





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   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						int     		=				0
   _UpperCamelCase :						bool     		=				False
   _UpperCamelCase :						float     		=				3.0







class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





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





   @require_cuda
   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						List[str]      		=  GradScalerKwargs(init_scale=1_024					,			growth_factor=2      )
      AcceleratorState._reset_state()
      a__	:						List[Any]      		=  Accelerator(mixed_precision="fp16"					,			kwargs_handlers=[scaler_handler]      )
      print(accelerator.use_fpaa      )
      a__	:						int      		=  accelerator.scaler

      # Check the kwargs have been applied
      self.assertEqual(scaler._init_scale					,			1_024.0      )
      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_000      )
      self.assertEqual(scaler._enabled					,			snake_case      )





   @require_multi_gpu
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      execute_subprocess_async(snake_case					,			env=os.environ.copy()      )


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

  # Check the values changed in kwargs
  SCREAMING_SNAKE_CASE__			      =						""""""
  SCREAMING_SNAKE_CASE__			      =						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)








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


SCREAMING_SNAKE_CASE__       :			Dict			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				"""mobilenet_v2"""





   def __init__( self					,			snake_case=3					,			snake_case=224					,			snake_case=1.0					,			snake_case=8					,			snake_case=8					,			snake_case=6					,			snake_case=32					,			snake_case=True					,			snake_case=True					,			snake_case="relu6"					,			snake_case=True					,			snake_case=0.8					,			snake_case=0.02					,			snake_case=0.001					,			snake_case=255					,			**snake_case					,			)				->  int:
      """simple docstring"""





      super().__init__(**snake_case      )

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

      a__	:						str      		=  num_channels
      a__	:						Dict      		=  image_size
      a__	:						Any      		=  depth_multiplier
      a__	:						str      		=  depth_divisible_by
      a__	:						Optional[int]      		=  min_depth
      a__	:						Dict      		=  expand_ratio
      a__	:						str      		=  output_stride
      a__	:						Optional[int]      		=  first_layer_is_expansion
      a__	:						Union[str, Any]      		=  finegrained_output
      a__	:						Union[str, Any]      		=  hidden_act
      a__	:						str      		=  tf_padding
      a__	:						List[Any]      		=  classifier_dropout_prob
      a__	:						List[Any]      		=  initializer_range
      a__	:						Optional[Any]      		=  layer_norm_eps
      a__	:						str      		=  semantic_loss_ignore_index







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Any     		=				version.parse("""1.11"""  )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





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





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """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 	_snake_case ( self      )				->  float:
      """simple docstring"""





      return 1E-4

