





from typing import Any

import numpy as np


def        __snake_case       (						lowerCAmelCase_  )							->	bool:
    assert np.shape(lowerCAmelCase_  )[0] == np.shape(lowerCAmelCase_  )[1]

    # If matrix not symmetric, exit right away.
    if np.allclose(lowerCAmelCase_   ,       matrix.T  ) is False:
        return False

    # Get eigenvalues and eignevectors for a symmetric matrix.
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= np.linalg.eigh(lowerCAmelCase_  )

    # Check sign of all eigenvalues.
    # np.all returns a value of type np.bool_
    return bool(np.all(eigen_values > 0  )  )


def        __snake_case       (						lowerCAmelCase_  )							->	Any:
    SCREAMING_SNAKE_CASE__       							= np.random.randn(lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= np.dot(lowerCAmelCase_   ,       random_matrix.T  )
    assert _is_matrix_spd(lowerCAmelCase_  )
    return spd_matrix


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_ = 1_0_0_0   ,       lowerCAmelCase_ = 1e-8   ,       )							->	Any:
    assert np.shape(lowerCAmelCase_  )[0] == np.shape(lowerCAmelCase_  )[1]
    assert np.shape(lowerCAmelCase_  )[0] == np.shape(lowerCAmelCase_  )[0]
    assert _is_matrix_spd(lowerCAmelCase_  )

    # Initialize solution guess, residual, search direction.
    SCREAMING_SNAKE_CASE__       							= np.zeros((np.shape(lowerCAmelCase_  )[0], 1)  )
    SCREAMING_SNAKE_CASE__       							= np.copy(lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= np.copy(lowerCAmelCase_  )

    # Set initial errors in solution guess and residual.
    SCREAMING_SNAKE_CASE__       							= 1e9
    SCREAMING_SNAKE_CASE__       							= 1e9
    SCREAMING_SNAKE_CASE__       							= 1e9

    # Set iteration counter to threshold number of iterations.
    SCREAMING_SNAKE_CASE__       							= 0

    while error > tol:
        # Save this value so we only calculate the matrix-vector product once.
        SCREAMING_SNAKE_CASE__       							= np.dot(lowerCAmelCase_   ,       lowerCAmelCase_  )

        # The main algorithm.

        # Update search direction magnitude.
        SCREAMING_SNAKE_CASE__       							= np.dot(ra.T   ,       lowerCAmelCase_  ) / np.dot(pa.T   ,       lowerCAmelCase_  )
        # Update solution guess.
        SCREAMING_SNAKE_CASE__       							= xa + alpha * pa
        # Calculate new residual.
        SCREAMING_SNAKE_CASE__       							= ra - alpha * w
        # Calculate new Krylov subspace scale.
        SCREAMING_SNAKE_CASE__       							= np.dot(r.T   ,       lowerCAmelCase_  ) / np.dot(ra.T   ,       lowerCAmelCase_  )
        # Calculate new A conjuage search direction.
        SCREAMING_SNAKE_CASE__       							= r + beta * pa

        # Calculate errors.
        SCREAMING_SNAKE_CASE__       							= np.linalg.norm(r - ra  )
        SCREAMING_SNAKE_CASE__       							= np.linalg.norm(x - xa  )
        SCREAMING_SNAKE_CASE__       							= np.maximum(lowerCAmelCase_   ,       lowerCAmelCase_  )

        # Update variables.
        SCREAMING_SNAKE_CASE__       							= np.copy(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= np.copy(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= np.copy(lowerCAmelCase_  )

        # Update number of iterations.
        iterations += 1
        if iterations > max_iterations:
            break

    return x


def        __snake_case       (						)							->	None:
    SCREAMING_SNAKE_CASE__       							= 3
    SCREAMING_SNAKE_CASE__       							= _create_spd_matrix(lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= np.random.randn(lowerCAmelCase_   ,       1  )
    SCREAMING_SNAKE_CASE__       							= np.dot(lowerCAmelCase_   ,       lowerCAmelCase_  )

    # Numpy solution.
    SCREAMING_SNAKE_CASE__       							= np.linalg.solve(lowerCAmelCase_   ,       lowerCAmelCase_  )

    # Our implementation.
    SCREAMING_SNAKE_CASE__       							= conjugate_gradient(lowerCAmelCase_   ,       lowerCAmelCase_  )

    # Ensure both solutions are close to x_true (and therefore one another).
    assert np.linalg.norm(x_numpy - x_true  ) <= 1e-6
    assert np.linalg.norm(x_conjugate_gradient - x_true  ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_conjugate_gradient()







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)
