



"""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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """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 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"mobilenet_v1"






							def __init__(   self			,     a_=3			,     a_=2_2_4			,     a_=1.0			,     a_=8			,     a_="relu6"			,     a_=True			,     a_=0.999			,     a_=0.02			,     a_=0.001			,     **a_			,     ):
									super().__init__(**a_      )

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

									a_     :      Optional[Any] 						=     num_channels
									a_     :      int 						=     image_size
									a_     :      Union[str, Any] 						=     depth_multiplier
									a_     :      Dict 						=     min_depth
									a_     :      List[str] 						=     hidden_act
									a_     :      Dict 						=     tf_padding
									a_     :      Optional[Any] 						=     classifier_dropout_prob
									a_     :      Dict 						=     initializer_range
									a_     :      List[Any] 						=     layer_norm_eps




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					version.parse("1.11"				)






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






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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

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