


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

 lowerCamelCase							:       str  						=     load_dataset('json', data_files=args.tr_data_path)['train']
 lowerCamelCase							:       Any  						=     load_dataset('json', data_files=args.val_data_path)['train']

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

 if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
  lowerCamelCase							:       Tuple  						=     tr_dataset.shuffle().select(range(80_000))
  lowerCamelCase							:       List[Any]  						=     val_dataset.shuffle().select(range(8_000))

 print(tr_dataset)
 print(val_dataset)

 lowerCamelCase							:       List[str]  						=     FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 lowerCamelCase							:       Any  						=     BigBirdTokenizerFast.from_pretrained(args.model_id)
 lowerCamelCase							:       int  						=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

 lowerCamelCase							:       Optional[Any]  						=     {
     '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							:       Dict  						=     build_tx(**tx_args)

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








'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							IFImgaImgPipeline
      lowerCAmelCase__ :					int   =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      lowerCAmelCase__ :					Optional[int]   =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__ :					str   =							PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             return self._get_dummy_components()



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]=0			):

             '''simple docstring'''







             if str(UpperCamelCase			).startswith('''mps'''			):
                    lowercase__				      =			torch.manual_seed(UpperCamelCase			)
             else:
                    lowercase__				      =			torch.Generator(device=UpperCamelCase			).manual_seed(UpperCamelCase			)

             lowercase__				      =			floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(UpperCamelCase			)			).to(UpperCamelCase			)

             lowercase__				      =			{
                 '''prompt''': '''A painting of a squirrel eating a burger''',
                 '''image''': image,
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''numpy''',
             }

             return inputs



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             self._test_save_load_optional_components()



      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()       ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,						)
      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







             super().test_save_load_floataa(expected_max_diff=1E-1			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             super().test_floataa_inference(expected_max_diff=1E-1			)



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             self._test_save_load_local()






      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







             self._test_inference_batch_single_identical(
                 expected_max_diff=1E-2       ,						)




