'''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__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	int						=			0
			UpperCAmelCase_     :	bool						=			False
			UpperCAmelCase_     :	float						=			3.0







class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					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 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    GradScalerKwargs(init_scale=1024			,       growth_factor=2)
					AcceleratorState._reset_state()
					lowerCAmelCase     =    Accelerator(mixed_precision="""fp16"""			,       kwargs_handlers=[scaler_handler])
					print(accelerator.use_fpaa)
					lowerCAmelCase     =    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			,       __lowerCAmelCase)







			@require_multi_gpu
			def 				a_			(  self):




					"""simple docstring"""


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


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

						# Check the values changed in kwargs
						__lowercase        =    ''''''
						__lowercase        =    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 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




