



"""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__		:				Dict          =	logging.get_logger(__name__)

lowercase__		:				Optional[int]          =	{
    """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__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """mobilenet_v1"""



      def __init__(      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str=3    ,			SCREAMING_SNAKE_CASE_				:      Tuple=2_2_4    ,			SCREAMING_SNAKE_CASE_				:      str=1.0    ,			SCREAMING_SNAKE_CASE_				:      Tuple=8    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="relu6"    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Any=0.9_99    ,			SCREAMING_SNAKE_CASE_				:      Any=0.02    ,			SCREAMING_SNAKE_CASE_				:      Any=0.0_01    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			):
        super().__init__(**SCREAMING_SNAKE_CASE_       )

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

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







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     version.parse("""1.11"""  )



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        return OrderedDict([('pixel_values', {0: 'batch'})]       )



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        if self.task == "image-classification":
          return OrderedDict([('logits', {0: 'batch'})]       )
        else:
          return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]       )





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        return 1E-4










"""simple docstring"""







from typing import Union

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     fire.Fire(convert)





