



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

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

	# drop extra batch for now
	UpperCAmelCase :				Optional[int]							=range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
	UpperCAmelCase :				Optional[int]							=tr_dataset.shuffle().select(indices)
	UpperCAmelCase :				Union[str, 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 :				str							=tr_dataset.shuffle().select(range(8_0000))
		UpperCAmelCase :				Optional[int]							=val_dataset.shuffle().select(range(8000))

	print(tr_dataset)
	print(val_dataset)

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

	UpperCAmelCase :				Optional[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 :				Any							=build_tx(**tx_args)

	UpperCAmelCase :				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 :				int							=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[Any]							=jax_utils.unreplicate(state.params)
	model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)




from typing import Any

import numpy as np






def  _UpperCAmelCase						( __lowerCamelCase							)		->bool:


 '''simple docstring'''
 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.
 snake_case	,       snake_case :			int							=   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  _UpperCAmelCase						( __lowerCamelCase							)		->Any:


 '''simple docstring'''
 snake_case :			Dict							=   np.random.randn(__lowerCamelCase			,  __lowerCamelCase							)
 snake_case :			List[Any]							=   np.dot(__lowerCamelCase			,  random_matrix.T							)
 assert _is_matrix_spd(__lowerCamelCase							)
 return spd_matrix






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase = 1000			,  __lowerCamelCase = 1E-8			,  )		->Any:


 '''simple docstring'''
 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.
 snake_case :			Any							=   np.zeros((np.shape(__lowerCamelCase							)[0], 1)							)
 snake_case :			Optional[int]							=   np.copy(__lowerCamelCase							)
 snake_case :			Optional[Any]							=   np.copy(__lowerCamelCase							)

 # Set initial errors in solution guess and residual.
 snake_case :			Optional[Any]							=   1E9
 snake_case :			List[Any]							=   1E9
 snake_case :			List[str]							=   1E9

 # Set iteration counter to threshold number of iterations.
 snake_case :			List[str]							=   0

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

  # The main algorithm.

  # Update search direction magnitude.
  snake_case :			List[str]							=   np.dot(ra.T			,  __lowerCamelCase							) / np.dot(pa.T			,  __lowerCamelCase							)
  # Update solution guess.
  snake_case :			Optional[int]							=   xa + alpha * pa
  # Calculate new residual.
  snake_case :			str							=   ra - alpha * w
  # Calculate new Krylov subspace scale.
  snake_case :			List[str]							=   np.dot(r.T			,  __lowerCamelCase							) / np.dot(ra.T			,  __lowerCamelCase							)
  # Calculate new A conjuage search direction.
  snake_case :			List[Any]							=   r + beta * pa

  # Calculate errors.
  snake_case :			List[Any]							=   np.linalg.norm(r - ra							)
  snake_case :			Any							=   np.linalg.norm(x - xa							)
  snake_case :			str							=   np.maximum(__lowerCamelCase			,  __lowerCamelCase							)

  # Update variables.
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			List[str]							=   np.copy(__lowerCamelCase							)

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

 return x






def  _UpperCAmelCase						( )		->None:


 '''simple docstring'''
 snake_case :			str							=   3
 snake_case :			Dict							=   _create_spd_matrix(__lowerCamelCase							)
 snake_case :			str							=   np.random.randn(__lowerCamelCase			,  1							)
 snake_case :			str							=   np.dot(__lowerCamelCase			,  __lowerCamelCase							)

 # Numpy solution.
 snake_case :			Any							=   np.linalg.solve(__lowerCamelCase			,  __lowerCamelCase							)

 # Our implementation.
 snake_case :			List[str]							=   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()






