





"""simple docstring"""






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

import numpy as np
import torch

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



@dataclass
class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	42
       lowerCAmelCase__    			=	42


def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=0.999,SCREAMING_SNAKE_CASE="cosine",)		->							Any:






       """simple docstring"""




       if alpha_transform_type == "cosine":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE				):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE				):
                     return math.exp(t * -12.0				)

       else:
              raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

       _UpperCAmelCase			     =  []
       for i in range(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  i / num_diffusion_timesteps
              _UpperCAmelCase			     =  (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE				) / alpha_bar_fn(SCREAMING_SNAKE_CASE				),SCREAMING_SNAKE_CASE				)				)
       return torch.tensor(SCREAMING_SNAKE_CASE,dtype=torch.floataa				)



class 						lowerCAmelCase       (   snake_case      ,						snake_case				):
       lowerCAmelCase__    			=	1






       @register_to_config
       def __init__( self     ,	a__ = 10_00     ,	a__ = 0.0_001     ,	a__ = 0.02     ,	a__ = "linear"     ,	a__ = 0.0     ,	a__ = None     ,	a__ = True     ,	):
              if trained_betas is not None:
                     _UpperCAmelCase			     =  torch.from_numpy(a__  )
              elif beta_schedule == "linear":
                     _UpperCAmelCase			     =  torch.linspace(a__     ,	a__     ,	a__     ,	dtype=torch.floataa  )
              elif beta_schedule == "scaled_linear":
                     # this schedule is very specific to the latent diffusion model.
                     _UpperCAmelCase			     =  (
                         torch.linspace(beta_start**0.5     ,	beta_end**0.5     ,	a__     ,	dtype=torch.floataa  ) ** 2
                     )
              elif beta_schedule == "squaredcos_cap_v2":
                     # Glide cosine schedule
                     _UpperCAmelCase			     =  betas_for_alpha_bar(a__  )
              elif beta_schedule == "sigmoid":
                     # GeoDiff sigmoid schedule
                     _UpperCAmelCase			     =  torch.linspace(-6     ,	6     ,	a__  )
                     _UpperCAmelCase			     =  torch.sigmoid(a__  ) * (beta_end - beta_start) + beta_start
              else:
                     raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""  )

              _UpperCAmelCase			     =  1.0 - self.betas
              _UpperCAmelCase			     =  torch.cumprod(self.alphas     ,	dim=0  )
              _UpperCAmelCase			     =  torch.tensor(1.0  )

              _UpperCAmelCase			     =  torch.tensor(1.0  )

              # standard deviation of the initial noise distribution
              _UpperCAmelCase			     =  1.0

              # setable values
              _UpperCAmelCase			     =  None
              _UpperCAmelCase			     =  torch.from_numpy(np.arange(0     ,	a__  )[::-1].copy()  )

              _UpperCAmelCase			     =  eta






       def 				__A			( self     ,	a__     ,	a__ = None  ):
              return sample






       def 				__A			( self     ,	a__     ,	a__ = 10     ,	a__ = 10     ,	a__ = None     ,	):
              _UpperCAmelCase			     =  min(self.config.num_train_timesteps     ,	a__  )
              _UpperCAmelCase			     =  num_inference_steps

              _UpperCAmelCase			     =  []

              _UpperCAmelCase			     =  {}
              for j in range(0     ,	num_inference_steps - jump_length     ,	a__  ):
                     _UpperCAmelCase			     =  jump_n_sample - 1

              _UpperCAmelCase			     =  num_inference_steps
              while t >= 1:
                     _UpperCAmelCase			     =  t - 1
                     timesteps.append(a__  )

                     if jumps.get(a__     ,	0  ) > 0:
                            _UpperCAmelCase			     =  jumps[t] - 1
                            for _ in range(a__  ):
                                   _UpperCAmelCase			     =  t + 1
                                   timesteps.append(a__  )

              _UpperCAmelCase			     =  np.array(a__  ) * (self.config.num_train_timesteps // self.num_inference_steps)
              _UpperCAmelCase			     =  torch.from_numpy(a__  ).to(a__  )






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  t - self.config.num_train_timesteps // self.num_inference_steps

              _UpperCAmelCase			     =  self.alphas_cumprod[t]
              _UpperCAmelCase			     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
              _UpperCAmelCase			     =  1 - alpha_prod_t
              _UpperCAmelCase			     =  1 - alpha_prod_t_prev

              # 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
              # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
              # without eta.
              # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
              _UpperCAmelCase			     =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

              return variance






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__     ,	a__ = None     ,	a__ = True     ,	):
              _UpperCAmelCase			     =  timestep
              _UpperCAmelCase			     =  timestep - self.config.num_train_timesteps // self.num_inference_steps

              # 1. compute alphas, betas
              _UpperCAmelCase			     =  self.alphas_cumprod[t]
              _UpperCAmelCase			     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
              _UpperCAmelCase			     =  1 - alpha_prod_t

              # 2. compute predicted original sample from predicted noise also called
              # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
              _UpperCAmelCase			     =  (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

              # 3. Clip "predicted x_0"
              if self.config.clip_sample:
                     _UpperCAmelCase			     =  torch.clamp(a__     ,	-1     ,	1  )

              # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
              # substitute formula (7) in the algorithm coming from DDPM paper
              # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
              # DDIM schedule gives the same results as DDPM with eta = 1.0
              # Noise is being reused in 7. and 8., but no impact on quality has
              # been observed.

