




'''simple docstring'''





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


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Optional[int]				=			{
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}


class    lowerCamelCase    (		a__						):






			'''simple docstring'''





			UpperCamelCase__								="""swinv2"""

			UpperCamelCase__								={
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}

			def __init__( self			:   Dict       ,						lowerCamelCase_			:   List[Any]=224       ,						lowerCamelCase_			:   Tuple=4       ,						lowerCamelCase_			:   str=3       ,						lowerCamelCase_			:   Union[str, Any]=96       ,						lowerCamelCase_			:   Dict=[2, 2, 6, 2]       ,						lowerCamelCase_			:   List[str]=[3, 6, 12, 24]       ,						lowerCamelCase_			:   Optional[int]=7       ,						lowerCamelCase_			:   Union[str, Any]=4.0       ,						lowerCamelCase_			:   Any=True       ,						lowerCamelCase_			:   Dict=0.0       ,						lowerCamelCase_			:   Union[str, Any]=0.0       ,						lowerCamelCase_			:   Optional[int]=0.1       ,						lowerCamelCase_			:   Optional[int]="gelu"       ,						lowerCamelCase_			:   str=False       ,						lowerCamelCase_			:   Tuple=0.0_2       ,						lowerCamelCase_			:   Optional[int]=1E-5       ,						lowerCamelCase_			:   List[str]=32       ,						**lowerCamelCase_			:   Any       ,						)						->   Optional[int]:
								super().__init__(**lowerCAmelCase__  )

								__magic_name__							:	Optional[Any]    							=							image_size
								__magic_name__							:	Any    							=							patch_size
								__magic_name__							:	int    							=							num_channels
								__magic_name__							:	Any    							=							embed_dim
								__magic_name__							:	Union[str, Any]    							=							depths
								__magic_name__							:	Dict    							=							len(lowerCAmelCase__  )
								__magic_name__							:	int    							=							num_heads
								__magic_name__							:	int    							=							window_size
								__magic_name__							:	List[str]    							=							mlp_ratio
								__magic_name__							:	Dict    							=							qkv_bias
								__magic_name__							:	Tuple    							=							hidden_dropout_prob
								__magic_name__							:	str    							=							attention_probs_dropout_prob
								__magic_name__							:	Any    							=							drop_path_rate
								__magic_name__							:	int    							=							hidden_act
								__magic_name__							:	List[str]    							=							use_absolute_embeddings
								__magic_name__							:	str    							=							layer_norm_eps
								__magic_name__							:	int    							=							initializer_range
								__magic_name__							:	Tuple    							=							encoder_stride
								# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__magic_name__							:	Any    							=							int(embed_dim * 2 ** (len(lowerCAmelCase__  ) - 1)  )
								__magic_name__							:	Dict    							=							(0, 0, 0, 0)









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



