowerCamelCase_ 		=     torch.optim.AdamW(model.parameters()  ,lr=10.0	)
						lowerCamelCase_ 		=     get_linear_schedule_with_warmup(optimizer=lowerCAmelCase__  ,num_warmup_steps=0  ,num_training_steps=20	)

						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     accelerator.prepare(lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	)

						for i in range(10 * num_steps	):
									with accelerator.accumulate(lowerCAmelCase__	):
												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 					UpperCAmelCase__	(							self      ):
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1      )       ,	num_processes=1      )
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1       ,	split_batches=UpperCAmelCase      )       ,	num_processes=1      )




				def 					UpperCAmelCase__	(							self      ):
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1      )       ,	num_processes=1      )
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1       ,	split_batches=UpperCAmelCase      )       ,	num_processes=1      )




				def 					UpperCAmelCase__	(							self      ):
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1       ,	step_scheduler_with_optimizer=UpperCAmelCase      )       ,	num_processes=1      )




				def 					UpperCAmelCase__	(							self      ):
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1       ,	step_scheduler_with_optimizer=UpperCAmelCase      )       ,	num_processes=1      )




				def 					UpperCAmelCase__	(							self      ):
							AcceleratorState._reset_state(UpperCAmelCase      )
							debug_launcher(UpperCAmelCase      )
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1       ,	split_batches=UpperCAmelCase      )       ,	num_processes=1      )




				def 					UpperCAmelCase__	(							self      ):
							AcceleratorState._reset_state(UpperCAmelCase      )
							debug_launcher(UpperCAmelCase      )
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1       ,	split_batches=UpperCAmelCase      )       ,	num_processes=1      )




				def 					UpperCAmelCase__	(							self      ):
							AcceleratorState._reset_state(UpperCAmelCase      )
							debug_launcher(partial(UpperCAmelCase       ,	step_scheduler_with_optimizer=UpperCAmelCase      )      )




				def 					UpperCAmelCase__	(							self      ):
							AcceleratorState._reset_state(UpperCAmelCase      )
							debug_launcher(partial(UpperCAmelCase       ,	step_scheduler_with_optimizer=UpperCAmelCase      )      )





				@require_huggingface_suite
				def 					UpperCAmelCase__	(							self      ):
							AcceleratorState._reset_state(UpperCAmelCase      )
							debug_launcher(partial(UpperCAmelCase       ,	num_processes=1      )      )
							debug_launcher(UpperCAmelCase      )





"""simple docstring"""


from typing import TYPE_CHECKING

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


A_						       =				{
    """configuration_flava""": [
        """FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """FlavaConfig""",
        """FlavaImageCodebookConfig""",
        """FlavaImageConfig""",
        """FlavaMultimodalConfig""",
        """FlavaTextConfig""",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				["""FlavaFeatureExtractor"""]
						A_						       =				["""FlavaImageProcessor"""]
						A_						       =				["""FlavaProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				[
						    """FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlavaForPreTraining""",
						    """FlavaImageCodebook""",
						    """FlavaImageModel""",
						    """FlavaModel""",
						    """FlavaMultimodalModel""",
						    """FlavaPreTrainedModel""",
						    """FlavaTextModel""",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

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



