






from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowercase  :							int			  =    logging.get_logger(__name__)

# General docstring
lowercase  :							Optional[int]			  =    '''ResNetConfig'''

# Base docstring
lowercase  :							Tuple			  =    '''microsoft/resnet-50'''
lowercase  :							Tuple			  =    [1, 20_48, 7, 7]

# Image classification docstring
lowercase  :							Optional[Any]			  =    '''microsoft/resnet-50'''
lowercase  :							Optional[int]			  =    '''tiger cat'''

lowercase  :							int			  =    [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]




class 		UpperCAmelCase_						(	nn.Module					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 3		,				_SCREAMING_SNAKE_CASE = 1		,				_SCREAMING_SNAKE_CASE = "relu"				) ->				List[Any]:
								super().__init__()
								snake_case_				:							Tuple						 =       nn.Convad(
								    _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				kernel_size=_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				padding=kernel_size // 2		,				bias=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       nn.BatchNormad(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Union[str, Any]						 =       ACTaFN[activation] if activation is not None else nn.Identity()






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Tensor:
								snake_case_				:							str						 =       self.convolution(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       self.normalization(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       self.activation(_SCREAMING_SNAKE_CASE				)
								return hidden_state




class 		UpperCAmelCase_						(	nn.Module					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								super().__init__()
								snake_case_				:							List[Any]						 =       ResNetConvLayer(
								    config.num_channels		,				config.embedding_size		,				kernel_size=7		,				stride=2		,				activation=config.hidden_act				)
								snake_case_				:							List[Any]						 =       nn.MaxPoolad(kernel_size=3		,				stride=2		,				padding=1				)
								snake_case_				:							Tuple						 =       config.num_channels






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Tensor:
								snake_case_				:							Union[str, Any]						 =       pixel_values.shape[1]
								if num_channels != self.num_channels:
										raise ValueError(
										    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."				)
								snake_case_				:							int						 =       self.embedder(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       self.pooler(_SCREAMING_SNAKE_CASE				)
								return embedding




class 		UpperCAmelCase_						(	nn.Module					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 2				) ->				Tuple:
								super().__init__()
								snake_case_				:							int						 =       nn.Convad(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				kernel_size=1		,				stride=_SCREAMING_SNAKE_CASE		,				bias=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Dict						 =       nn.BatchNormad(_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Tensor:
								snake_case_				:							Any						 =       self.convolution(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       self.normalization(_SCREAMING_SNAKE_CASE				)
								return hidden_state




class 		UpperCAmelCase_						(	nn.Module					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 1		,				_SCREAMING_SNAKE_CASE = "relu"				) ->				int:
								super().__init__()
								snake_case_				:							str						 =       in_channels != out_channels or stride != 1
								snake_case_				:							Any						 =       (
								    ResNetShortCut(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE				) if should_apply_shortcut else nn.Identity()
								)
								snake_case_				:							List[Any]						 =       nn.Sequential(
								    ResNetConvLayer(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE				)		,				ResNetConvLayer(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				activation=_SCREAMING_SNAKE_CASE				)		,				)
								snake_case_				:							List[str]						 =       ACTaFN[activation]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							Optional[Any]						 =       hidden_state
								snake_case_				:							Union[str, Any]						 =       self.layer(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       self.shortcut(_SCREAMING_SNAKE_CASE				)
								hidden_state += residual
								snake_case_				:							Tuple						 =       self.activation(_SCREAMING_SNAKE_CASE				)
								return hidden_state




class 		UpperCAmelCase_						(	nn.Module					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 1		,				_SCREAMING_SNAKE_CASE = "relu"		,				_SCREAMING_SNAKE_CASE = 4				) ->				Union[str, Any]:
								super().__init__()
								snake_case_				:							List[Any]						 =       in_channels != out_channels or stride != 1
								snake_case_				:							List[Any]						 =       out_channels // reduction
								snake_case_				:							Optional[Any]						 =       (
								    ResNetShortCut(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE				) if should_apply_shortcut else nn.Identity()
								)
								snake_case_				:							Any						 =       nn.Sequential(
								    ResNetConvLayer(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				kernel_size=1				)		,				ResNetConvLayer(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE				)		,				ResNetConvLayer(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				kernel_size=1		,				activation=_SCREAMING_SNAKE_CASE				)		,				)
								snake_case_				:							Union[str, Any]						 =       ACTaFN[activation]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Tuple						 =       hidden_state
								snake_case_				:							int						 =       self.layer(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       self.shortcut(_SCREAMING_SNAKE_CASE				)
								hidden_state += residual
								snake_case_				:							int						 =       self.activation(_SCREAMING_SNAKE_CASE				)
								return hidden_state




