





'''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     (					__magic_name__							):
	__lowerCAmelCase       						=  None
	__lowerCAmelCase       						=  None
	__lowerCAmelCase       						=  None
	__lowerCAmelCase       						=  None






class 				__snake_case     (					__magic_name__							):





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






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  [r'''pooler''', r'''logit_scale''']
	__lowerCAmelCase       						=  [r'''position_ids''', r'''predictions.decoder.bias''']
	__lowerCAmelCase       						=  '''roberta'''
	__lowerCAmelCase       						=  RobertaSeriesConfig





	def __init__(		self      ,       UpperCamelCase_		)      ->		List[Any]:
		super().__init__(UpperCamelCase_		)
		snake_case__	       =   XLMRobertaModel(UpperCamelCase_		)
		snake_case__	       =   nn.Linear(config.hidden_size      ,       config.project_dim		)
		snake_case__	       =   getattr(UpperCamelCase_      ,       'has_pre_transformation'      ,       UpperCamelCase_		)
		if self.has_pre_transformation:
			snake_case__	       =   nn.Linear(config.hidden_size      ,       config.project_dim		)
			snake_case__	       =   nn.LayerNorm(config.hidden_size      ,       eps=config.layer_norm_eps		)
		self.post_init()





	def   _snake_case   (		self      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       )      ->		Optional[Any]:
		snake_case__	       =   return_dict if return_dict is not None else self.config.use_return_dict

		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:
			snake_case__	       =   outputs['hidden_states'][-2]
			snake_case__	       =   self.pre_LN(UpperCamelCase_		)
			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:
			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      ,       )










'''simple docstring'''




import enum
import shutil
import sys


a__      ,       a__					:			Any          =     shutil.get_terminal_size()

a__					:			Optional[int]          =     {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}






class 				__snake_case     (					enum.Enum							):
	__lowerCAmelCase       						=  0
	__lowerCAmelCase       						=  1





def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->Optional[Any]:
	sys.stdout.write(str(UpperCAmelCase_							) + end							)
	sys.stdout.flush()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->List[str]:
	forceWrite(f'''\u001b[{color}m{content}\u001b[0m'''					,				UpperCAmelCase_							)




def 						__lowerCamelCase			(	)   ->Optional[Any]:
	forceWrite('\r'							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Any:
	forceWrite(f'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''							)




def 						__lowerCamelCase			(	)   ->str:
	forceWrite(' ' * TERMINAL_WIDTH							)
	reset_cursor()







def 						__lowerCamelCase			(	)   ->Tuple:
	reset_cursor()
	forceWrite('-' * TERMINAL_WIDTH							)



