




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 						SCREAMING_SNAKE_CASE__		(unittest.TestCase ):
     def __init__(	self  :		int					,	__lowerCamelCase  :		List[Any]					,	__lowerCamelCase  :		Any=13					,	__lowerCamelCase  :		int=3					,	__lowerCamelCase  :		str=18					,	__lowerCamelCase  :		List[Any]=30					,	__lowerCamelCase  :		Optional[Any]=4_00					,	__lowerCamelCase  :		Optional[Any]=True					,	__lowerCamelCase  :		Tuple=None					,	__lowerCamelCase  :		Dict=True					,	__lowerCamelCase  :		List[Any]=[0.5, 0.5, 0.5]					,	__lowerCamelCase  :		Optional[int]=[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 					A__			(	self  :		str	):



       """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 						SCREAMING_SNAKE_CASE__		(_a						,				unittest.TestCase ):
     lowercase_   :			List[str]   =   EfficientNetImageProcessor if is_vision_available() else None
     def 					A__			(	self  :		Dict	):



       """simple docstring"""
       lowerCAmelCase__								=			EfficientNetImageProcessorTester(self	)
     @property
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()
     def 					A__			(	self  :		Dict	):



       """simple docstring"""
       lowerCAmelCase__								=			self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(__lowerCamelCase					,	'''image_mean'''	)	)
       self.assertTrue(hasattr(__lowerCamelCase					,	'''image_std'''	)	)
       self.assertTrue(hasattr(__lowerCamelCase					,	'''do_normalize'''	)	)
       self.assertTrue(hasattr(__lowerCamelCase					,	'''do_resize'''	)	)
       self.assertTrue(hasattr(__lowerCamelCase					,	'''size'''	)	)
     def 					A__			(	self  :		Dict	):



       """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 					A__			(	self  :		int	):



       """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.size['''height'''],
               self.image_processor_tester.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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,	)
     def 					A__			(	self  :		Dict	):



       """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.size['''height'''],
               self.image_processor_tester.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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,	)



     def 					A__			(	self  :		Optional[Any]	):



       """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.size['''height'''],
               self.image_processor_tester.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.size['''height'''],
               self.image_processor_tester.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)

