
'''simple docstring'''

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 	A_			(    _snake_case	,       _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  1


     @register_to_config
     def __init__(		self			:					Optional[Any]					,      lowercase_			:					Dict=2_000					,      lowercase_			:					int=0.1					,      lowercase_			:					str=20					,      lowercase_			:					Optional[Any]=1E-3	)						->     Tuple:
      UpperCAmelCase	:     Tuple						 =			None
      UpperCAmelCase	:     List[str]						 =			None
      UpperCAmelCase	:     Optional[int]						 =			None


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					List[Any]					,      lowercase_			:					str = None	)						->     str:
      UpperCAmelCase	:     Dict						 =			torch.linspace(1					,      self.config.sampling_eps					,      UpperCamelCase__					,      device=UpperCamelCase__	)


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					str					,      lowercase_			:					Any					,      lowercase_			:					str					,      lowercase_			:					Optional[Any]=None	)						->     Any:
      if self.timesteps is None:
       raise ValueError(
           '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'	)

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      UpperCAmelCase	:     Dict						 =			(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      UpperCAmelCase	:     List[str]						 =			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff	)	)
      UpperCAmelCase	:     Tuple						 =			std.flatten()
      while len(std.shape	) < len(score.shape	):
       UpperCAmelCase	:     Optional[int]						 =			std.unsqueeze(-1	)
      UpperCAmelCase	:     List[str]						 =			-score / std

      # compute
      UpperCAmelCase	:     Tuple						 =			-1.0 / len(self.timesteps	)

      UpperCAmelCase	:     List[Any]						 =			self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      UpperCAmelCase	:     Optional[int]						 =			beta_t.flatten()
      while len(beta_t.shape	) < len(x.shape	):
       UpperCAmelCase	:     List[str]						 =			beta_t.unsqueeze(-1	)
      UpperCAmelCase	:     Union[str, Any]						 =			-0.5 * beta_t * x

      UpperCAmelCase	:     Optional[Any]						 =			torch.sqrt(UpperCamelCase__	)
      UpperCAmelCase	:     str						 =			drift - diffusion**2 * score
      UpperCAmelCase	:     Union[str, Any]						 =			x + drift * dt

      # add noise
      UpperCAmelCase	:     Any						 =			randn_tensor(x.shape					,      layout=x.layout					,      generator=UpperCamelCase__					,      device=x.device					,      dtype=x.dtype	)
      UpperCAmelCase	:     Tuple						 =			x_mean + diffusion * math.sqrt(-dt	) * noise

      return x, x_mean




     def __len__(		self			:					Optional[int]	)						->     Optional[Any]:
      return self.config.num_train_timesteps







'''simple docstring'''

import argparse
import json

from tqdm import tqdm




def 				UpperCamelCase(					):
 UpperCAmelCase	:     List[Any]						 =			argparse.ArgumentParser()

 # Required parameters
 parser.add_argument(
     '--src_path'  ,	type=UpperCAmelCase_  ,	default='biencoder-nq-dev.json'  ,	help='Path to raw DPR training data'  ,	)
 parser.add_argument(
     '--evaluation_set'  ,	type=UpperCAmelCase_  ,	help='where to store parsed evaluation_set file'  ,	)
 parser.add_argument(
     '--gold_data_path'  ,	type=UpperCAmelCase_  ,	help='where to store parsed gold_data_path file'  ,	)
 UpperCAmelCase	:     int						 =			parser.parse_args()

 with open(args.src_path  ,	'r'     ) as src_file, open(args.evaluation_set  ,	'w'     ) as eval_file, open(
     args.gold_data_path  ,	'w'     ) as gold_file:
  UpperCAmelCase	:     int						 =			json.load(UpperCAmelCase_     )
  for dpr_record in tqdm(UpperCAmelCase_     ):
   UpperCAmelCase	:     Any						 =			dpr_record['question']
   UpperCAmelCase	:     List[str]						 =			[context['title'] for context in dpr_record['positive_ctxs']]
   eval_file.write(question + '\n'     )
   gold_file.write('\t'.join(UpperCAmelCase_     ) + '\n'     )


if __name__ == "__main__":
 main()




