
"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Union[str, Any]	=       (DPMSolverMultistepScheduler,)
    __lowercase				:			Dict	=       (("""num_inference_steps""", 2_5),)
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Optional[int]            = {
          "num_train_timesteps": 10_00,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
          "solver_order": 2,
          "prediction_type": "epsilon",
          "thresholding": False,
          "sample_max_value": 1.0,
          "algorithm_type": "dpmsolver++",
          "solver_type": "midpoint",
          "lower_order_final": False,
          "lambda_min_clipped": -float("inf"  ),
          "variance_type": None,
      }

      config.update(**snake_case_  )
      return config
    def        lowerCamelCase__						(     self    ,     snake_case_=0    ,     **snake_case_  ):
      _snake_case  :    Optional[Any]            = dict(self.forward_default_kwargs  )
      _snake_case  :    List[str]            = kwargs.pop("num_inference_steps"    ,     snake_case_  )
      _snake_case  :    List[Any]            = self.dummy_sample
      _snake_case  :    List[Any]            = 0.1 * sample
      _snake_case  :    Union[str, Any]            = [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    Any            = self.get_scheduler_config(**snake_case_  )
        _snake_case  :    str            = scheduler_class(**snake_case_  )
        scheduler.set_timesteps(snake_case_  )
        # copy over dummy past residuals
        _snake_case  :    int            = dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(snake_case_  )
          _snake_case  :    List[Any]            = scheduler_class.from_pretrained(snake_case_  )
          new_scheduler.set_timesteps(snake_case_  )
          # copy over dummy past residuals
          _snake_case  :    Any            = dummy_past_residuals[: new_scheduler.config.solver_order]

        _snake_case      ,       _snake_case  :    str            = sample, sample
        for t in range(snake_case_    ,     time_step + scheduler.config.solver_order + 1  ):
          _snake_case  :    Optional[int]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample
          _snake_case  :    str            = new_scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample

          assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self    ,     snake_case_=0    ,     **snake_case_  ):
      _snake_case  :    Optional[int]            = dict(self.forward_default_kwargs  )
      _snake_case  :    Any            = kwargs.pop("num_inference_steps"    ,     snake_case_  )
      _snake_case  :    Tuple            = self.dummy_sample
      _snake_case  :    Dict            = 0.1 * sample
      _snake_case  :    Optional[Any]            = [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    List[str]            = self.get_scheduler_config()
        _snake_case  :    str            = scheduler_class(**snake_case_  )
        scheduler.set_timesteps(snake_case_  )

        # copy over dummy past residuals (must be after setting timesteps)
        _snake_case  :    Dict            = dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(snake_case_  )
          _snake_case  :    Optional[int]            = scheduler_class.from_pretrained(snake_case_  )
          # copy over dummy past residuals
          new_scheduler.set_timesteps(snake_case_  )

          # copy over dummy past residual (must be after setting timesteps)
          _snake_case  :    List[str]            = dummy_past_residuals[: new_scheduler.config.solver_order]

        _snake_case  :    List[Any]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample
        _snake_case  :    Optional[int]            = new_scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample

        assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
    def        lowerCamelCase__						(     self    ,     snake_case_=None    ,     **snake_case_  ):
      if scheduler is None:
        _snake_case  :    Tuple            = self.scheduler_classes[0]
        _snake_case  :    Tuple            = self.get_scheduler_config(**snake_case_  )
        _snake_case  :    int            = scheduler_class(**snake_case_  )

      _snake_case  :    Tuple            = 10
      _snake_case  :    Union[str, Any]            = self.dummy_model()
      _snake_case  :    Dict            = self.dummy_sample_deter
      scheduler.set_timesteps(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    int            = model(snake_case_    ,     snake_case_  )
        _snake_case  :    str            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  ).prev_sample

      return sample
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = dict(self.forward_default_kwargs  )

      _snake_case  :    int            = kwargs.pop("num_inference_steps"    ,     snake_case_  )

      for scheduler_class in self.scheduler_classes:
        _snake_case  :    Optional[int]            = self.get_scheduler_config()
        _snake_case  :    Tuple            = scheduler_class(**snake_case_  )

        _snake_case  :    List[str]            = self.dummy_sample
        _snake_case  :    Union[str, Any]            = 0.1 * sample

        if num_inference_steps is not None and hasattr(snake_case_    ,     "set_timesteps"  ):
          scheduler.set_timesteps(snake_case_  )
        elif num_inference_steps is not None and not hasattr(snake_case_    ,     "set_timesteps"  ):
          _snake_case  :    Tuple            = num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        _snake_case  :    List[Any]            = [residual + 0.2, residual + 0.15, residual + 0.10]
        _snake_case  :    Any            = dummy_past_residuals[: scheduler.config.solver_order]

