



"""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        _UpperCAmelCase       (			UpperCAmelCase_):
 _lowerCAmelCase   :			Optional[torch.FloatTensor]							=		None
 _lowerCAmelCase   :			torch.FloatTensor							=		None
 _lowerCAmelCase   :			Optional[Tuple[torch.FloatTensor]]							=		None
 _lowerCAmelCase   :			Optional[Tuple[torch.FloatTensor]]							=		None





class        _UpperCAmelCase       (			UpperCAmelCase_):

 def __init__(     self		:       Union[str, Any]  ,     lowercase_		:       Tuple=1  ,     lowercase_		:       str=0  ,     lowercase_		:       str=2  ,     lowercase_		:       Union[str, Any]=512  ,     lowercase_		:       List[str]="cls"  ,     lowercase_		:       Optional[int]=False  ,     lowercase_		:       str=True  ,     **lowercase_		:       str  ,     ):
     super().__init__(pad_token_id=__UpperCAmelCase  ,     bos_token_id=__UpperCAmelCase  ,     eos_token_id=__UpperCAmelCase  ,     **__UpperCAmelCase				)
     snake_case_ :     Tuple				=	project_dim
     snake_case_ :     Union[str, Any]				=	pooler_fn
     snake_case_ :     List[str]				=	learn_encoder
     snake_case_ :     Any				=	use_attention_mask





class        _UpperCAmelCase       (			UpperCAmelCase_):
 _lowerCAmelCase   :			Tuple							=		[r'pooler', r'logit_scale']
 _lowerCAmelCase   :			int							=		[r'position_ids', r'predictions.decoder.bias']
 _lowerCAmelCase   :			Any							=		'roberta'
 _lowerCAmelCase   :			List[str]							=		RobertaSeriesConfig

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






 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[torch.Tensor] = None  ,     lowercase_		:       Optional[bool] = None  ,     lowercase_		:       Optional[bool] = None  ,     lowercase_		:       Optional[bool] = None  ,     ):
     snake_case_ :     List[str]				=	return_dict if return_dict is not None else self.config.use_return_dict

     snake_case_ :     Tuple				=	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_ :     Dict				=	outputs['''hidden_states'''][-2]
         snake_case_ :     Optional[Any]				=	self.pre_LN(__UpperCAmelCase				)
         snake_case_ :     Optional[int]				=	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_ :     Any				=	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  ,     )






from __future__ import annotations

from scipy.special import comb  # type: ignore





class      A							:


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						list[tuple[float, float]]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  list_of_points
         # Degree determines the flexibility of the curve.
         # Degree = 1 will produce a straight line.
         UpperCAmelCase__								=  len(__UpperCAmelCase		) - 1


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						float		)						->				list[float]:
         """simple docstring"""



         assert 0 <= t <= 1, "Time t must be between 0 and 1."
         UpperCAmelCase__								=  []
         for i in range(len(self.list_of_points		)		):
               # basis function for each i
               output_values.append(
                   comb(self.degree       ,  __UpperCAmelCase		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
         # the basis must sum up to 1 for it to produce a valid Bezier curve.
         assert round(sum(__UpperCAmelCase		)       ,  5		) == 1
         return output_values


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						float		)						->				tuple[float, float]:
         """simple docstring"""



         assert 0 <= t <= 1, "Time t must be between 0 and 1."

         UpperCAmelCase__								=  self.basis_function(__UpperCAmelCase		)
         UpperCAmelCase__								=  0.0
         UpperCAmelCase__								=  0.0
         for i in range(len(self.list_of_points		)		):
               # For all points, sum up the product of i-th basis function and i-th point.
               x += basis_function[i] * self.list_of_points[i][0]
               y += basis_function[i] * self.list_of_points[i][1]
         return (x, y)





   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						float = 0.01		)						->				Optional[int]:
         """simple docstring"""



         from matplotlib import pyplot as plt  # type: ignore

         UpperCAmelCase__								=  []  # x coordinates of points to plot
         UpperCAmelCase__								=  []  # y coordinates of points to plot

         UpperCAmelCase__								=  0.0
         while t <= 1:
               UpperCAmelCase__								=  self.bezier_curve_function(__UpperCAmelCase		)
               to_plot_x.append(value[0]		)
               to_plot_y.append(value[1]		)
               t += step_size

         UpperCAmelCase__								=  [i[0] for i in self.list_of_points]
         UpperCAmelCase__								=  [i[1] for i in self.list_of_points]

         plt.plot(
             __UpperCAmelCase       ,  __UpperCAmelCase       ,  color="blue"       ,  label="Curve of Degree " + str(self.degree		)       ,  )
         plt.scatter(__UpperCAmelCase       ,  __UpperCAmelCase       ,  color="red"       ,  label="Control Points"		)
         plt.legend()
         plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
      BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
      BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



