





'''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       						=			logging.get_logger(__name__)

_lowercase       						=			{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''mobilenet_v2'''







     def __init__( self    ,   A__=3    ,   A__=2_24    ,   A__=1.0    ,   A__=8    ,   A__=8    ,   A__=6    ,   A__=32    ,   A__=True    ,   A__=True    ,   A__="relu6"    ,   A__=True    ,   A__=0.8    ,   A__=0.0_2    ,   A__=0.0_0_1    ,   A__=2_55    ,   **A__    ,   ):
      super().__init__(**A__    )

      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				=			depth_divisible_by
      snake_case				=			min_depth
      snake_case				=			expand_ratio
      snake_case				=			output_stride
      snake_case				=			first_layer_is_expansion
      snake_case				=			finegrained_output
      snake_case				=			hidden_act
      snake_case				=			tf_padding
      snake_case				=			classifier_dropout_prob
      snake_case				=			initializer_range
      snake_case				=			layer_norm_eps
      snake_case				=			semantic_loss_ignore_index




class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					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








'''simple docstring'''







_lowercase       						=			{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

