'''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 							A__   (	lowercase__  ):
  A__					=    0
  A__					=    False
  A__					=    3.0







class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Optional[Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       self.assertDictEqual(MockClass().to_kwargs()							, {}			)
       self.assertDictEqual(MockClass(a=2			).to_kwargs()							, {'a': 2}			)
       self.assertDictEqual(MockClass(a=2							, b=_UpperCamelCase			).to_kwargs()							, {'a': 2, 'b': True}			)
       self.assertDictEqual(MockClass(a=2							, c=2.25			).to_kwargs()							, {'a': 2, 'c': 2.25}			)





  @require_cuda
  def 						A  (		self				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =GradScalerKwargs(init_scale=1024							, growth_factor=2			)
       AcceleratorState._reset_state()
       _SCREAMING_SNAKE_CASE							   =Accelerator(mixed_precision='fp16'							, kwargs_handlers=[scaler_handler]			)
       print(accelerator.use_fpaa			)
       _SCREAMING_SNAKE_CASE							   =accelerator.scaler

       # Check the kwargs have been applied
       self.assertEqual(scaler._init_scale							, 1024.0			)
       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							, 2000			)
       self.assertEqual(scaler._enabled							, _UpperCamelCase			)





  @require_multi_gpu
  def 						A  (		self				:     Union[str, Any]			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__			)]
       execute_subprocess_async(_UpperCamelCase							, env=os.environ.copy()			)


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

     # Check the values changed in kwargs
     lowerCamelCase			:				str 			=					''
     lowerCamelCase			:				List[Any] 			=					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'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase			:				Optional[int] 			=					{
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					["EfficientNetImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "EfficientNetForImageClassification",
         "EfficientNetModel",
         "EfficientNetPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_efficientnet import (
         EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EfficientNetConfig,
         EfficientNetOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_efficientnet import EfficientNetImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_efficientnet import (
              EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              EfficientNetForImageClassification,
              EfficientNetModel,
              EfficientNetPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				int 			=					_LazyModule(__name__, globals()["__file__"], _import_structure)
