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

			SCREAMING_SNAKE_CASE_			      =					load_dataset('''json''', data_files=args.tr_data_path)['''train''']
			SCREAMING_SNAKE_CASE_			      =					load_dataset('''json''', data_files=args.val_data_path)['''train''']

			# drop extra batch for now
			SCREAMING_SNAKE_CASE_			      =					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
			SCREAMING_SNAKE_CASE_			      =					tr_dataset.shuffle().select(indices)
			SCREAMING_SNAKE_CASE_			      =					range(len(val_dataset) - len(val_dataset) % args.batch_size)
			SCREAMING_SNAKE_CASE_			      =					val_dataset.shuffle().select(indices)

			if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
						SCREAMING_SNAKE_CASE_			      =					tr_dataset.shuffle().select(range(80_000))
						SCREAMING_SNAKE_CASE_			      =					val_dataset.shuffle().select(range(8_000))

			print(tr_dataset)
			print(val_dataset)

			SCREAMING_SNAKE_CASE_			      =					FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			SCREAMING_SNAKE_CASE_			      =					BigBirdTokenizerFast.from_pretrained(args.model_id)
			SCREAMING_SNAKE_CASE_			      =					DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

			SCREAMING_SNAKE_CASE_			      =					{
			    '''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,
			}
			SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_			      =					build_tx(**tx_args)

			SCREAMING_SNAKE_CASE_			      =					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,
			)

			SCREAMING_SNAKE_CASE_			      =					None
			SCREAMING_SNAKE_CASE_			      =					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`''')
			SCREAMING_SNAKE_CASE_			      =					jax_utils.unreplicate(state.params)
			model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)


"""simple docstring"""
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """Remaining Weights %"""				,  """Remaining Weight"""		)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
			        ''' pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
			        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
			        '''Not needed for `l0`'''
			    ),
			)
			parser.add_argument(
			    '''--serialization_dir''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
