'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						0
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						3.0






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()     ,			{}    )
         self.assertDictEqual(MockClass(a=2    ).to_kwargs()     ,			{'a': 2}    )
         self.assertDictEqual(MockClass(a=2     ,			b=_lowerCamelCase    ).to_kwargs()     ,			{'a': 2, 'b': True}    )
         self.assertDictEqual(MockClass(a=2     ,			c=2.2_5    ).to_kwargs()     ,			{'a': 2, 'c': 2.2_5}    )







     @require_cuda
     def lowerCAmelCase     (		self    ):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase	:	List[str]		    =  GradScalerKwargs(init_scale=1_0_2_4     ,			growth_factor=2    )
         AcceleratorState._reset_state()
         __UpperCamelCase	:	List[str]		    =  Accelerator(mixed_precision='fp16'     ,			kwargs_handlers=[scaler_handler]    )
         print(accelerator.use_fpaa    )
         __UpperCamelCase	:	List[str]		    =  accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale     ,			1_0_2_4.0    )
         self.assertEqual(scaler._growth_factor     ,			2.0    )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor     ,			0.5    )
         self.assertEqual(scaler._growth_interval     ,			2_0_0_0    )
         self.assertEqual(scaler._enabled     ,			_lowerCamelCase    )







     @require_multi_gpu
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
         execute_subprocess_async(_lowerCamelCase     ,			env=os.environ.copy()    )


if __name__ == "__main__":
  a=  DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
  a=  Accelerator(kwargs_handlers=[ddp_scaler])
  a=  torch.nn.Linear(1_0_0, 2_0_0)
  a=  accelerator.prepare(model)

  # Check the values changed in kwargs
  a=  ''''''
  a=  model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
  if observed_bucket_cap_map != 1_5:
    error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
  if model.find_unused_parameters is not True:
    error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

  # Check the values of the defaults
  if model.dim != 0:
    error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
  if model.broadcast_buffers is not True:
    error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
  if model.gradient_as_bucket_view is not False:
    error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)




'''simple docstring'''
from typing import TYPE_CHECKING

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


a=  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''BridgeTowerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BridgeTowerForContrastiveLearning''',
      '''BridgeTowerForImageAndTextRetrieval''',
      '''BridgeTowerForMaskedLM''',
      '''BridgeTowerModel''',
      '''BridgeTowerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bridgetower import (
      BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BridgeTowerConfig,
      BridgeTowerTextConfig,
      BridgeTowerVisionConfig,
  )
  from .processing_bridgetower import BridgeTowerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bridgetower import BridgeTowerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bridgetower import (
        BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BridgeTowerForContrastiveLearning,
        BridgeTowerForImageAndTextRetrieval,
        BridgeTowerForMaskedLM,
        BridgeTowerModel,
        BridgeTowerPreTrainedModel,
    )


else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)



