


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput




class    UpperCAmelCase					(       __snake_case      , __snake_case    ):
			lowercase						= 1




			@register_to_config
			def __init__(						self							:		List[Any]       ,       __magic_name__							:		int = 1_0_0_0       ,       __magic_name__							:		Optional[Union[np.ndarray, List[float]]] = None						):







						"""simple docstring"""







						self.set_timesteps(__magic_name__						)

						# standard deviation of the initial noise distribution
						UpperCamelCase         =					1.0

						# For now we only support F-PNDM, i.e. the runge-kutta method
						# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
						# mainly at formula (9), (12), (13) and the Algorithm 2.
						UpperCamelCase         =					4

						# running values
						UpperCamelCase         =					[]




			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		int       ,       __magic_name__							:		Union[str, torch.device] = None						):







						"""simple docstring"""







						UpperCamelCase         =					num_inference_steps
						UpperCamelCase         =					torch.linspace(1       ,       0       ,       num_inference_steps + 1						)[:-1]
						UpperCamelCase         =					torch.cat([steps, torch.tensor([0.0]						)]						)

						if self.config.trained_betas is not None:
									UpperCamelCase         =					torch.tensor(self.config.trained_betas       ,       dtype=torch.floataa						)
						else:
									UpperCamelCase         =					torch.sin(steps * math.pi / 2						) ** 2

						UpperCamelCase         =					(1.0 - self.betas**2) ** 0.5

						UpperCamelCase         =					(torch.atana(self.betas       ,       self.alphas						) / math.pi * 2)[:-1]
						UpperCamelCase         =					timesteps.to(__magic_name__						)

						UpperCamelCase         =					[]




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		torch.FloatTensor       ,       __magic_name__							:		int       ,       __magic_name__							:		torch.FloatTensor       ,       __magic_name__							:		bool = True       ,       ):







						"""simple docstring"""







						if self.num_inference_steps is None:
									raise ValueError(
									    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

						UpperCamelCase         =					(self.timesteps == timestep).nonzero().item()
						UpperCamelCase         =					timestep_index + 1

						UpperCamelCase         =					sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
						self.ets.append(__magic_name__						)

						if len(self.ets						) == 1:
									UpperCamelCase         =					self.ets[-1]
						elif len(self.ets						) == 2:
									UpperCamelCase         =					(3 * self.ets[-1] - self.ets[-2]) / 2
						elif len(self.ets						) == 3:
									UpperCamelCase         =					(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
						else:
									UpperCamelCase         =					(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

						UpperCamelCase         =					self._get_prev_sample(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__						)

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		torch.FloatTensor       ,       *__magic_name__							:		Optional[int]       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return sample




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					self.alphas[timestep_index]
						UpperCamelCase         =					self.betas[timestep_index]

						UpperCamelCase         =					self.alphas[prev_timestep_index]
						UpperCamelCase         =					self.betas[prev_timestep_index]

						UpperCamelCase         =					(sample - sigma * ets) / max(__magic_name__       ,       1e-8						)
						UpperCamelCase         =					next_alpha * pred + ets * next_sigma

						return prev_sample





			def __len__(						self							:		Tuple						):







						"""simple docstring"""







						return self.config.num_train_timesteps








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







						return self.softmax(T * self.cos(__magic_name__       ,       __magic_name__						)						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




