




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 MobileNetVaImageProcessor





class 						SCREAMING_SNAKE_CASE__		(unittest.TestCase ):
					def __init__(	self  :		str					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		int=7					,	__lowerCamelCase  :		Any=3					,	__lowerCamelCase  :		int=18					,	__lowerCamelCase  :		int=30					,	__lowerCamelCase  :		int=4_00					,	__lowerCamelCase  :		Union[str, Any]=True					,	__lowerCamelCase  :		Optional[Any]=None					,	__lowerCamelCase  :		int=True					,	__lowerCamelCase  :		Dict=None					,	):



							"""simple docstring"""
							lowerCAmelCase__								=			size if size is not None else {"shortest_edge": 20}
							lowerCAmelCase__								=			crop_size if crop_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_center_crop
							lowerCAmelCase__								=			crop_size



					def 					A__			(	self  :		Dict	):



							"""simple docstring"""
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							}





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE__		(UpperCAmelCase__						,				unittest.TestCase ):
					lowercase_   :			str   =   MobileNetVaImageProcessor if is_vision_available() else None
					def 					A__			(	self  :		Optional[int]	):



							"""simple docstring"""
							lowerCAmelCase__								=			MobileNetVaImageProcessingTester(self	)
					@property
					def 					A__			(	self  :		List[Any]	):



							"""simple docstring"""
							return self.image_processor_tester.prepare_image_processor_dict()
					def 					A__			(	self  :		Optional[int]	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(lowerCamelCase__					,	'''do_resize'''	)	)
							self.assertTrue(hasattr(lowerCamelCase__					,	'''size'''	)	)
							self.assertTrue(hasattr(lowerCamelCase__					,	'''do_center_crop'''	)	)
							self.assertTrue(hasattr(lowerCamelCase__					,	'''center_crop'''	)	)
					def 					A__			(	self  :		Dict	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size					,	{'''shortest_edge''': 20}	)
							self.assertEqual(image_processor.crop_size					,	{'''height''': 18, '''width''': 18}	)

							lowerCAmelCase__								=			self.image_processing_class.from_dict(self.image_processor_dict					,	size=42					,	crop_size=84	)
							self.assertEqual(image_processor.size					,	{'''shortest_edge''': 42}	)
							self.assertEqual(image_processor.crop_size					,	{'''height''': 84, '''width''': 84}	)
					def 					A__			(	self  :		Any	):



							"""simple docstring"""
							pass
					def 					A__			(	self  :		Dict	):



							"""simple docstring"""
							# Initialize image_processing
							lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase__								=			prepare_image_inputs(self.image_processor_tester					,	equal_resolution=lowerCamelCase__	)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__					,	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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,	)

							# Test batched
							lowerCAmelCase__								=			image_processing(lowerCamelCase__					,	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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,	)
					def 					A__			(	self  :		Tuple	):



							"""simple docstring"""
							# Initialize image_processing
							lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCAmelCase__								=			prepare_image_inputs(self.image_processor_tester					,	equal_resolution=lowerCamelCase__					,	numpify=lowerCamelCase__	)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__					,	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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,	)

							# Test batched
							lowerCAmelCase__								=			image_processing(lowerCamelCase__					,	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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,	)



					def 					A__			(	self  :		List[str]	):



							"""simple docstring"""
							# Initialize image_processing
							lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCAmelCase__								=			prepare_image_inputs(self.image_processor_tester					,	equal_resolution=lowerCamelCase__					,	torchify=lowerCamelCase__	)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__					,	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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,	)

							# Test batched
							lowerCAmelCase__								=			image_processing(lowerCamelCase__					,	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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )					,	)







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

      __magic_name__     :     Union[str, Any]  							=					load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
      __magic_name__     :     List[Any]  							=					load_dataset("""json""", data_files=args.val_data_path)["""train"""]

      # drop extra batch for now
      __magic_name__     :     List[str]  							=					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					tr_dataset.shuffle().select(indices)
      __magic_name__     :     Any  							=					range(len(val_dataset) - len(val_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					val_dataset.shuffle().select(indices)

      if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
            __magic_name__     :     Any  							=					tr_dataset.shuffle().select(range(8_00_00))
            __magic_name__     :     Optional[int]  							=					val_dataset.shuffle().select(range(80_00))

      print(tr_dataset)
      print(val_dataset)

      __magic_name__     :     Union[str, Any]  							=					FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      __magic_name__     :     Tuple  							=					BigBirdTokenizerFast.from_pretrained(args.model_id)
      __magic_name__     :     Tuple  							=					DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

      __magic_name__     :     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,
      }
      __magic_name__							,   __magic_name__     :     Optional[int]  							=					build_tx(**tx_args)

      __magic_name__     :     List[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,
      )

      __magic_name__     :     Union[str, Any]  							=					None
      __magic_name__     :     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`""")
      __magic_name__     :     List[Any]  							=					jax_utils.unreplicate(state.params)
      model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)

