





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					(	lowerCAmelCase_     ):
							_UpperCamelCase				:					Optional[Any]         =   '''mobilenet_v1'''

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




														"""simple docstring"""


														super().__init__(**_SCREAMING_SNAKE_CASE		)

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

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



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
							_UpperCamelCase				:					Optional[int]         =   version.parse('''1.11'''     )

							@property
							def lowercase (		self: Dict		)       -> Mapping[str, Mapping[int, str]]:




														"""simple docstring"""


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

							@property
							def lowercase (		self: Tuple		)       -> 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 lowercase (		self: Tuple		)       -> float:




														"""simple docstring"""


														return 1e-4








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory




def  lowerCAmelCase_ (							)     ->		Dict:
       raise RuntimeError("CUDA out of memory."  )



class 							_UpperCamelCase					(	nn.Module     ):

       def __init__(		self: int		)       -> List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_          =  nn.Linear(3					,   4		)
              UpperCamelCase_          =  nn.BatchNormad(4		)
              UpperCamelCase_          =  nn.Linear(4					,   5		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE		)		)		)



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: str		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()

              mock_training_loop_function()
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)

       def lowercase (		self: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()
                     return batch_size, arga

              UpperCamelCase_       , UpperCamelCase_          =  mock_training_loop_function("hello"		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)
              self.assertListEqual([bs, arga]					,   [8, "hello"]		)

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=0		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Optional[int]		):
                     if batch_size > 0:
                            raise_fake_out_of_memory()
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     if batch_size != 8:
                            raise raise_fake_out_of_memory()

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function(128					,   "hello"					,   "world"		)
                     self.assertIn("Batch size was passed into `f`"					,   cm.exception.args[0]		)
                     self.assertIn("`f(arg1='hello', arg2='world')"					,   cm.exception.args[0]		)

       def lowercase (		self: List[str]		)       -> Optional[Any]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[Any]		):
                     raise ValueError("Oops, we had an error!"		)

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("Oops, we had an error!"					,   cm.exception.args[0]		)



       @require_cuda
       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  ModelForTest()
              model.cuda()
              self.assertGreater(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  release_memory(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)

