
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a								=						{
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a								=						[
				    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "AltCLIPPreTrainedModel",
				    "AltCLIPModel",
				    "AltCLIPTextModel",
				    "AltCLIPVisionModel",
				]


if TYPE_CHECKING:
				from .configuration_altclip import (
				    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AltCLIPConfig,
				    AltCLIPTextConfig,
				    AltCLIPVisionConfig,
				)
				from .processing_altclip import AltCLIPProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_altclip import (
								    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AltCLIPModel,
								    AltCLIPPreTrainedModel,
								    AltCLIPTextModel,
								    AltCLIPVisionModel,
								)


else:
				import sys

				_a								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












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


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     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_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

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

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




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





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




				"""simple docstring"""



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





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




				"""simple docstring"""



				return 1e-4
