

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 A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						'''mobilenet_v1'''

 def __init__(		self :	List[str]     ,						A_ :	Optional[Any]=3     ,						A_ :	str=2_2_4     ,						A_ :	List[Any]=1.0     ,						A_ :	str=8     ,						A_ :	Tuple="relu6"     ,						A_ :	Optional[Any]=True     ,						A_ :	List[Any]=0.999     ,						A_ :	Tuple=0.02     ,						A_ :	Any=0.001     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )

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

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





class A__    ( A ):



 """simple docstring"""







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

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''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    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      return 1E-4









from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[str]      =						''''''
 _lowercase     :					Optional[Any]      =						'''hf-legacy'''  # "hf://"" is reserved for hffs

 def __init__(		self :	Dict     ,						A_ :	Optional[DatasetInfo] = None     ,						A_ :	Optional[str] = None     ,						**A_ :	int     ,						):






      '''simple docstring'''




      super().__init__(self     ,						**A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    repo_info
      _lowerCAmelCase   :					Optional[int]        =    token
      _lowerCAmelCase   :					str        =    None

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      if self.dir_cache is None:
           _lowerCAmelCase   :					int        =    {}
           for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _lowerCAmelCase   :					Optional[int]        =    {
                    "name": hf_file.rfilename,
                    "size": None,
                    "type": "file",
                }
                self.dir_cache.update(
                    {
                        str(A_       ): {"name": str(A_       ), "size": None, "type": "directory"}
                        for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
                    }       )

 def    __magic_name__       (		self :	Dict     ,						A_ :	str     ,						A_ :	str = "rb"     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      if not isinstance(self.repo_info     ,						A_       ):
           raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''       )
      _lowerCAmelCase   :					str        =    hf_hub_url(self.repo_info.id     ,						A_     ,						revision=self.repo_info.sha       )
      return fsspec.open(
          A_     ,						mode=A_     ,						headers=get_authentication_headers_for_url(A_     ,						use_auth_token=self.token       )     ,						client_kwargs={"trust_env": True}     ,						).open()

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      self._get_dirs()
      _lowerCAmelCase   :					Optional[int]        =    self._strip_protocol(A_       )
      if path in self.dir_cache:
           return self.dir_cache[path]
      else:
           raise FileNotFoundError(A_       )


 def    __magic_name__       (		self :	Any     ,						A_ :	str     ,						A_ :	Any=False     ,						**A_ :	Dict       ):






      '''simple docstring'''




      self._get_dirs()
      _lowerCAmelCase   :					Dict        =    PurePosixPath(path.strip("/"       )       )
      _lowerCAmelCase   :					str        =    {}
      for p, f in self.dir_cache.items():
           _lowerCAmelCase   :					List[str]        =    PurePosixPath(p.strip("/"       )       )
           _lowerCAmelCase   :					Tuple        =    p.parent
           if root == path:
                _lowerCAmelCase   :					Union[str, Any]        =    f
      _lowerCAmelCase   :					List[str]        =    list(paths.values()       )
      if detail:
           return out
      else:
           return sorted(f["name"] for f in out       )






