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_			(							lowerCAmelCase__			):
  _SCREAMING_SNAKE_CASE     					=      0
  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      3.0
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   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=2.25							).to_kwargs()  ,				{"a": 2, "c": 2.25}							)

  @require_cuda
  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	GradScalerKwargs(init_scale=10_24  ,				growth_factor=2							)
   AcceleratorState._reset_state()
   __a							=	Accelerator(mixed_precision="fp16"  ,				kwargs_handlers=[scaler_handler]							)
   print(accelerator.use_fpaa							)
   __a							=	accelerator.scaler

   # Check the kwargs have been applied
   self.assertEqual(scaler._init_scale  ,				10_24.0							)
   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  ,				20_00							)
   self.assertEqual(scaler._enabled  ,				__lowerCAmelCase							)

  @require_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__							)]
   execute_subprocess_async(__lowerCAmelCase  ,				env=os.environ.copy()							)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Tuple				  =		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
  SCREAMING_SNAKE_CASE			:						Dict				  =		Accelerator(kwargs_handlers=[ddp_scaler])
  SCREAMING_SNAKE_CASE			:						int				  =		torch.nn.Linear(100, 200)
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		accelerator.prepare(model)

  # Check the values changed in kwargs
  SCREAMING_SNAKE_CASE			:						Dict				  =		""""""
  SCREAMING_SNAKE_CASE			:						int				  =		model.bucket_bytes_cap // (1024 * 1024)
  if observed_bucket_cap_map != 15:
    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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase        =    logging.get_logger(__name__)

__lowercase        =    {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''mobilenet_v1'''



			def __init__(  self			,       __lowerCAmelCase=3			,       __lowerCAmelCase=224			,       __lowerCAmelCase=1.0			,       __lowerCAmelCase=8			,       __lowerCAmelCase="relu6"			,       __lowerCAmelCase=True			,       __lowerCAmelCase=0.999			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=0.001			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)

					if depth_multiplier <= 0:
							raise ValueError("""depth_multiplier must be greater than zero.""")

					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    depth_multiplier
					lowerCAmelCase     =    min_depth
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    tf_padding
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			version.parse('''1.11'''      )



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return OrderedDict([("""pixel_values""", {0: """batch"""})])



			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self.task == "image-classification":
							return OrderedDict([("""logits""", {0: """batch"""})])
					else:
							return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})])







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 1E-4




