






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def 	__lowerCamelCase      (		*snake_case__		)							->  List[Any]:






						"""simple docstring"""





						if not isinstance(snake_case__	,snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  list(snake_case__		)
						for i in range(len(snake_case__		)		):
												_SCREAMING_SNAKE_CASE     				=  None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects

def 	__lowerCamelCase      (		snake_case__		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  [
						    """CUDA out of memory.""",  # CUDA OOM
						    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
						    """DefaultCPUAllocator: can\'t allocate memory""",  # CPU OOM
						]
						if isinstance(snake_case__	,snake_case__		) and len(exception.args		) == 1:
												return any(err in exception.args[0] for err in _statements		)
						return False




def 	__lowerCamelCase      (		snake_case__ = None	,snake_case__ = 1_28		)							->  Any:






						"""simple docstring"""





						if function is None:
												return functools.partial(snake_case__	,starting_batch_size=snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  starting_batch_size

						def decorator(*snake_case__	,**snake_case__		):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												_SCREAMING_SNAKE_CASE     				=  list(inspect.signature(snake_case__		).parameters.keys()		)
												# Guard against user error
												if len(snake_case__		) < (len(snake_case__		) + 1):
																		_SCREAMING_SNAKE_CASE     				=  """, """.join([F'{arg}={value}' for arg, value in zip(params[1:]	,args[1:]		)]		)
																		raise TypeError(
																		    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
																		    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'		)
												while True:
																		if batch_size == 0:
																								raise RuntimeError("""No executable batch size found, reached zero."""		)
																		try:
																								return function(snake_case__	,*snake_case__	,**snake_case__		)
																		except Exception as e:
																								if should_reduce_batch_size(snake_case__		):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator









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


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''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 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Any									=							"mobilenet_v1"







			def __init__(     self:  List[str]				,     UpperCAmelCase_:  List[Any]=3				,     UpperCAmelCase_:  List[str]=224				,     UpperCAmelCase_:  Optional[Any]=1.0				,     UpperCAmelCase_:  Dict=8				,     UpperCAmelCase_:  List[str]="relu6"				,     UpperCAmelCase_:  List[Any]=True				,     UpperCAmelCase_:  Dict=0.9_99				,     UpperCAmelCase_:  Union[str, Any]=0.02				,     UpperCAmelCase_:  Tuple=0.0_01				,     **UpperCAmelCase_:  List[str]				,     ):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)

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

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



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				List[Any]									=							version.parse("1.11"   )







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




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







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''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 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									return 1E-4

