


'''simple docstring'''



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


__A	=logging.get_logger(__name__)

__A	={
    '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 		_snake_case					(     a__       ,   a__	):
					lowerCAmelCase      :Dict   =					'''nat'''

					lowerCAmelCase      :Tuple   =					{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}




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

								UpperCAmelCase__  :					List[str]        =     patch_size
								UpperCAmelCase__  :					Any        =     num_channels
								UpperCAmelCase__  :					str        =     embed_dim
								UpperCAmelCase__  :					Tuple        =     depths
								UpperCAmelCase__  :					Union[str, Any]        =     len(_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     num_heads
								UpperCAmelCase__  :					Tuple        =     kernel_size
								UpperCAmelCase__  :					Any        =     mlp_ratio
								UpperCAmelCase__  :					Optional[int]        =     qkv_bias
								UpperCAmelCase__  :					Optional[Any]        =     hidden_dropout_prob
								UpperCAmelCase__  :					Optional[Any]        =     attention_probs_dropout_prob
								UpperCAmelCase__  :					List[str]        =     drop_path_rate
								UpperCAmelCase__  :					int        =     hidden_act
								UpperCAmelCase__  :					Optional[Any]        =     layer_norm_eps
								UpperCAmelCase__  :					List[str]        =     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
								UpperCAmelCase__  :					int        =     int(embed_dim * 2 ** (len(_lowerCamelCase) - 1))
								UpperCAmelCase__  :					Optional[int]        =     layer_scale_init_value
								UpperCAmelCase__  :					Tuple        =     ["""stem"""] + [f'''stage{idx}''' for idx in range(1							,							len(_lowerCamelCase) + 1)]
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     get_aligned_output_features_output_indices(
								    out_features=_lowerCamelCase							,							out_indices=_lowerCamelCase							,							stage_names=self.stage_names)



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)