



'''simple docstring'''





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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42

       # setable values
       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				None



       @classmethod
       def  A_   (     cls     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             return cls(common=lowercase     ,init_noise_sigma=lowercase     ,timesteps=lowercase)


@dataclass
class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				42


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				[e.name for e in FlaxKarrasDiffusionSchedulers]

       _lowerCamelCase			    =				42



       @property
       def  A_   (     self):

             """simple docstring"""





             return True



       @register_to_config
       def __init__(     self     ,lowercase = 1000     ,lowercase = 0.0001     ,lowercase = 0.02     ,lowercase = "linear"     ,lowercase = None     ,lowercase = "fixed_small"     ,lowercase = True     ,lowercase = "epsilon"     ,lowercase = jnp.floataa     ,):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		dtype



       def  A_   (     self     ,lowercase = None):

             """simple docstring"""





             if common is None:
                   UpperCAmelCase_  :	int				      =		CommonSchedulerState.create(self)

             # standard deviation of the initial noise distribution
             UpperCAmelCase_  :	List[str]				      =		jnp.array(1.0     ,dtype=self.dtype)

             UpperCAmelCase_  :	int				      =		jnp.arange(0     ,self.config.num_train_timesteps).round()[::-1]

             return DDPMSchedulerState.create(
                 common=lowercase     ,init_noise_sigma=lowercase     ,timesteps=lowercase     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = None):

             """simple docstring"""





             return sample



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = ()):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.config.num_train_timesteps // num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # rounding to avoid issues when num_inference_step is power of 3
             UpperCAmelCase_  :	Optional[int]				      =		(jnp.arange(0     ,lowercase) * step_ratio).round()[::-1]

             return state.replace(
                 num_inference_steps=lowercase     ,timesteps=lowercase     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase=None     ,lowercase=None):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		state.common.alphas_cumprod[t]
             UpperCAmelCase_  :	Union[str, Any]				      =		jnp.where(t > 0     ,state.common.alphas_cumprod[t - 1]     ,jnp.array(1.0     ,dtype=self.dtype))

             # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
             # and sample from it to get previous sample
             # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
             UpperCAmelCase_  :	List[str]				      =		(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

             if variance_type is None:
                   UpperCAmelCase_  :	Union[str, Any]				      =		self.config.variance_type

             # hacks - were probably added for training stability
             if variance_type == "fixed_small":
                   UpperCAmelCase_  :	List[Any]				      =		jnp.clip(lowercase     ,a_min=1E-20)
             # for rl-diffuser https://arxiv.org/abs/2205.09991
             elif variance_type == "fixed_small_log":
                   UpperCAmelCase_  :	List[Any]				      =		jnp.log(jnp.clip(lowercase     ,a_min=1E-20))
             elif variance_type == "fixed_large":
                   UpperCAmelCase_  :	int				      =		state.common.betas[t]
             elif variance_type == "fixed_large_log":
                   # Glide max_log
                   UpperCAmelCase_  :	str				      =		jnp.log(state.common.betas[t])
             elif variance_type == "learned":
                   return predicted_variance
             elif variance_type == "learned_range":
                   UpperCAmelCase_  :	str				      =		variance
                   UpperCAmelCase_  :	List[str]				      =		state.common.betas[t]
                   UpperCAmelCase_  :	List[str]				      =		(predicted_variance + 1) / 2
                   UpperCAmelCase_  :	str				      =		frac * max_log + (1 - frac) * min_log

             return variance



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		timestep

             if key is None:
                   UpperCAmelCase_  :	str				      =		jax.random.PRNGKey(0)

             if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                   UpperCAmelCase_      ,      UpperCAmelCase_  :	Dict				      =		jnp.split(lowercase     ,sample.shape[1]     ,axis=1)
             else:
                   UpperCAmelCase_  :	str				      =		None

             # 1. compute alphas, betas
             UpperCAmelCase_  :	Tuple				      =		state.common.alphas_cumprod[t]
             UpperCAmelCase_  :	Optional[Any]				      =		jnp.where(t > 0     ,state.common.alphas_cumprod[t - 1]     ,jnp.array(1.0     ,dtype=self.dtype))
             UpperCAmelCase_  :	Union[str, Any]				      =		1 - alpha_prod_t
             UpperCAmelCase_  :	List[str]				      =		1 - alpha_prod_t_prev

