



"""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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      None
	SCREAMING_SNAKE_CASE        =      None
	SCREAMING_SNAKE_CASE        =      None
	SCREAMING_SNAKE_CASE        =      None






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			project_dim
				__lowerCAmelCase :    List[str]				=			pooler_fn
				__lowerCAmelCase :    str				=			learn_encoder
				__lowerCAmelCase :    Optional[Any]				=			use_attention_mask






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Dict)							->			List[Any]:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			XLMRobertaModel(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[Any]				=			nn.Linear(config.hidden_size			,     config.project_dim)
				__lowerCAmelCase :    str				=			getattr(_SCREAMING_SNAKE_CASE			,     "has_pre_transformation"			,     _SCREAMING_SNAKE_CASE)
				if self.has_pre_transformation:
							__lowerCAmelCase :    Optional[int]				=			nn.Linear(config.hidden_size			,     config.project_dim)
							__lowerCAmelCase :    List[Any]				=			nn.LayerNorm(config.hidden_size			,     eps=config.layer_norm_eps)
				self.post_init()







	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Tensor] = None			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     )							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			return_dict if return_dict is not None else self.config.use_return_dict

				__lowerCAmelCase :    Optional[Any]				=			self.base_model(
				    input_ids=_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     position_ids=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     inputs_embeds=_SCREAMING_SNAKE_CASE			,     encoder_hidden_states=_SCREAMING_SNAKE_CASE			,     encoder_attention_mask=_SCREAMING_SNAKE_CASE			,     output_attentions=_SCREAMING_SNAKE_CASE			,     output_hidden_states=True if self.has_pre_transformation else output_hidden_states			,     return_dict=_SCREAMING_SNAKE_CASE			,     )

				if self.has_pre_transformation:
							__lowerCAmelCase :    List[str]				=			outputs["hidden_states"][-2]
							__lowerCAmelCase :    str				=			self.pre_LN(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    List[Any]				=			self.transformation_pre(_SCREAMING_SNAKE_CASE)

							return TransformationModelOutput(
							    projection_state=_SCREAMING_SNAKE_CASE			,     last_hidden_state=outputs.last_hidden_state			,     hidden_states=outputs.hidden_states			,     attentions=outputs.attentions			,     )
				else:
							__lowerCAmelCase :    List[Any]				=			self.transformation(outputs.last_hidden_state)
							return TransformationModelOutput(
							    projection_state=_SCREAMING_SNAKE_CASE			,     last_hidden_state=outputs.last_hidden_state			,     hidden_states=outputs.hidden_states			,     attentions=outputs.attentions			,     )




"""simple docstring"""






def      _lowercase							(    __snake_case      )		->  float:
			return 10 - x * x


def      _lowercase							(    __snake_case   ,__snake_case      )		->  float:
			# Bolzano theory in order to find if there is a root between a and b
			if equation(__snake_case      ) * equation(__snake_case      ) >= 0:
						raise ValueError("Wrong space!"      )

			__lowerCAmelCase :    List[Any]				=			a
			while (b - a) >= 0.01:
						# Find middle point
						__lowerCAmelCase :    str				=			(a + b) / 2
						# Check if middle point is root
						if equation(__snake_case      ) == 0.0:
									break
						# Decide the side to repeat the steps
						if equation(__snake_case      ) * equation(__snake_case      ) < 0:
									__lowerCAmelCase :    List[Any]				=			c
						else:
									__lowerCAmelCase :    List[str]				=			c
			return c


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(bisection(-2, 5))
	print(bisection(0, 6))