



"""simple docstring"""





from typing import TYPE_CHECKING

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


__lowerCAmelCase       :					Optional[Any]      					=					{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase       :					Optional[int]      					=					[
					    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaPreLayerNormForCausalLM''',
					    '''RobertaPreLayerNormForMaskedLM''',
					    '''RobertaPreLayerNormForMultipleChoice''',
					    '''RobertaPreLayerNormForQuestionAnswering''',
					    '''RobertaPreLayerNormForSequenceClassification''',
					    '''RobertaPreLayerNormForTokenClassification''',
					    '''RobertaPreLayerNormModel''',
					    '''RobertaPreLayerNormPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase       :					Optional[int]      					=					[
					    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaPreLayerNormForCausalLM''',
					    '''TFRobertaPreLayerNormForMaskedLM''',
					    '''TFRobertaPreLayerNormForMultipleChoice''',
					    '''TFRobertaPreLayerNormForQuestionAnswering''',
					    '''TFRobertaPreLayerNormForSequenceClassification''',
					    '''TFRobertaPreLayerNormForTokenClassification''',
					    '''TFRobertaPreLayerNormMainLayer''',
					    '''TFRobertaPreLayerNormModel''',
					    '''TFRobertaPreLayerNormPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase       :					str      					=					[
					    '''FlaxRobertaPreLayerNormForCausalLM''',
					    '''FlaxRobertaPreLayerNormForMaskedLM''',
					    '''FlaxRobertaPreLayerNormForMultipleChoice''',
					    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
					    '''FlaxRobertaPreLayerNormForSequenceClassification''',
					    '''FlaxRobertaPreLayerNormForTokenClassification''',
					    '''FlaxRobertaPreLayerNormModel''',
					    '''FlaxRobertaPreLayerNormPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					__lowerCAmelCase       :					str      					=					_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


__lowerCAmelCase       :					str      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Tuple      					=					{
    '''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    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''nat'''

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





		def __init__(					self							,    _lowercase=4							,    _lowercase=3							,    _lowercase=6_4							,    _lowercase=[3, 4, 6, 5]							,    _lowercase=[2, 4, 8, 1_6]							,    _lowercase=7							,    _lowercase=3.0							,    _lowercase=True							,    _lowercase=0.0							,    _lowercase=0.0							,    _lowercase=0.1							,    _lowercase="gelu"							,    _lowercase=0.02							,    _lowercase=1E-5							,    _lowercase=0.0							,    _lowercase=None							,    _lowercase=None							,    **_lowercase							,    ) ->						Optional[Any]:







								'''simple docstring'''

								super().__init__(**_lowercase   )

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

