



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

	A__       =			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
	A__       =			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

	if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
		A__       =			tr_dataset.shuffle().select(range(8_00_00))
		A__       =			val_dataset.shuffle().select(range(80_00))

	print(tr_dataset)
	print(val_dataset)

	A__       =			FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	A__       =			BigBirdTokenizerFast.from_pretrained(args.model_id)
	A__       =			DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

	A__       =			{
	    """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__       =			build_tx(**tx_args)

	A__       =			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__       =			None
	A__       =			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__       =			jax_utils.unreplicate(state.params)
	model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import EfficientNetImageProcessor





class    __lowerCAmelCase					(	unittest.TestCase			):




				def __init__(				self		,							_snake_case		,							_snake_case=13		,							_snake_case=3		,							_snake_case=18		,							_snake_case=30		,							_snake_case=400		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=[0.5, 0.5, 0.5]		,							_snake_case=[0.5, 0.5, 0.5]		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       size if size is not None else {"""height""": 18, """width""": 18}
									_lowerCAmelCase         =       parent
									_lowerCAmelCase         =       batch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       min_resolution
									_lowerCAmelCase         =       max_resolution
									_lowerCAmelCase         =       do_resize
									_lowerCAmelCase         =       size
									_lowerCAmelCase         =       do_normalize
									_lowerCAmelCase         =       image_mean
									_lowerCAmelCase         =       image_std




				def 				snake_case  (				self						):



									"""simple docstring"""





									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}





@require_torch
@require_vision
class    __lowerCAmelCase					(	lowerCamelCase__			,		unittest.TestCase			):
				__lowerCamelCase					=							EfficientNetImageProcessor if is_vision_available() else None




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       EfficientNetImageProcessorTester(self						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_snake_case		,							"""image_mean"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""image_std"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_normalize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_resize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""size"""						)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size		,							{"""height""": 18, """width""": 18}						)

									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict		,							size=42						)
									self.assertEqual(image_processor.size		,							{"""height""": 42, """width""": 42}						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							Image.Image						)

									# Test not batched input
									_lowerCAmelCase         =       image_processing(image_inputs[0]		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       image_processing(_snake_case		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case		,							numpify=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							np.ndarray						)

									# Test not batched input
									_lowerCAmelCase         =       image_processing(image_inputs[0]		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       image_processing(_snake_case		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case		,							torchify=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							torch.Tensor						)

									# Test not batched input
									_lowerCAmelCase         =       image_processing(image_inputs[0]		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       image_processing(_snake_case		,							return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape		,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)



