




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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					(       _A     ,       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Dict       		=  1
     @register_to_config
     def __init__(     self       :      List[Any] ,   __snake_case       :      List[Any]=20_00 ,   __snake_case       :      Tuple=0.1 ,   __snake_case       :      Union[str, Any]=20 ,   __snake_case       :      Dict=1E-3    ):
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Union[str, torch.device] = None    ):
         UpperCAmelCase_										=					torch.linspace(1 ,   self.config.sampling_eps ,   __snake_case ,   device=__snake_case    )
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Dict ,   __snake_case       :      List[Any]=None    ):
         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_										=					(
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         UpperCAmelCase_										=					torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
         UpperCAmelCase_										=					std.flatten()
         while len(std.shape    ) < len(score.shape    ):
             UpperCAmelCase_										=					std.unsqueeze(-1    )
         UpperCAmelCase_										=					-score / std

         # compute
         UpperCAmelCase_										=					-1.0 / len(self.timesteps    )

         UpperCAmelCase_										=					self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         UpperCAmelCase_										=					beta_t.flatten()
         while len(beta_t.shape    ) < len(x.shape    ):
             UpperCAmelCase_										=					beta_t.unsqueeze(-1    )
         UpperCAmelCase_										=					-0.5 * beta_t * x

         UpperCAmelCase_										=					torch.sqrt(__snake_case    )
         UpperCAmelCase_										=					drift - diffusion**2 * score
         UpperCAmelCase_										=					x + drift * dt

         # add noise
         UpperCAmelCase_										=					randn_tensor(x.shape ,   layout=x.layout ,   generator=__snake_case ,   device=x.device ,   dtype=x.dtype    )
         UpperCAmelCase_										=					x_mean + diffusion * math.sqrt(-dt    ) * noise

         return x, x_mean




     def __len__(     self       :      Dict    ):
         return self.config.num_train_timesteps











import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase      			=						logging.getLogger(__name__)

if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser(
     description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
 )
 parser.add_argument(
     '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
 )
 parser.add_argument(
     '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
 )
 parser.add_argument('--vocab_size', default=3_05_22, type=int)
 _lowerCamelCase      			=						parser.parse_args()

 logger.info(F"Loading data from {args.data_file}")
 with open(args.data_file, 'rb') as fp:
  _lowerCamelCase      			=						pickle.load(fp)

 logger.info('Counting occurrences for MLM.')
 _lowerCamelCase      			=						Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 _lowerCamelCase      			=						[0] * args.vocab_size
 for k, v in counter.items():
  _lowerCamelCase      			=						v

 logger.info(F"Dump to {args.token_counts_dump}")
 with open(args.token_counts_dump, 'wb') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





