





"""simple docstring"""



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        _SCREAMING_SNAKE_CASE  (  _snake_case							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					None
				SCREAMING_SNAKE_CASE_:							str							=					None
				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					None
				SCREAMING_SNAKE_CASE_:							Tuple							=					None






class        _SCREAMING_SNAKE_CASE  (  _snake_case							):






				'''simple docstring'''



				def __init__(					self	:			int				,     UpperCAmelCase_	:			Dict=1				,     UpperCAmelCase_	:			Optional[int]=0				,     UpperCAmelCase_	:			str=2				,     UpperCAmelCase_	:			List[Any]=512				,     UpperCAmelCase_	:			Dict="cls"				,     UpperCAmelCase_	:			Tuple=False				,     UpperCAmelCase_	:			List[Any]=True				,     **UpperCAmelCase_	:			int				,     )		->  Optional[Any]:







											"""simple docstring"""




											super().__init__(pad_token_id=lowerCAmelCase__				,     bos_token_id=lowerCAmelCase__				,     eos_token_id=lowerCAmelCase__				,     **lowerCAmelCase__  )
											_lowerCAmelCase  	=			project_dim
											_lowerCAmelCase  	=			pooler_fn
											_lowerCAmelCase  	=			learn_encoder
											_lowerCAmelCase  	=			use_attention_mask






class        _SCREAMING_SNAKE_CASE  (  _snake_case							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					[r"pooler", r"logit_scale"]
				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					[r"position_ids", r"predictions.decoder.bias"]
				SCREAMING_SNAKE_CASE_:							str							=					"roberta"
				SCREAMING_SNAKE_CASE_:							int							=					RobertaSeriesConfig


				def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			Tuple  )		->  Union[str, Any]:







											"""simple docstring"""




											super().__init__(lowerCAmelCase__  )
											_lowerCAmelCase  	=			XLMRobertaModel(lowerCAmelCase__  )
											_lowerCAmelCase  	=			nn.Linear(config.hidden_size				,     config.project_dim  )
											_lowerCAmelCase  	=			getattr(lowerCAmelCase__				,     'has_pre_transformation'				,     lowerCAmelCase__  )
											if self.has_pre_transformation:
																		_lowerCAmelCase  	=			nn.Linear(config.hidden_size				,     config.project_dim  )
																		_lowerCAmelCase  	=			nn.LayerNorm(config.hidden_size				,     eps=config.layer_norm_eps  )
											self.post_init()







				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[torch.Tensor] = None				,     UpperCAmelCase_	:			Optional[bool] = None				,     UpperCAmelCase_	:			Optional[bool] = None				,     UpperCAmelCase_	:			Optional[bool] = None				,     )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			return_dict if return_dict is not None else self.config.use_return_dict

											_lowerCAmelCase  	=			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:
																		_lowerCAmelCase  	=			outputs['hidden_states'][-2]
																		_lowerCAmelCase  	=			self.pre_LN(lowerCAmelCase__  )
																		_lowerCAmelCase  	=			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:
																		_lowerCAmelCase  	=			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				,     )








"""simple docstring"""



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					field(default="image-classification"      ,				metadata={"include_in_asdict_even_if_is_default": True}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"image": Image()}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"labels": ClassLabel}							)
				SCREAMING_SNAKE_CASE_:							str							=					"image"
				SCREAMING_SNAKE_CASE_:							str							=					"labels"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											if self.label_column not in features:
																		raise ValueError(F"""Column {self.label_column} is not present in features."""  )
											if not isinstance(features[self.label_column]				,     UpperCAmelCase_  ):
																		raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
											_lowerCAmelCase  	=			copy.deepcopy(self  )
											_lowerCAmelCase  	=			self.label_schema.copy()
											_lowerCAmelCase  	=			features[self.label_column]
											_lowerCAmelCase  	=			label_schema
											return task_template







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict[str, str]:







											"""simple docstring"""




											return {
											    self.image_column: "image",
											    self.label_column: "labels",
											}






