




"""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


a      =							logging.get_logger(__name__)

a      =							{
    '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       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'mobilenet_v2'


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Dict=3						,						lowerCAmelCase				:			Union[str, Any]=224						,						lowerCAmelCase				:			Optional[int]=1.0						,						lowerCAmelCase				:			Tuple=8						,						lowerCAmelCase				:			str=8						,						lowerCAmelCase				:			Tuple=6						,						lowerCAmelCase				:			Dict=32						,						lowerCAmelCase				:			Optional[Any]=True						,						lowerCAmelCase				:			Optional[int]=True						,						lowerCAmelCase				:			List[str]="relu6"						,						lowerCAmelCase				:			List[str]=True						,						lowerCAmelCase				:			Dict=0.8						,						lowerCAmelCase				:			Dict=0.02						,						lowerCAmelCase				:			Any=0.001						,						lowerCAmelCase				:			Tuple=255						,						**lowerCAmelCase				:			Optional[Any]						,						):
										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  =				depth_divisible_by
										lowerCAmelCase  =				min_depth
										lowerCAmelCase  =				expand_ratio
										lowerCAmelCase  =				output_stride
										lowerCAmelCase  =				first_layer_is_expansion
										lowerCAmelCase  =				finegrained_output
										lowerCAmelCase  =				hidden_act
										lowerCAmelCase  =				tf_padding
										lowerCAmelCase  =				classifier_dropout_prob
										lowerCAmelCase  =				initializer_range
										lowerCAmelCase  =				layer_norm_eps
										lowerCAmelCase  =				semantic_loss_ignore_index






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						version.parse('1.11'					)


						@property
						def   __lowercase (							self				:			Any					):
										return OrderedDict([("""pixel_values""", {0: """batch"""})]					)


						@property
						def   __lowercase (							self				:			Any					):
										if self.task == "image-classification":
														return OrderedDict([("""logits""", {0: """batch"""})]					)
										else:
														return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]					)




						@property
						def   __lowercase (							self				:			Tuple					):
										return 1e-4











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a      =							{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

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





