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   lowercase      (       a       ):
       lowercase__  :    Optional[torch.FloatTensor]									=							None
       lowercase__  :    torch.FloatTensor									=							None
       lowercase__  :    Optional[Tuple[torch.FloatTensor]]									=							None
       lowercase__  :    Optional[Tuple[torch.FloatTensor]]									=							None



class   lowercase      (       a       ):




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



            '''simple docstring'''

            super().__init__(pad_token_id=_UpperCamelCase		,							bos_token_id=_UpperCamelCase		,							eos_token_id=_UpperCamelCase		,							**_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    project_dim
            SCREAMING_SNAKE_CASE           =    pooler_fn
            SCREAMING_SNAKE_CASE           =    learn_encoder
            SCREAMING_SNAKE_CASE           =    use_attention_mask



class   lowercase      (       a       ):
       lowercase__  :    Dict									=							[R"""pooler""", R"""logit_scale"""]
       lowercase__  :    Any									=							[R"""position_ids""", R"""predictions.decoder.bias"""]
       lowercase__  :    Optional[int]									=							"""roberta"""
       lowercase__  :    Union[str, Any]									=							RobertaSeriesConfig




       def __init__(  self				:   List[str]		,							_UpperCamelCase				:   List[Any] )   ->			Optional[int]:



            '''simple docstring'''

            super().__init__(_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    XLMRobertaModel(_UpperCamelCase )
            SCREAMING_SNAKE_CASE           =    nn.Linear(config.hidden_size		,							config.project_dim )
            SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							"has_pre_transformation"		,							_UpperCamelCase )
            if self.has_pre_transformation:
                 SCREAMING_SNAKE_CASE           =    nn.Linear(config.hidden_size		,							config.project_dim )
                 SCREAMING_SNAKE_CASE           =    nn.LayerNorm(config.hidden_size		,							eps=config.layer_norm_eps )
            self.post_init()





       def 		__snake_case(  self				:   Dict		,							_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		,							)   ->			Tuple:



            '''simple docstring'''

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

            SCREAMING_SNAKE_CASE           =    self.base_model(
                input_ids=_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							token_type_ids=_UpperCamelCase		,							position_ids=_UpperCamelCase		,							head_mask=_UpperCamelCase		,							inputs_embeds=_UpperCamelCase		,							encoder_hidden_states=_UpperCamelCase		,							encoder_attention_mask=_UpperCamelCase		,							output_attentions=_UpperCamelCase		,							output_hidden_states=True if self.has_pre_transformation else output_hidden_states		,							return_dict=_UpperCamelCase		,							)

            if self.has_pre_transformation:
                 SCREAMING_SNAKE_CASE           =    outputs["hidden_states"][-2]
                 SCREAMING_SNAKE_CASE           =    self.pre_LN(_UpperCamelCase )
                 SCREAMING_SNAKE_CASE           =    self.transformation_pre(_UpperCamelCase )

                 return TransformationModelOutput(
                     projection_state=_UpperCamelCase		,							last_hidden_state=outputs.last_hidden_state		,							hidden_states=outputs.hidden_states		,							attentions=outputs.attentions		,							)
            else:
                 SCREAMING_SNAKE_CASE           =    self.transformation(outputs.last_hidden_state )
                 return TransformationModelOutput(
                     projection_state=_UpperCamelCase		,							last_hidden_state=outputs.last_hidden_state		,							hidden_states=outputs.hidden_states		,							attentions=outputs.attentions		,							)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					while b:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    b, a % b
					return a





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__ ,	a % b					)





def 		__lowerCamelCase							():
					print(F"euclidean_gcd(3, 5) = {euclidean_gcd(3 ,	5					)}"					)
					print(F"euclidean_gcd(5, 3) = {euclidean_gcd(5 ,	3					)}"					)
					print(F"euclidean_gcd(1, 3) = {euclidean_gcd(1 ,	3					)}"					)
					print(F"euclidean_gcd(3, 6) = {euclidean_gcd(3 ,	6					)}"					)
					print(F"euclidean_gcd(6, 3) = {euclidean_gcd(6 ,	3					)}"					)

					print(F"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,	5					)}"					)
					print(F"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,	6					)}"					)
					print(F"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,	3					)}"					)


if __name__ == "__main__":
							main()




