
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  : List[Any]						   =							{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  : Tuple						   =							["""DebertaTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  : Dict						   =							[
		    """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """DebertaForMaskedLM""",
		    """DebertaForQuestionAnswering""",
		    """DebertaForSequenceClassification""",
		    """DebertaForTokenClassification""",
		    """DebertaModel""",
		    """DebertaPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  : Dict						   =							[
		    """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFDebertaForMaskedLM""",
		    """TFDebertaForQuestionAnswering""",
		    """TFDebertaForSequenceClassification""",
		    """TFDebertaForTokenClassification""",
		    """TFDebertaModel""",
		    """TFDebertaPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
		from .tokenization_deberta import DebertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_fast import DebertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta import (
				    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaForMaskedLM,
				    DebertaForQuestionAnswering,
				    DebertaForSequenceClassification,
				    DebertaForTokenClassification,
				    DebertaModel,
				    DebertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta import (
				    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaForMaskedLM,
				    TFDebertaForQuestionAnswering,
				    TFDebertaForSequenceClassification,
				    TFDebertaForTokenClassification,
				    TFDebertaModel,
				    TFDebertaPreTrainedModel,
				)


else:
		import sys

		lowercase  : Dict						   =							_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


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """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		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

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

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






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           return 1e-4
