






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

import numpy as np
import torch

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


_UpperCAmelCase				:	Dict       =    logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class 		lowerCAmelCase_							(     snake_case__   ):
     __lowerCamelCase					:						Any     =    42




class 		lowerCAmelCase_							(     snake_case__							,    snake_case__   ):
     __lowerCamelCase					:						Dict     =    1





     @register_to_config
     def __init__(  self       ,   _UpperCAmelCase = 40       ,   _UpperCAmelCase = 0.002       ,   _UpperCAmelCase = 80.0       ,   _UpperCAmelCase = 0.5       ,   _UpperCAmelCase = 1.0       ,   _UpperCAmelCase = 7.0       ,   _UpperCAmelCase = True       ,   )		->							Optional[Any]:
        # standard deviation of the initial noise distribution
        lowerCamelCase											=sigma_max

        lowerCamelCase											=np.linspace(0       ,   1       ,   __snake_case     )
        lowerCamelCase											=self._convert_to_karras(__snake_case     )
        lowerCamelCase											=self.sigma_to_t(__snake_case     )

        # setable values
        lowerCamelCase											=None
        lowerCamelCase											=torch.from_numpy(__snake_case     )
        lowerCamelCase											=torch.from_numpy(__snake_case     )
        lowerCamelCase											=False
        lowerCamelCase											=False





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase=None     )		->							Any:
        if schedule_timesteps is None:
           lowerCamelCase											=self.timesteps

        lowerCamelCase											=(schedule_timesteps == timestep).nonzero()
        return indices.item()





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase     )		->							Tuple:
        # Get sigma corresponding to timestep
        if isinstance(__snake_case       ,   torch.Tensor     ):
           lowerCamelCase											=timestep.to(self.timesteps.device     )
        lowerCamelCase											=self.index_for_timestep(__snake_case     )
        lowerCamelCase											=self.sigmas[step_idx]

        lowerCamelCase											=sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

        lowerCamelCase											=True
        return sample





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							int:
        if not isinstance(__snake_case       ,   np.ndarray     ):
           lowerCamelCase											=np.array(__snake_case       ,   dtype=np.floataa     )

        lowerCamelCase											=1000 * 0.25 * np.log(sigmas + 1E-4_4     )

        return timesteps





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   )		->							Dict:
        if num_inference_steps is None and timesteps is None:
           raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""     )

        if num_inference_steps is not None and timesteps is not None:
           raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""     )

        # Follow DDPMScheduler custom timesteps logic
        if timesteps is not None:
           for i in range(1       ,   len(__snake_case     )     ):
              if timesteps[i] >= timesteps[i - 1]:
                 raise ValueError("""`timesteps` must be in descending order."""     )

           if timesteps[0] >= self.config.num_train_timesteps:
              raise ValueError(
                  F'''`timesteps` must start before `self.config.train_timesteps`:'''
                  F''' {self.config.num_train_timesteps}.'''     )

           lowerCamelCase											=np.array(__snake_case       ,   dtype=np.intaa     )
           lowerCamelCase											=True
        else:
           if num_inference_steps > self.config.num_train_timesteps:
              raise ValueError(
                  F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                  F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                  F''' maximal {self.config.num_train_timesteps} timesteps.'''     )

           lowerCamelCase											=num_inference_steps

           lowerCamelCase											=self.config.num_train_timesteps // self.num_inference_steps
           lowerCamelCase											=(np.arange(0       ,   __snake_case     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
           lowerCamelCase											=False

        # Map timesteps to Karras sigmas directly for multistep sampling
        # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
        lowerCamelCase											=self.config.num_train_timesteps
        lowerCamelCase											=timesteps[::-1].copy()
        lowerCamelCase											=ramp / (num_train_timesteps - 1)
        lowerCamelCase											=self._convert_to_karras(__snake_case     )
        lowerCamelCase											=self.sigma_to_t(__snake_case     )

        lowerCamelCase											=np.concatenate([sigmas, [self.sigma_min]]     ).astype(np.floataa     )
        lowerCamelCase											=torch.from_numpy(__snake_case     ).to(device=__snake_case     )

        if str(__snake_case     ).startswith("""mps"""     ):
           # mps does not support float64
           lowerCamelCase											=torch.from_numpy(__snake_case     ).to(__snake_case       ,   dtype=torch.floataa     )
        else:
           lowerCamelCase											=torch.from_numpy(__snake_case     ).to(device=__snake_case     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							int:
        lowerCamelCase											=self.config.sigma_min
        lowerCamelCase											=self.config.sigma_max

        lowerCamelCase											=self.config.rho
        lowerCamelCase											=sigma_min ** (1 / rho)
        lowerCamelCase											=sigma_max ** (1 / rho)
        lowerCamelCase											=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							int:
        lowerCamelCase											=self.config.sigma_data

        lowerCamelCase											=sigma_data**2 / (sigma**2 + sigma_data**2)
        lowerCamelCase											=sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							str:
        lowerCamelCase											=self.config.sigma_min
        lowerCamelCase											=self.config.sigma_data

        lowerCamelCase											=sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
        lowerCamelCase											=(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = True       ,   )		->							Optional[int]:

        if (
            isinstance(__snake_case       ,   __snake_case     )
            or isinstance(__snake_case       ,   torch.IntTensor     )
            or isinstance(__snake_case       ,   torch.LongTensor     )
        ):
           raise ValueError(
               (
                   """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                   F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                   """ one of the `scheduler.timesteps` as a timestep."""
               )       ,   )

        if not self.is_scale_input_called:
           logger.warning(
               """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
               """See `StableDiffusionPipeline` for a usage example."""     )

        if isinstance(__snake_case       ,   torch.Tensor     ):
           lowerCamelCase											=timestep.to(self.timesteps.device     )

        lowerCamelCase											=self.config.sigma_min
        lowerCamelCase											=self.config.sigma_max

        lowerCamelCase											=self.index_for_timestep(__snake_case     )

