





from typing import TYPE_CHECKING

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


_UpperCAmelCase				       = {
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         '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

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








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_       (							UpperCamelCase_=2    ,						UpperCamelCase_=True    ,						UpperCamelCase_=False     )   ->						str:
       UpperCamelCase_      				=     Accelerator(step_scheduler_with_optimizer=UpperCamelCase_    ,						split_batches=UpperCamelCase_     )
       UpperCamelCase_      				=     torch.nn.Linear(2    ,						4     )
       UpperCamelCase_      				=     torch.optim.AdamW(model.parameters()    ,						lr=1.0     )
       UpperCamelCase_      				=     torch.optim.lr_scheduler.OneCycleLR(UpperCamelCase_    ,						max_lr=0.01    ,						steps_per_epoch=2    ,						epochs=1     )
       UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     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        lowerCAmelCase_       (							UpperCamelCase_=2    ,						UpperCamelCase_=True    ,						UpperCamelCase_=False     )   ->						List[Any]:
       UpperCamelCase_      				=     Accelerator(step_scheduler_with_optimizer=UpperCamelCase_    ,						split_batches=UpperCamelCase_     )
       UpperCamelCase_      				=     torch.nn.Linear(2    ,						4     )
       UpperCamelCase_      				=     torch.optim.AdamW(model.parameters()    ,						lr=1.0     )
       UpperCamelCase_      				=     torch.optim.lr_scheduler.LambdaLR(UpperCamelCase_    ,						lr_lambda=lambda UpperCamelCase_     : 1 - n / 10     )
       UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     accelerator.prepare(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )

       # Optimizer has stepped
       UpperCamelCase_      				=     False
       scheduler.step()
       UpperCamelCase_      				=     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
       UpperCamelCase_      				=     True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              UpperCamelCase_      				=     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_       (							UpperCamelCase_ = 2     )   ->						int:
       from transformers import get_linear_schedule_with_warmup

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

              UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     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 							_UpperCamelCase    (		unittest.TestCase   ):

       def lowercase       (						self:       Dict						)				->       Any:




              """simple docstring"""


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

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


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

       def lowercase       (						self:       Dict						)				->       List[str]:




              """simple docstring"""


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

       def lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


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

       def lowercase       (						self:       Optional[int]						)				->       List[str]:




              """simple docstring"""


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

       def lowercase       (						self:       Tuple						)				->       Optional[int]:




              """simple docstring"""


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

       def lowercase       (						self:       int						)				->       Tuple:




              """simple docstring"""


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

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


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



       @require_huggingface_suite
       def lowercase       (						self:       Dict						)				->       Optional[Any]:




              """simple docstring"""


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

