





'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return DownloadCommand(args.model					,	args.cache_dir					,	args.force					,	args.trust_remote_code       )




class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase	):
       _snake_case     		=				parser.add_parser("download"	)
       download_parser.add_argument(
           "--cache-dir"						,   type=lowerCamelCase						,   default=lowerCamelCase						,   help="Path to location to store the models"	)
       download_parser.add_argument(
           "--force"						,   action="store_true"						,   help="Force the model to be download even if already in cache-dir"	)
       download_parser.add_argument(
           "--trust-remote-code"						,   action="store_true"						,   help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"						,   )
       download_parser.add_argument("model"						,   type=lowerCamelCase						,   help="Name of the model to download"	)
       download_parser.set_defaults(func=lowerCamelCase	)

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				model
       _snake_case     		=				cache
       _snake_case     		=				force
       _snake_case     		=				trust_remote_code






     def 	UpperCamelCase(					self	):
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model						,   cache_dir=self._cache						,   force_download=self._force						,   trust_remote_code=self._trust_remote_code	)
       AutoTokenizer.from_pretrained(
           self._model						,   cache_dir=self._cache						,   force_download=self._force						,   trust_remote_code=self._trust_remote_code	)









'''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


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''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						,   ):
       super().__init__(**lowerCamelCase	)

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

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







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict([("pixel_values", {0: "batch"})]	)

     @property
     def 	UpperCamelCase(					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 	UpperCamelCase(					self	):
       return 1e-4