        # sigma_next corresponds to next_t in original implementation
        lowerCamelCase											=self.sigmas[step_index]
        if step_index + 1 < self.config.num_train_timesteps:
           lowerCamelCase											=self.sigmas[step_index + 1]
        else:
           # Set sigma_next to sigma_min
           lowerCamelCase											=self.sigmas[-1]

        # Get scalings for boundary conditions
        lowerCamelCase							,					lowerCamelCase											=self.get_scalings_for_boundary_condition(__snake_case     )

        # 1. Denoise model output using boundary conditions
        lowerCamelCase											=c_out * model_output + c_skip * sample
        if self.config.clip_denoised:
           lowerCamelCase											=denoised.clamp(-1       ,   1     )

        # 2. Sample z ~ N(0, s_noise^2 * I)
        # Noise is not used for onestep sampling.
        if len(self.timesteps     ) > 1:
           lowerCamelCase											=randn_tensor(
               model_output.shape       ,   dtype=model_output.dtype       ,   device=model_output.device       ,   generator=__snake_case     )
        else:
           lowerCamelCase											=torch.zeros_like(__snake_case     )
        lowerCamelCase											=noise * self.config.s_noise

        lowerCamelCase											=sigma_next.clamp(min=__snake_case       ,   max=__snake_case     )

        # 3. Return noisy sample
        # tau = sigma_hat, eps = sigma_min
        lowerCamelCase											=denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

        if not return_dict:
           return (prev_sample,)

        return CMStochasticIterativeSchedulerOutput(prev_sample=__snake_case     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   )		->							Optional[int]:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCamelCase											=self.sigmas.to(device=original_samples.device       ,   dtype=original_samples.dtype     )
        if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case     ):
           # mps does not support float64
           lowerCamelCase											=self.timesteps.to(original_samples.device       ,   dtype=torch.floataa     )
           lowerCamelCase											=timesteps.to(original_samples.device       ,   dtype=torch.floataa     )
        else:
           lowerCamelCase											=self.timesteps.to(original_samples.device     )
           lowerCamelCase											=timesteps.to(original_samples.device     )

        lowerCamelCase											=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowerCamelCase											=sigmas[step_indices].flatten()
        while len(sigma.shape     ) < len(original_samples.shape     ):
           lowerCamelCase											=sigma.unsqueeze(-1     )

        lowerCamelCase											=original_samples + noise * sigma
        return noisy_samples





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







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
   _UpperCAmelCase				:	Optional[Any]    						=    Args()
   _UpperCAmelCase				:	Tuple    						=    wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _UpperCAmelCase				:	Optional[Any]    						=    dict(logger.config)
   del wandb_args["batch_size"]
   _UpperCAmelCase				:	str    						=    replace(args, **wandb_args)
   _UpperCAmelCase				:	Dict    						=    args.base_dir + '-' + wandb.run.id
   _UpperCAmelCase				:	Tuple    						=    replace(args, base_dir=base_dir)
   print(args)

   _UpperCAmelCase				:	List[Any]    						=    load_dataset('json', data_files=args.tr_data_path)['train']
   _UpperCAmelCase				:	Dict    						=    load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _UpperCAmelCase				:	List[str]    						=    range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _UpperCAmelCase				:	List[Any]    						=    tr_dataset.shuffle().select(indices)
   _UpperCAmelCase				:	Any    						=    range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _UpperCAmelCase				:	Union[str, Any]    						=    val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _UpperCAmelCase				:	Optional[Any]    						=    tr_dataset.shuffle().select(range(8_0000))
      _UpperCAmelCase				:	List[Any]    						=    val_dataset.shuffle().select(range(8000))

   print(tr_dataset)
   print(val_dataset)

   _UpperCAmelCase				:	int    						=    FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _UpperCAmelCase				:	Union[str, Any]    						=    BigBirdTokenizerFast.from_pretrained(args.model_id)
   _UpperCAmelCase				:	List[str]    						=    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

   _UpperCAmelCase				:	Any    						=    {
       '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,
   }
   _UpperCAmelCase						,						_UpperCAmelCase				:	Optional[int]    						=    build_tx(**tx_args)

   _UpperCAmelCase				:	List[Any]    						=    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,
   )

   _UpperCAmelCase				:	Union[str, Any]    						=    None
   _UpperCAmelCase				:	Optional[Any]    						=    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`')
   _UpperCAmelCase				:	Optional[int]    						=    jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)