             # 2. compute predicted original sample from predicted noise also called
             # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
             if self.config.prediction_type == "epsilon":
                   UpperCAmelCase_  :	int				      =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             elif self.config.prediction_type == "sample":
                   UpperCAmelCase_  :	Optional[int]				      =		model_output
             elif self.config.prediction_type == "v_prediction":
                   UpperCAmelCase_  :	List[str]				      =		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             else:
                   raise ValueError(
                       F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
                       " for the FlaxDDPMScheduler.")

             # 3. Clip "predicted x_0"
             if self.config.clip_sample:
                   UpperCAmelCase_  :	Optional[Any]				      =		jnp.clip(lowercase     ,-1     ,1)

             # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
             # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
             UpperCAmelCase_  :	Tuple				      =		(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
             UpperCAmelCase_  :	str				      =		state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

             # 5. Compute predicted previous sample µ_t
             # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
             UpperCAmelCase_  :	Dict				      =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

             # 6. Add noise
             def random_variance():
                   UpperCAmelCase_  :	str				      =		jax.random.split(lowercase     ,num=1)
                   UpperCAmelCase_  :	List[str]				      =		jax.random.normal(lowercase     ,shape=model_output.shape     ,dtype=self.dtype)
                   return (self._get_variance(lowercase     ,lowercase     ,predicted_variance=lowercase) ** 0.5) * noise

             UpperCAmelCase_  :	Dict				      =		jnp.where(t > 0     ,random_variance()     ,jnp.zeros(model_output.shape     ,dtype=self.dtype))

             UpperCAmelCase_  :	List[Any]				      =		pred_prev_sample + variance

             if not return_dict:
                   return (pred_prev_sample, state)

             return FlaxDDPMSchedulerOutput(prev_sample=lowercase     ,state=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             return add_noise_common(state.common     ,lowercase     ,lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             return get_velocity_common(state.common     ,lowercase     ,lowercase     ,lowercase)



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps





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
)
__lowerCamelCase         =			logging.getLogger(__name__)







def    _snake_case (       )     ->     Dict:






      '''simple docstring'''







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

      logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""    )
      if args.tokenizer_type == "bert":
            UpperCAmelCase_  :	int				      =		BertTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase_  :	Dict				      =		tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase_  :	str				      =		RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	int				      =		tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase_  :	Tuple				      =		GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	List[Any]				      =		tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase_  :	List[str]				      =		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:
            UpperCAmelCase_  :	Union[str, Any]				      =		fp.readlines()

      logger.info("Start encoding"    )
      logger.info(F"""{len(__snake_case    )} examples to process."""    )

      UpperCAmelCase_  :	List[str]				      =		[]
      UpperCAmelCase_  :	str				      =		0
      UpperCAmelCase_  :	Union[str, Any]				      =		1_0_0_0_0
      UpperCAmelCase_  :	int				      =		time.time()
      for text in data:
            UpperCAmelCase_  :	str				      =		F"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase_  :	Any				      =		tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case    )
            rslt.append(__snake_case    )

            iter += 1
            if iter % interval == 0:
                  UpperCAmelCase_  :	List[str]				      =		time.time()
                  logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""    )
                  UpperCAmelCase_  :	Optional[Any]				      =		time.time()
      logger.info("Finished binarization"    )
      logger.info(F"""{len(__snake_case    )} examples processed."""    )

      UpperCAmelCase_  :	List[Any]				      =		F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase_  :	Optional[int]				      =		tokenizer.vocab_size
      if vocab_size < (1 << 1_6):
            UpperCAmelCase_  :	List[Any]				      =		[np.uintaa(__snake_case    ) for d in rslt]
      else:
            UpperCAmelCase_  :	str				      =		[np.intaa(__snake_case    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(F"""Dump to {dp_file}"""    )
      with open(__snake_case					,  "wb"    ) as handle:
            pickle.dump(rslt_					,  __snake_case					,  protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
   main()