              # 5. Add noise
              _UpperCAmelCase			     =  model_output.device
              _UpperCAmelCase			     =  randn_tensor(model_output.shape     ,	generator=a__     ,	device=a__     ,	dtype=model_output.dtype  )
              _UpperCAmelCase			     =  self.eta * self._get_variance(a__  ) ** 0.5

              _UpperCAmelCase			     =  0
              if t > 0 and self.eta > 0:
                     _UpperCAmelCase			     =  std_dev_t * noise

              # 6. compute "direction pointing to x_t" of formula (12)
              # from https://arxiv.org/pdf/2010.02502.pdf
              _UpperCAmelCase			     =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

              # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
              _UpperCAmelCase			     =  alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

              # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
              _UpperCAmelCase			     =  (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

              # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
              _UpperCAmelCase			     =  mask * prev_known_part + (1.0 - mask) * prev_unknown_part

              if not return_dict:
                     return (
                         pred_prev_sample,
                         pred_original_sample,
                     )

              return RePaintSchedulerOutput(prev_sample=a__     ,	pred_original_sample=a__  )






       def 				__A			( self     ,	a__     ,	a__     ,	a__=None  ):
              _UpperCAmelCase			     =  self.config.num_train_timesteps // self.num_inference_steps

              for i in range(a__  ):
                     _UpperCAmelCase			     =  self.betas[timestep + i]
                     if sample.device.type == "mps":
                            # randn does not work reproducibly on mps
                            _UpperCAmelCase			     =  randn_tensor(sample.shape     ,	dtype=sample.dtype     ,	generator=a__  )
                            _UpperCAmelCase			     =  noise.to(sample.device  )
                     else:
                            _UpperCAmelCase			     =  randn_tensor(sample.shape     ,	generator=a__     ,	device=sample.device     ,	dtype=sample.dtype  )

                     # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                     _UpperCAmelCase			     =  (1 - beta) ** 0.5 * sample + beta**0.5 * noise

              return sample






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	):
              raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'  )






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













"""simple docstring"""






import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
    print('''#################### AVAILABLE DEVICES ####################''')
    print(jax.devices())
    print('''###########################################################''')

    # setup for wandb sweep
    lowerCAmelCase_      =				Args()
    lowerCAmelCase_      =				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
    lowerCAmelCase_      =				dict(logger.config)
    del wandb_args["batch_size"]
    lowerCAmelCase_      =				replace(args, **wandb_args)
    lowerCAmelCase_      =				args.base_dir + '''-''' + wandb.run.id
    lowerCAmelCase_      =				replace(args, base_dir=base_dir)
    print(args)

    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.val_data_path)['''train''']

    # drop extra batch for now
    lowerCAmelCase_      =				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
    lowerCAmelCase_      =				tr_dataset.shuffle().select(indices)
    lowerCAmelCase_      =				range(len(val_dataset) - len(val_dataset) % args.batch_size)
    lowerCAmelCase_      =				val_dataset.shuffle().select(indices)

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        lowerCAmelCase_      =				tr_dataset.shuffle().select(range(80_000))
        lowerCAmelCase_      =				val_dataset.shuffle().select(range(8_000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase_      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase_      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase_      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

    lowerCAmelCase_      =				{
        '''lr''': args.lr,
        '''init_lr''': args.init_lr,
        '''warmup_steps''': args.warmup_steps,
        '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
        '''weight_decay''': args.weight_decay,
    }
    lowerCAmelCase_      ,					lowerCAmelCase_      =				build_tx(**tx_args)

    lowerCAmelCase_      =				Trainer(
        args=args,
        data_collator=data_collator,
        model_save_fn=model.save_pretrained,
        train_step_fn=train_step,
        val_step_fn=val_step,
        logger=logger,
        scheduler_fn=lr,
    )

    lowerCAmelCase_      =				None
    lowerCAmelCase_      =				trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
    try:
        trainer.train(state, tr_dataset, val_dataset)
    except KeyboardInterrupt:
        print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')

    print('''SAVING WEIGHTS IN `final-weights`''')
    lowerCAmelCase_      =				jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)






