



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

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


__a      =				{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["MobileBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MobileBertForMaskedLM",
						    "MobileBertForMultipleChoice",
						    "MobileBertForNextSentencePrediction",
						    "MobileBertForPreTraining",
						    "MobileBertForQuestionAnswering",
						    "MobileBertForSequenceClassification",
						    "MobileBertForTokenClassification",
						    "MobileBertLayer",
						    "MobileBertModel",
						    "MobileBertPreTrainedModel",
						    "load_tf_weights_in_mobilebert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMobileBertForMaskedLM",
						    "TFMobileBertForMultipleChoice",
						    "TFMobileBertForNextSentencePrediction",
						    "TFMobileBertForPreTraining",
						    "TFMobileBertForQuestionAnswering",
						    "TFMobileBertForSequenceClassification",
						    "TFMobileBertForTokenClassification",
						    "TFMobileBertMainLayer",
						    "TFMobileBertModel",
						    "TFMobileBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mobilebert import (
						    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileBertConfig,
						    MobileBertOnnxConfig,
						)
						from .tokenization_mobilebert import MobileBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mobilebert_fast import MobileBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilebert import (
												    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileBertForMaskedLM,
												    MobileBertForMultipleChoice,
												    MobileBertForNextSentencePrediction,
												    MobileBertForPreTraining,
												    MobileBertForQuestionAnswering,
												    MobileBertForSequenceClassification,
												    MobileBertForTokenClassification,
												    MobileBertLayer,
												    MobileBertModel,
												    MobileBertPreTrainedModel,
												    load_tf_weights_in_mobilebert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilebert import (
												    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileBertForMaskedLM,
												    TFMobileBertForMultipleChoice,
												    TFMobileBertForNextSentencePrediction,
												    TFMobileBertForPreTraining,
												    TFMobileBertForQuestionAnswering,
												    TFMobileBertForSequenceClassification,
												    TFMobileBertForTokenClassification,
												    TFMobileBertMainLayer,
												    TFMobileBertModel,
												    TFMobileBertPreTrainedModel,
												)

else:
						import sys

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


'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase :  int    							=       logging.get_logger(__name__)

_lowercase :  List[Any]    							=       {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     '''nat'''

					lowerCAmelCase_        =     {
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=[3, 4, 6, 5]				,							__SCREAMING_SNAKE_CASE=[2, 4, 8, 16]				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=3.0				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=1E-5				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       patch_size
											lowercase_	:     List[Any]													=       num_channels
											lowercase_	:     str													=       embed_dim
											lowercase_	:     List[str]													=       depths
											lowercase_	:     str													=       len(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       num_heads
											lowercase_	:     int													=       kernel_size
											lowercase_	:     Union[str, Any]													=       mlp_ratio
											lowercase_	:     Optional[int]													=       qkv_bias
											lowercase_	:     List[Any]													=       hidden_dropout_prob
											lowercase_	:     Optional[int]													=       attention_probs_dropout_prob
											lowercase_	:     List[Any]													=       drop_path_rate
											lowercase_	:     List[Any]													=       hidden_act
											lowercase_	:     int													=       layer_norm_eps
											lowercase_	:     int													=       initializer_range
											# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											lowercase_	:     Dict													=       int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE		) - 1)		)
											lowercase_	:     Tuple													=       layer_scale_init_value
											lowercase_	:     Union[str, Any]													=       ['''stem'''] + [F'''stage{idx}''' for idx in range(1				,							len(__SCREAMING_SNAKE_CASE		) + 1		)]
											lowercase_						, lowercase_	:     int													=       get_aligned_output_features_output_indices(
											    out_features=__SCREAMING_SNAKE_CASE				,							out_indices=__SCREAMING_SNAKE_CASE				,							stage_names=self.stage_names		)



