





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		int							=  (DPMSolverMultistepScheduler,)
    lowerCamelCase__						:		Union[str, Any]							=  (("""num_inference_steps""", 2_5),)



    def    lowercase_				(					self      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= {
            '''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(**A_    )
        return config



    def    lowercase_				(					self      ,							A_=0      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= dict(self.forward_default_kwargs    )
        SCREAMING_SNAKE_CASE__       							= kwargs.pop('''num_inference_steps'''      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= self.dummy_sample
        SCREAMING_SNAKE_CASE__       							= 0.1 * sample
        SCREAMING_SNAKE_CASE__       							= [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__       							= self.get_scheduler_config(**A_    )
            SCREAMING_SNAKE_CASE__       							= scheduler_class(**A_    )
            scheduler.set_timesteps(A_    )
            # copy over dummy past residuals
            SCREAMING_SNAKE_CASE__       							= dummy_past_residuals[: scheduler.config.solver_order]

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

            SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= sample, sample
            for t in range(A_      ,							time_step + scheduler.config.solver_order + 1    ):
                SCREAMING_SNAKE_CASE__       							= scheduler.step(A_      ,							A_      ,							A_      ,							**A_    ).prev_sample
                SCREAMING_SNAKE_CASE__       							= new_scheduler.step(A_      ,							A_      ,							A_      ,							**A_    ).prev_sample

                assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"



    def    lowercase_				(					self    ):





        '''simple docstring'''

        pass



    def    lowercase_				(					self      ,							A_=0      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= dict(self.forward_default_kwargs    )
        SCREAMING_SNAKE_CASE__       							= kwargs.pop('''num_inference_steps'''      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= self.dummy_sample
        SCREAMING_SNAKE_CASE__       							= 0.1 * sample
        SCREAMING_SNAKE_CASE__       							= [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__       							= self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__       							= scheduler_class(**A_    )
            scheduler.set_timesteps(A_    )

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A_    )
                SCREAMING_SNAKE_CASE__       							= scheduler_class.from_pretrained(A_    )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(A_    )

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

            SCREAMING_SNAKE_CASE__       							= scheduler.step(A_      ,							A_      ,							A_      ,							**A_    ).prev_sample
            SCREAMING_SNAKE_CASE__       							= new_scheduler.step(A_      ,							A_      ,							A_      ,							**A_    ).prev_sample

            assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"



    def    lowercase_				(					self      ,							A_=None      ,							**A_    ):





        '''simple docstring'''

        if scheduler is None:
            SCREAMING_SNAKE_CASE__       							= self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE__       							= self.get_scheduler_config(**A_    )
            SCREAMING_SNAKE_CASE__       							= scheduler_class(**A_    )

        SCREAMING_SNAKE_CASE__       							= 10
        SCREAMING_SNAKE_CASE__       							= self.dummy_model()
        SCREAMING_SNAKE_CASE__       							= self.dummy_sample_deter
        scheduler.set_timesteps(A_    )

        for i, t in enumerate(scheduler.timesteps    ):
            SCREAMING_SNAKE_CASE__       							= model(A_      ,							A_    )
            SCREAMING_SNAKE_CASE__       							= scheduler.step(A_      ,							A_      ,							A_    ).prev_sample

        return sample



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= dict(self.forward_default_kwargs    )

        SCREAMING_SNAKE_CASE__       							= kwargs.pop('''num_inference_steps'''      ,							A_    )

        for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__       							= self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__       							= scheduler_class(**A_    )

            SCREAMING_SNAKE_CASE__       							= self.dummy_sample
            SCREAMING_SNAKE_CASE__       							= 0.1 * sample

            if num_inference_steps is not None and hasattr(A_      ,							'''set_timesteps'''    ):
                scheduler.set_timesteps(A_    )
            elif num_inference_steps is not None and not hasattr(A_      ,							'''set_timesteps'''    ):
                SCREAMING_SNAKE_CASE__       							= num_inference_steps

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

            SCREAMING_SNAKE_CASE__       							= scheduler.timesteps[5]
            SCREAMING_SNAKE_CASE__       							= scheduler.timesteps[6]

            SCREAMING_SNAKE_CASE__       							= scheduler.step(A_      ,							A_      ,							A_      ,							**A_    ).prev_sample
            SCREAMING_SNAKE_CASE__       							= scheduler.step(A_      ,							A_      ,							A_      ,							**A_    ).prev_sample

            self.assertEqual(output_a.shape      ,							sample.shape    )
            self.assertEqual(output_a.shape      ,							output_a.shape    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        for timesteps in [25, 50, 1_00, 9_99, 10_00]:
            self.check_over_configs(num_train_timesteps=A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.check_over_configs(thresholding=A_    )
        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=A_      ,							prediction_type=A_      ,							sample_max_value=A_      ,							algorithm_type='''dpmsolver++'''      ,							solver_order=A_      ,							solver_type=A_      ,							)



    def    lowercase_				(					self    ):





