



"""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_						:    Union[str, Any]				=      logging.get_logger(__name__)

A_						:    Tuple				=      {
    '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     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= '''mobilenet_v1'''





	def __init__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=3     ,       _SCREAMING_SNAKE_CASE				:       int=224     ,       _SCREAMING_SNAKE_CASE				:       Tuple=1.0     ,       _SCREAMING_SNAKE_CASE				:       str=8     ,       _SCREAMING_SNAKE_CASE				:       Tuple="relu6"     ,       _SCREAMING_SNAKE_CASE				:       Tuple=True     ,       _SCREAMING_SNAKE_CASE				:       Dict=0.9_9_9     ,       _SCREAMING_SNAKE_CASE				:       Any=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       List[str]=0.0_0_1     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					super().__init__(**_SCREAMING_SNAKE_CASE    )

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

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




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Any  						= version.parse('''1.11''' )





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





					"""simple docstring"""


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





	@property
	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	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 			_lowerCAmelCase     (      self				:       Dict    )						->	float:





					"""simple docstring"""


					return 1E-4










"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    Optional[Any]				=      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SqueezeBertForMaskedLM',
							    'SqueezeBertForMultipleChoice',
							    'SqueezeBertForQuestionAnswering',
							    'SqueezeBertForSequenceClassification',
							    'SqueezeBertForTokenClassification',
							    'SqueezeBertModel',
							    'SqueezeBertModule',
							    'SqueezeBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

							A_						:    Optional[Any]				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





