'''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_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__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''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			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)

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

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







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			version.parse('''1.11'''      )



			@property
			def 				a_			(  self):




					"""simple docstring"""


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



			@property
			def 				a_			(  self):




					"""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 				a_			(  self):




					"""simple docstring"""


					return 1E-4





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase        =    {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase        =    ['''ChineseCLIPFeatureExtractor''']
						__lowercase        =    ['''ChineseCLIPImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase        =    [
						    '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ChineseCLIPModel''',
						    '''ChineseCLIPPreTrainedModel''',
						    '''ChineseCLIPTextModel''',
						    '''ChineseCLIPVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_chinese_clip import (
						    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ChineseCLIPConfig,
						    ChineseCLIPOnnxConfig,
						    ChineseCLIPTextConfig,
						    ChineseCLIPVisionConfig,
						)
						from .processing_chinese_clip import ChineseCLIPProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_chinese_clip import (
												    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ChineseCLIPModel,
												    ChineseCLIPPreTrainedModel,
												    ChineseCLIPTextModel,
												    ChineseCLIPVisionModel,
												)

else:
						import sys

						__lowercase        =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




