



"""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 	__snake_case	(     _lowercase):
  snake_case__    :       Optional[int]        =						None
  snake_case__    :       Optional[Any]        =						None
  snake_case__    :       str        =						None
  snake_case__    :       int        =						None






class 	__snake_case	(     _lowercase):




  def __init__(			self					:						Dict						, __lowerCAmelCase					:						List[str]=1						, __lowerCAmelCase					:						str=0						, __lowerCAmelCase					:						Union[str, Any]=2						, __lowerCAmelCase					:						List[Any]=5_1_2						, __lowerCAmelCase					:						Optional[int]="cls"						, __lowerCAmelCase					:						str=False						, __lowerCAmelCase					:						List[str]=True						, **__lowerCAmelCase					:						str						, ):






         """simple docstring"""






         super().__init__(pad_token_id=lowercase_						, bos_token_id=lowercase_						, eos_token_id=lowercase_						, **lowercase_		)
         _lowerCamelCase						:    Optional[Any]         =			project_dim
         _lowerCamelCase						:    List[str]         =			pooler_fn
         _lowerCamelCase						:    Dict         =			learn_encoder
         _lowerCamelCase						:    int         =			use_attention_mask






class 	__snake_case	(     _lowercase):
  snake_case__    :       int        =						[R"pooler", R"logit_scale"]
  snake_case__    :       List[Any]        =						[R"position_ids", R"predictions.decoder.bias"]
  snake_case__    :       Dict        =						"roberta"
  snake_case__    :       Optional[Any]        =						RobertaSeriesConfig




  def __init__(			self					:						Optional[Any]						, __lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         super().__init__(lowercase_		)
         _lowerCamelCase						:    Any         =			XLMRobertaModel(lowercase_		)
         _lowerCamelCase						:    Union[str, Any]         =			nn.Linear(config.hidden_size						, config.project_dim		)
         _lowerCamelCase						:    Any         =			getattr(lowercase_						, '''has_pre_transformation'''						, lowercase_		)
         if self.has_pre_transformation:
                _lowerCamelCase						:    Dict         =			nn.Linear(config.hidden_size						, config.project_dim		)
                _lowerCamelCase						:    Optional[Any]         =			nn.LayerNorm(config.hidden_size						, eps=config.layer_norm_eps		)
         self.post_init()




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __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						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			return_dict if return_dict is not None else self.config.use_return_dict

         _lowerCamelCase						:    Dict         =			self.base_model(
             input_ids=lowercase_						, attention_mask=lowercase_						, token_type_ids=lowercase_						, position_ids=lowercase_						, head_mask=lowercase_						, inputs_embeds=lowercase_						, encoder_hidden_states=lowercase_						, encoder_attention_mask=lowercase_						, output_attentions=lowercase_						, output_hidden_states=True if self.has_pre_transformation else output_hidden_states						, return_dict=lowercase_						, )

         if self.has_pre_transformation:
                _lowerCamelCase						:    Optional[Any]         =			outputs['''hidden_states'''][-2]
                _lowerCamelCase						:    Any         =			self.pre_LN(lowercase_		)
                _lowerCamelCase						:    Any         =			self.transformation_pre(lowercase_		)

                return TransformationModelOutput(
                    projection_state=lowercase_						, last_hidden_state=outputs.last_hidden_state						, hidden_states=outputs.hidden_states						, attentions=outputs.attentions						, )
         else:
                _lowerCamelCase						:    Dict         =			self.transformation(outputs.last_hidden_state		)
                return TransformationModelOutput(
                    projection_state=lowercase_						, last_hidden_state=outputs.last_hidden_state						, hidden_states=outputs.hidden_states						, attentions=outputs.attentions						, )







"""simple docstring"""






from maths.prime_factors import prime_factors






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       if not isinstance(A_,    A_						):
              _lowerCamelCase						:    str         =			F'''Input value of [number={number}] must be an integer'''
              raise TypeError(A_						)
       if number < 1:
              raise ValueError('''Input must be a positive integer'''						)
       return -1 if len(prime_factors(A_						)						) % 2 else 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()