class 		UpperCAmelCase_						(	nn.Module					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 2		,				_SCREAMING_SNAKE_CASE = 2		,				) ->				Dict:
								super().__init__()

								snake_case_				:							int						 =       ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

								snake_case_				:							List[Any]						 =       nn.Sequential(
								    # downsampling is done in the first layer with stride of 2
								    layer(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				activation=config.hidden_act				)		,				*[layer(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				activation=config.hidden_act				) for _ in range(depth - 1				)]		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Tensor:
								snake_case_				:							Dict						 =       input
								for layer in self.layers:
										snake_case_				:							str						 =       layer(_SCREAMING_SNAKE_CASE				)
								return hidden_state




class 		UpperCAmelCase_						(	nn.Module					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								super().__init__()
								snake_case_				:							Dict						 =       nn.ModuleList([]				)
								# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
								self.stages.append(
								    ResNetStage(
								        _SCREAMING_SNAKE_CASE		,				config.embedding_size		,				config.hidden_sizes[0]		,				stride=2 if config.downsample_in_first_stage else 1		,				depth=config.depths[0]		,				)				)
								snake_case_				:							Optional[Any]						 =       zip(config.hidden_sizes		,				config.hidden_sizes[1:]				)
								for (in_channels, out_channels), depth in zip(_SCREAMING_SNAKE_CASE		,				config.depths[1:]				):
										self.stages.append(ResNetStage(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				depth=_SCREAMING_SNAKE_CASE				)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True				) ->				BaseModelOutputWithNoAttention:
								snake_case_				:							str						 =       () if output_hidden_states else None

								for stage_module in self.stages:
										if output_hidden_states:
												snake_case_				:							Optional[int]						 =       hidden_states + (hidden_state,)

										snake_case_				:							int						 =       stage_module(_SCREAMING_SNAKE_CASE				)

								if output_hidden_states:
										snake_case_				:							Optional[int]						 =       hidden_states + (hidden_state,)

								if not return_dict:
										return tuple(v for v in [hidden_state, hidden_states] if v is not None				)

								return BaseModelOutputWithNoAttention(
								    last_hidden_state=_SCREAMING_SNAKE_CASE		,				hidden_states=_SCREAMING_SNAKE_CASE		,				)




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   int						=					ResNetConfig
						A     :   Union[str, Any]						=					'resnet'
						A     :   List[str]						=					'pixel_values'
						A     :   Optional[Any]						=					True






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				str:
								if isinstance(_SCREAMING_SNAKE_CASE		,				nn.Convad				):
										nn.init.kaiming_normal_(module.weight		,				mode="fan_out"		,				nonlinearity="relu"				)
								elif isinstance(_SCREAMING_SNAKE_CASE		,				(nn.BatchNormad, nn.GroupNorm)				):
										nn.init.constant_(module.weight		,				1				)
										nn.init.constant_(module.bias		,				0				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=False				) ->				Dict:
								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							int						 =       value




lowercase  :							List[str]			  =    r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

lowercase  :							Optional[Any]			  =    r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''




@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'					,  SCREAMING_SNAKE_CASE__					,  )
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								super().__init__(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       config
								snake_case_				:							Optional[Any]						 =       ResNetEmbeddings(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[Any]						 =       ResNetEncoder(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       nn.AdaptiveAvgPoolad((1, 1)				)
								# Initialize weights and apply final processing
								self.post_init()






						@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE				)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC		,				output_type=_SCREAMING_SNAKE_CASE		,				config_class=_CONFIG_FOR_DOC		,				modality="vision"		,				expected_output=_EXPECTED_OUTPUT_SHAPE		,				)
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None				) ->				BaseModelOutputWithPoolingAndNoAttention:
								snake_case_				:							List[Any]						 =       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								snake_case_				:							Tuple						 =       return_dict if return_dict is not None else self.config.use_return_dict

								snake_case_				:							List[Any]						 =       self.embedder(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							int						 =       self.encoder(
								    _SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							int						 =       encoder_outputs[0]

								snake_case_				:							Optional[Any]						 =       self.pooler(_SCREAMING_SNAKE_CASE				)

								if not return_dict:
										return (last_hidden_state, pooled_output) + encoder_outputs[1:]

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=_SCREAMING_SNAKE_CASE		,				pooler_output=_SCREAMING_SNAKE_CASE		,				hidden_states=encoder_outputs.hidden_states		,				)




@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '					,  SCREAMING_SNAKE_CASE__					,  )
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								super().__init__(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       config.num_labels
								snake_case_				:							int						 =       ResNetModel(_SCREAMING_SNAKE_CASE				)
								# classification head
								snake_case_				:							Any						 =       nn.Sequential(
								    nn.Flatten()		,				nn.Linear(config.hidden_sizes[-1]		,				config.num_labels				) if config.num_labels > 0 else nn.Identity()		,				)
								# initialize weights and apply final processing
								self.post_init()






