
'''simple docstring'''
import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


__lowerCAmelCase  							= logging.get_logger(__name__)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						)  ->     Any:
														super().__init__()
														if version.parse(torch.__version__						) < version.parse("""1.12.0"""						):
																					raise ImportError(
																					    f"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
																					    """PytorchGELUTanh. Please upgrade torch."""						)





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return nn.functional.gelu(UpperCAmelCase						,						approximate="""tanh"""						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.04_4715 * torch.pow(UpperCAmelCase						,						3.0						))						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase = False						)  ->     Optional[Any]:
														super().__init__()
														if use_gelu_python:
																					_snake_case     =						self._gelu_python
														else:
																					_snake_case     =						nn.functional.gelu

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0						)						))





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return self.act(UpperCAmelCase						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input * torch.sigmoid(1.702 * input						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     List[Any]:
														if min > max:
																					raise ValueError(f"""min should be < max (got min: {min}, max: {max})"""						)

														super().__init__()
														_snake_case     =						min
														_snake_case     =						max





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return torch.clip(gelu(UpperCAmelCase						)						,						self.min						,						self.max						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						)  ->     Tuple:
														super().__init__()
														_snake_case     =						math.sqrt(2 / math.pi						)





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(UpperCAmelCase						,						3						))						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return nn.functional.silu(UpperCAmelCase						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						)  ->     Any:
														super().__init__()
														if version.parse(torch.__version__						) < version.parse("""1.9.0"""						):
																					_snake_case     =						self._mish_python
														else:
																					_snake_case     =						nn.functional.mish

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input * torch.tanh(nn.functional.softplus(UpperCAmelCase						)						)





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return self.act(UpperCAmelCase						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=0.70_7107						,						UpperCAmelCase=0.28_2095						)  ->     List[str]:
														_snake_case     =						(input - mu).div(sigma * math.sqrt(2.0						)						)
														return 0.5 * (1.0 + torch.erf(UpperCAmelCase						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Any:
														_snake_case     =						nn.functional.relu(UpperCAmelCase						)
														_snake_case     =						torch.square(UpperCAmelCase						)
														return squared


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							def __getitem__(self						,						UpperCAmelCase						)  ->     Tuple:
														_snake_case     =						super().__getitem__(UpperCAmelCase						)
														_snake_case,							_snake_case     =						content if isinstance(UpperCAmelCase						,						UpperCAmelCase						) else (content, {})
														return cls(**UpperCAmelCase						)
__lowerCAmelCase  							= {
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -10, 'max': 10}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
__lowerCAmelCase  							= ClassInstantier(ACTaCLS)






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()   )}"""   )


# For backwards compatibility with: from activations import gelu_python
__lowerCAmelCase  							= get_activation('gelu_python')
__lowerCAmelCase  							= get_activation('gelu_new')
__lowerCAmelCase  							= get_activation('gelu')
__lowerCAmelCase  							= get_activation('gelu_fast')
__lowerCAmelCase  							= get_activation('quick_gelu')
__lowerCAmelCase  							= get_activation('silu')
__lowerCAmelCase  							= get_activation('mish')

__lowerCAmelCase  							= get_activation('linear')

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

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

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

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

				print(tr_dataset)
				print(val_dataset)

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

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

				__lowerCAmelCase  							= 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,
				)

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