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						:   Tuple         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   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        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'mobilenet_v1'



							def __init__(						self				,    lowercase_=3				,    lowercase_=224				,    lowercase_=1.0				,    lowercase_=8				,    lowercase_="relu6"				,    lowercase_=True				,    lowercase_=0.999				,    lowercase_=0.02				,    lowercase_=0.001				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

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

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




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		version.parse('1.11'		)



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



							@property
							def 	UpperCamelCase	(						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 	UpperCamelCase	(						self    ):
													return 1e-4
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			['DistilBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   int         =			[
				    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DistilBertForMaskedLM',
				    'DistilBertForMultipleChoice',
				    'DistilBertForQuestionAnswering',
				    'DistilBertForSequenceClassification',
				    'DistilBertForTokenClassification',
				    'DistilBertModel',
				    'DistilBertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Any         =			[
				    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDistilBertForMaskedLM',
				    'TFDistilBertForMultipleChoice',
				    'TFDistilBertForQuestionAnswering',
				    'TFDistilBertForSequenceClassification',
				    'TFDistilBertForTokenClassification',
				    'TFDistilBertMainLayer',
				    'TFDistilBertModel',
				    'TFDistilBertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'FlaxDistilBertForMaskedLM',
				    'FlaxDistilBertForMultipleChoice',
				    'FlaxDistilBertForQuestionAnswering',
				    'FlaxDistilBertForSequenceClassification',
				    'FlaxDistilBertForTokenClassification',
				    'FlaxDistilBertModel',
				    'FlaxDistilBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_distilbert import (
				    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DistilBertConfig,
				    DistilBertOnnxConfig,
				)
				from .tokenization_distilbert import DistilBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_distilbert_fast import DistilBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_distilbert import (
								    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DistilBertForMaskedLM,
								    DistilBertForMultipleChoice,
								    DistilBertForQuestionAnswering,
								    DistilBertForSequenceClassification,
								    DistilBertForTokenClassification,
								    DistilBertModel,
								    DistilBertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_distilbert import (
								    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDistilBertForMaskedLM,
								    TFDistilBertForMultipleChoice,
								    TFDistilBertForQuestionAnswering,
								    TFDistilBertForSequenceClassification,
								    TFDistilBertForTokenClassification,
								    TFDistilBertMainLayer,
								    TFDistilBertModel,
								    TFDistilBertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_distilbert import (
								    FlaxDistilBertForMaskedLM,
								    FlaxDistilBertForMultipleChoice,
								    FlaxDistilBertForQuestionAnswering,
								    FlaxDistilBertForSequenceClassification,
								    FlaxDistilBertForTokenClassification,
								    FlaxDistilBertModel,
								    FlaxDistilBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)