        _snake_case  :    Optional[Any]            = scheduler.timesteps[5]
        _snake_case  :    str            = scheduler.timesteps[6]

        _snake_case  :    str            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample
        _snake_case  :    Any            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     **snake_case_  ).prev_sample

        self.assertEqual(output_a.shape    ,     sample.shape  )
        self.assertEqual(output_a.shape    ,     output_a.shape  )
    def        lowerCamelCase__						(     self  ):
      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
        self.check_over_configs(num_train_timesteps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(thresholding=snake_case_  )
      for order in [1, 2, 3]:
        for solver_type in ["midpoint", "heun"]:
          for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  thresholding=snake_case_    ,     prediction_type=snake_case_    ,     sample_max_value=snake_case_    ,     algorithm_type="dpmsolver++"    ,     solver_order=snake_case_    ,     solver_type=snake_case_    ,     )
    def        lowerCamelCase__						(     self  ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
        for solver_type in ["midpoint", "heun"]:
          for order in [1, 2, 3]:
            for prediction_type in ["epsilon", "sample"]:
              if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                if order == 3:
                  continue
              else:
                self.check_over_configs(
                    solver_order=snake_case_    ,     solver_type=snake_case_    ,     prediction_type=snake_case_    ,     algorithm_type=snake_case_    ,     )
              _snake_case  :    Optional[Any]            = self.full_loop(
                  solver_order=snake_case_    ,     solver_type=snake_case_    ,     prediction_type=snake_case_    ,     algorithm_type=snake_case_    ,     )
              assert not torch.isnan(snake_case_  ).any(), "Samples have nan numbers"
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(lower_order_final=snake_case_  )
      self.check_over_configs(lower_order_final=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(lambda_min_clipped=-float("inf"  )  )
      self.check_over_configs(lambda_min_clipped=-5.1  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(variance_type=snake_case_  )
      self.check_over_configs(variance_type="learned_range"  )
    def        lowerCamelCase__						(     self  ):
      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
        self.check_over_forward(num_inference_steps=snake_case_    ,     time_step=0  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.full_loop()
      _snake_case  :    List[str]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.full_loop(thresholding=snake_case_    ,     dynamic_thresholding_ratio=0.87    ,     sample_max_value=0.5  )
      _snake_case  :    Optional[int]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 1.1364  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.full_loop(prediction_type="v_prediction"  )
      _snake_case  :    Union[str, Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.2251  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.full_loop(prediction_type="v_prediction"    ,     use_karras_sigmas=snake_case_  )
      _snake_case  :    List[Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.2096  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      _snake_case  :    Union[str, Any]            = DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
      _snake_case  :    Dict            = self.full_loop(scheduler=snake_case_  )
      _snake_case  :    List[str]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3

      _snake_case  :    Optional[Any]            = DPMSolverSinglestepScheduler.from_config(scheduler.config  )
      _snake_case  :    Dict            = UniPCMultistepScheduler.from_config(scheduler.config  )
      _snake_case  :    List[Any]            = DEISMultistepScheduler.from_config(scheduler.config  )
      _snake_case  :    Tuple            = DPMSolverMultistepScheduler.from_config(scheduler.config  )

      _snake_case  :    Dict            = self.full_loop(scheduler=snake_case_  )
      _snake_case  :    Optional[Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_mean.item() - 0.3301  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.scheduler_classes[0]
      _snake_case  :    Tuple            = self.get_scheduler_config(thresholding=snake_case_    ,     dynamic_thresholding_ratio=0  )
      _snake_case  :    Any            = scheduler_class(**snake_case_  )

      _snake_case  :    int            = 10
      _snake_case  :    int            = self.dummy_model()
      _snake_case  :    str            = self.dummy_sample_deter.half()
      scheduler.set_timesteps(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    Tuple            = model(snake_case_    ,     snake_case_  )
        _snake_case  :    List[str]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  ).prev_sample

      assert sample.dtype == torch.floataa


    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      for scheduler_class in self.scheduler_classes:
        _snake_case  :    Union[str, Any]            = self.get_scheduler_config(**snake_case_  )
        _snake_case  :    Any            = scheduler_class(**snake_case_  )

        scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
        assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps






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

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





