




"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""mobilenet_v1"""
			def __init__(  self  ,      lowercase=3  ,      lowercase=224  ,      lowercase=1.0  ,      lowercase=8  ,      lowercase="relu6"  ,      lowercase=True  ,      lowercase=0.9_99  ,      lowercase=0.02  ,      lowercase=0.0_01  ,      **lowercase  ,      ):
							super().__init__(**lowercase  )

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

							_lowerCamelCase     :			Optional[int]    =    num_channels
							_lowerCamelCase     :			Any    =    image_size
							_lowerCamelCase     :			str    =    depth_multiplier
							_lowerCamelCase     :			Dict    =    min_depth
							_lowerCamelCase     :			List[str]    =    hidden_act
							_lowerCamelCase     :			Union[str, Any]    =    tf_padding
							_lowerCamelCase     :			str    =    classifier_dropout_prob
							_lowerCamelCase     :			Tuple    =    initializer_range
							_lowerCamelCase     :			int    =    layer_norm_eps






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			version.parse("""1.11"""						)
			@property
			def 					A_    (  self  ):
							return OrderedDict([('pixel_values', {0: 'batch'})]  )
			@property
			def 					A_    (  self  ):
							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  ):
							return 1E-4

"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