						@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE				)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT		,				output_type=_SCREAMING_SNAKE_CASE		,				config_class=_CONFIG_FOR_DOC		,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,				)
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				) ->				ImageClassifierOutputWithNoAttention:
								snake_case_				:							int						 =       return_dict if return_dict is not None else self.config.use_return_dict

								snake_case_				:							int						 =       self.resnet(_SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       outputs.pooler_output if return_dict else outputs[1]

								snake_case_				:							Tuple						 =       self.classifier(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       None

								if labels is not None:
										if self.config.problem_type is None:
												if self.num_labels == 1:
														snake_case_				:							Dict						 =       "regression"
												elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
														snake_case_				:							Dict						 =       "single_label_classification"
												else:
														snake_case_				:							Any						 =       "multi_label_classification"
										if self.config.problem_type == "regression":
												snake_case_				:							List[Any]						 =       MSELoss()
												if self.num_labels == 1:
														snake_case_				:							str						 =       loss_fct(logits.squeeze()		,				labels.squeeze()				)
												else:
														snake_case_				:							Tuple						 =       loss_fct(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										elif self.config.problem_type == "single_label_classification":
												snake_case_				:							Tuple						 =       CrossEntropyLoss()
												snake_case_				:							List[str]						 =       loss_fct(logits.view(-1		,				self.num_labels				)		,				labels.view(-1				)				)
										elif self.config.problem_type == "multi_label_classification":
												snake_case_				:							List[Any]						 =       BCEWithLogitsLoss()
												snake_case_				:							Tuple						 =       loss_fct(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								if not return_dict:
										snake_case_				:							Any						 =       (logits,) + outputs[2:]
										return (loss,) + output if loss is not None else output

								return ImageClassifierOutputWithNoAttention(loss=_SCREAMING_SNAKE_CASE		,				logits=_SCREAMING_SNAKE_CASE		,				hidden_states=outputs.hidden_states				)




@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '					,  SCREAMING_SNAKE_CASE__					,  )
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								super().__init__(_SCREAMING_SNAKE_CASE				)
								super()._init_backbone(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       [config.embedding_size] + config.hidden_sizes
								snake_case_				:							Tuple						 =       ResNetEmbeddings(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       ResNetEncoder(_SCREAMING_SNAKE_CASE				)

								# initialize weights and apply final processing
								self.post_init()






						@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE				)
						@replace_return_docstrings(output_type=_SCREAMING_SNAKE_CASE		,				config_class=_CONFIG_FOR_DOC				)
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None				) ->				BackboneOutput:
								snake_case_				:							Any						 =       return_dict if return_dict is not None else self.config.use_return_dict
								snake_case_				:							str						 =       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)

								snake_case_				:							Dict						 =       self.embedder(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       self.encoder(_SCREAMING_SNAKE_CASE		,				output_hidden_states=_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       outputs.hidden_states

								snake_case_				:							Optional[int]						 =       ()
								for idx, stage in enumerate(self.stage_names				):
										if stage in self.out_features:
												feature_maps += (hidden_states[idx],)

								if not return_dict:
										snake_case_				:							Optional[Any]						 =       (feature_maps,)
										if output_hidden_states:
												output += (outputs.hidden_states,)
										return output

								return BackboneOutput(
								    feature_maps=_SCREAMING_SNAKE_CASE		,				hidden_states=outputs.hidden_states if output_hidden_states else None		,				attentions=_SCREAMING_SNAKE_CASE		,				)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							str			  =    {
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'donut-swin'

						A     :   Union[str, Any]						=					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=224		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=96		,				_SCREAMING_SNAKE_CASE=[2, 2, 6, 2]		,				_SCREAMING_SNAKE_CASE=[3, 6, 12, 24]		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=4.0		,				_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=False		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-5		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       image_size
								snake_case_				:							Any						 =       patch_size
								snake_case_				:							str						 =       num_channels
								snake_case_				:							Dict						 =       embed_dim
								snake_case_				:							Tuple						 =       depths
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							Optional[int]						 =       window_size
								snake_case_				:							Any						 =       mlp_ratio
								snake_case_				:							str						 =       qkv_bias
								snake_case_				:							Optional[Any]						 =       hidden_dropout_prob
								snake_case_				:							Union[str, Any]						 =       attention_probs_dropout_prob
								snake_case_				:							str						 =       drop_path_rate
								snake_case_				:							List[str]						 =       hidden_act
								snake_case_				:							Optional[int]						 =       use_absolute_embeddings
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							List[Any]						 =       initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								snake_case_				:							Any						 =       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE				) - 1)				)
