

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

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

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



@dataclass
class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      torch.FloatTensor
  __lowerCamelCase			:      torch.FloatTensor







class 						_A     (				snake_case						,    snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      List[str]					=   1





  @register_to_config
  def __init__(  self   ,SCREAMING_SNAKE_CASE_ = 2000   ,SCREAMING_SNAKE_CASE_ = 0.15   ,SCREAMING_SNAKE_CASE_ = 0.01   ,SCREAMING_SNAKE_CASE_ = 13_48.0   ,SCREAMING_SNAKE_CASE_ = 1E-5   ,SCREAMING_SNAKE_CASE_ = 1   ,):

      '''simple docstring'''
      # standard deviation of the initial noise distribution
      snake_case							:		Union[str, Any]        =		sigma_max

      # setable values
      snake_case							:		Any        =		None

      self.set_sigmas(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      return sample





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		Any        =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

      snake_case							:		str        =		torch.linspace(1   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,device=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		sigma_min if sigma_min is not None else self.config.sigma_min
      snake_case							:		str        =		sigma_max if sigma_max is not None else self.config.sigma_max
      snake_case							:		Any        =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if self.timesteps is None:
          self.set_timesteps(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		str        =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
      snake_case							:		List[Any]        =		torch.exp(torch.linspace(math.log(SCREAMING_SNAKE_CASE_    )   ,math.log(SCREAMING_SNAKE_CASE_    )   ,SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		Optional[Any]        =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return torch.where(
          timesteps == 0   ,torch.zeros_like(t.to(timesteps.device    )    )   ,self.discrete_sigmas[timesteps - 1].to(timesteps.device    )   ,)





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = True   ,):

      '''simple docstring'''
      if self.timesteps is None:
          raise ValueError(
              """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

      snake_case							:		Optional[Any]        =		timestep * torch.ones(
          sample.shape[0]   ,device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
      snake_case							:		List[str]        =		(timestep * (len(self.timesteps    ) - 1)).long()

      # mps requires indices to be in the same device, so we use cpu as is the default with cuda
      snake_case							:		Dict        =		timesteps.to(self.discrete_sigmas.device    )

      snake_case							:		Any        =		self.discrete_sigmas[timesteps].to(sample.device    )
      snake_case							:		str        =		self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ).to(sample.device    )
      snake_case							:		Tuple        =		torch.zeros_like(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      snake_case							:		Tuple        =		diffusion.flatten()
      while len(diffusion.shape    ) < len(sample.shape    ):
          snake_case							:		Dict        =		diffusion.unsqueeze(-1    )
      snake_case							:		List[Any]        =		drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      snake_case							:		Tuple        =		randn_tensor(
          sample.shape   ,layout=sample.layout   ,generator=SCREAMING_SNAKE_CASE_   ,device=sample.device   ,dtype=sample.dtype    )
      snake_case							:		Tuple        =		sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      snake_case							:		List[str]        =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
          return (prev_sample, prev_sample_mean)

      return SdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_   ,prev_sample_mean=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = True   ,):

      '''simple docstring'''
      if self.timesteps is None:
          raise ValueError(
              """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      snake_case							:		Dict        =		randn_tensor(sample.shape   ,layout=sample.layout   ,generator=SCREAMING_SNAKE_CASE_    ).to(sample.device    )

      # compute step size from the model_output, the noise, and the snr
      snake_case							:		List[str]        =		torch.norm(model_output.reshape(model_output.shape[0]   ,-1    )   ,dim=-1    ).mean()
      snake_case							:		List[Any]        =		torch.norm(noise.reshape(noise.shape[0]   ,-1    )   ,dim=-1    ).mean()
      snake_case							:		str        =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      snake_case							:		int        =		step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
      # self.repeat_scalar(step_size, sample.shape[0])

      # compute corrected sample: model_output term and noise term
      snake_case							:		Optional[int]        =		step_size.flatten()
      while len(step_size.shape    ) < len(sample.shape    ):
          snake_case							:		Union[str, Any]        =		step_size.unsqueeze(-1    )
      snake_case							:		Optional[Any]        =		sample + step_size * model_output
      snake_case							:		Any        =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
          return (prev_sample,)

      return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      snake_case							:		Optional[Any]        =		timesteps.to(original_samples.device    )
      snake_case							:		Any        =		self.discrete_sigmas.to(original_samples.device    )[timesteps]
      snake_case							:		Tuple        =		(
          noise * sigmas[:, None, None, None]
          if noise is not None
          else torch.randn_like(SCREAMING_SNAKE_CASE_    ) * sigmas[:, None, None, None]
      )
      snake_case							:		Optional[Any]        =		noise + original_samples
      return noisy_samples





  def __len__(  self    ):

      '''simple docstring'''
      return self.config.num_train_timesteps










"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


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

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser(
     description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
 parser.add_argument('--file_path'  ,     type=_UpperCAmelCase  ,     default='data/dump.txt'  ,     help='The path to the data.'			)
 parser.add_argument('--tokenizer_type'  ,     type=_UpperCAmelCase  ,     default='bert'  ,     choices=['bert', 'roberta', 'gpt2']			)
 parser.add_argument('--tokenizer_name'  ,     type=_UpperCAmelCase  ,     default='bert-base-uncased'  ,     help='The tokenizer to use.'			)
 parser.add_argument('--dump_file'  ,     type=_UpperCAmelCase  ,     default='data/dump'  ,     help='The dump file prefix.'			)
 lowerCAmelCase    				= parser.parse_args()

 logger.info(F'Loading Tokenizer ({args.tokenizer_name})'			)
 if args.tokenizer_type == "bert":
  lowerCAmelCase    				= BertTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  lowerCAmelCase    				= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `<s>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  lowerCAmelCase    				= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

 logger.info(F'Loading text from {args.file_path}'			)
 with open(args.file_path  ,     'r'  ,     encoding='utf8'			) as fp:
  lowerCAmelCase    				= fp.readlines()

 logger.info('Start encoding'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples to process.'			)

 lowerCAmelCase    				= []
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 1_0000
 lowerCAmelCase    				= time.time()
 for text in data:
  lowerCAmelCase    				= F'{bos} {text.strip()} {sep}'
  lowerCAmelCase    				= tokenizer.encode(_UpperCAmelCase  ,     add_special_tokens=_UpperCAmelCase			)
  rslt.append(_UpperCAmelCase			)

  iter += 1
  if iter % interval == 0:
   lowerCAmelCase    				= time.time()
   logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
   lowerCAmelCase    				= time.time()
 logger.info('Finished binarization'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples processed.'			)

 lowerCAmelCase    				= F'{args.dump_file}.{args.tokenizer_name}.pickle'
 lowerCAmelCase    				= tokenizer.vocab_size
 if vocab_size < (1 << 16):
  lowerCAmelCase    				= [np.uintaa(_UpperCAmelCase			) for d in rslt]
 else:
  lowerCAmelCase    				= [np.intaa(_UpperCAmelCase			) for d in rslt]
 random.shuffle(rslt_			)
 logger.info(F'Dump to {dp_file}'			)
 with open(_UpperCAmelCase  ,     'wb'			) as handle:
  pickle.dump(rslt_  ,     _UpperCAmelCase  ,     protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
       main()