        '''simple docstring'''

        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        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=A_      ,							solver_type=A_      ,							prediction_type=A_      ,							algorithm_type=A_      ,							)
                        SCREAMING_SNAKE_CASE__       							= self.full_loop(
                            solver_order=A_      ,							solver_type=A_      ,							prediction_type=A_      ,							algorithm_type=A_      ,							)
                        assert not torch.isnan(A_    ).any(), "Samples have nan numbers"



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.check_over_configs(lower_order_final=A_    )
        self.check_over_configs(lower_order_final=A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.check_over_configs(lambda_min_clipped=-float('''inf'''    )    )
        self.check_over_configs(lambda_min_clipped=-5.1    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.check_over_configs(variance_type=A_    )
        self.check_over_configs(variance_type='''learned_range'''    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
            self.check_over_forward(num_inference_steps=A_      ,							time_step=0    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.full_loop()
        SCREAMING_SNAKE_CASE__       							= torch.mean(torch.abs(A_    )    )

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



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.full_loop(thresholding=A_      ,							dynamic_thresholding_ratio=0.87      ,							sample_max_value=0.5    )
        SCREAMING_SNAKE_CASE__       							= torch.mean(torch.abs(A_    )    )

        assert abs(result_mean.item() - 1.1364    ) < 1E-3



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.full_loop(prediction_type='''v_prediction'''    )
        SCREAMING_SNAKE_CASE__       							= torch.mean(torch.abs(A_    )    )

        assert abs(result_mean.item() - 0.2251    ) < 1E-3



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.full_loop(prediction_type='''v_prediction'''      ,							use_karras_sigmas=A_    )
        SCREAMING_SNAKE_CASE__       							= torch.mean(torch.abs(A_    )    )

        assert abs(result_mean.item() - 0.2096    ) < 1E-3



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
        SCREAMING_SNAKE_CASE__       							= self.full_loop(scheduler=A_    )
        SCREAMING_SNAKE_CASE__       							= torch.mean(torch.abs(A_    )    )

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

        SCREAMING_SNAKE_CASE__       							= DPMSolverSinglestepScheduler.from_config(scheduler.config    )
        SCREAMING_SNAKE_CASE__       							= UniPCMultistepScheduler.from_config(scheduler.config    )
        SCREAMING_SNAKE_CASE__       							= DEISMultistepScheduler.from_config(scheduler.config    )
        SCREAMING_SNAKE_CASE__       							= DPMSolverMultistepScheduler.from_config(scheduler.config    )

        SCREAMING_SNAKE_CASE__       							= self.full_loop(scheduler=A_    )
        SCREAMING_SNAKE_CASE__       							= torch.mean(torch.abs(A_    )    )

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



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE__       							= self.get_scheduler_config(thresholding=A_      ,							dynamic_thresholding_ratio=0    )
        SCREAMING_SNAKE_CASE__       							= scheduler_class(**A_    )

        SCREAMING_SNAKE_CASE__       							= 10
        SCREAMING_SNAKE_CASE__       							= self.dummy_model()
        SCREAMING_SNAKE_CASE__       							= self.dummy_sample_deter.half()
        scheduler.set_timesteps(A_    )

        for i, t in enumerate(scheduler.timesteps    ):
            SCREAMING_SNAKE_CASE__       							= model(A_      ,							A_    )
            SCREAMING_SNAKE_CASE__       							= scheduler.step(A_      ,							A_      ,							A_    ).prev_sample

        assert sample.dtype == torch.floataa




    def    lowercase_				(					self      ,							**A_    ):





        '''simple docstring'''

        for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__       							= self.get_scheduler_config(**A_    )
            SCREAMING_SNAKE_CASE__       							= scheduler_class(**A_    )

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







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

  _A				:							Tuple							= load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  _A				:							Optional[Any]							= load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

  if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
    _A				:							Optional[Any]							= tr_dataset.shuffle().select(range(8_00_00))
    _A				:							Tuple							= val_dataset.shuffle().select(range(80_00))

  print(tr_dataset)
  print(val_dataset)

  _A				:							int							= FlaxBigBirdForNaturalQuestions.from_pretrained(
      args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  )
  _A				:							Any							= BigBirdTokenizerFast.from_pretrained(args.model_id)
  _A				:							Optional[Any]							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

  _A				:							int							= {
      """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,
  }
  _A,					_A				:							Union[str, Any]							= build_tx(**tx_args)

  _A				:							str							= 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,
  )

  _A				:							Tuple							= None
  _A				:							Dict							= 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`""")
  _A				:							Tuple							= jax_utils.unreplicate(state.params)
  model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
