
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


A     :	Tuple    = logging.get_logger(__name__)

A     :	str    = {
    '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 			__A( a			):
				snake_case_						   =    '''mobilenet_v2'''




				def __init__(     self		,     _snake_case=3		,     _snake_case=224		,     _snake_case=1.0		,     _snake_case=8		,     _snake_case=8		,     _snake_case=6		,     _snake_case=32		,     _snake_case=True		,     _snake_case=True		,     _snake_case="relu6"		,     _snake_case=True		,     _snake_case=0.8		,     _snake_case=0.02		,     _snake_case=0.001		,     _snake_case=255		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)

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

								__a										=    num_channels
								__a										=    image_size
								__a										=    depth_multiplier
								__a										=    depth_divisible_by
								__a										=    min_depth
								__a										=    expand_ratio
								__a										=    output_stride
								__a										=    first_layer_is_expansion
								__a										=    finegrained_output
								__a										=    hidden_act
								__a										=    tf_padding
								__a										=    classifier_dropout_prob
								__a										=    initializer_range
								__a										=    layer_norm_eps
								__a										=    semantic_loss_ignore_index







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




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


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4

# flake8: noqa
# Lint as: python3
A     :	Optional[Any]    = [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental