






import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()


class 		lowerCAmelCase_							(     unittest.TestCase   ):





     @property
     def 	lowerCamelCase       (  self     )		->							List[Any]:
        lowerCamelCase											=1
        lowerCamelCase											=3
        lowerCamelCase											=(32, 32)

        lowerCamelCase											=floats_tensor((batch_size, num_channels) + sizes       ,   rng=random.Random(0     )     ).to(lowerCamelCase_     )
        return image





     @property
     def 	lowerCamelCase       (  self     )		->							List[Any]:
        torch.manual_seed(0     )
        lowerCamelCase											=UNetaDModel(
            block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=6       ,   out_channels=3       ,   down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,   up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,   )
        return model





     @property
     def 	lowerCamelCase       (  self     )		->							Dict:
        torch.manual_seed(0     )
        lowerCamelCase											=VQModel(
            block_out_channels=[32, 64]       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,   latent_channels=3       ,   )
        return model





     def 	lowerCamelCase       (  self     )		->							List[Any]:
        lowerCamelCase											="""cpu"""
        lowerCamelCase											=self.dummy_uncond_unet
        lowerCamelCase											=DDIMScheduler()
        lowerCamelCase											=self.dummy_vq_model

        lowerCamelCase											=LDMSuperResolutionPipeline(unet=lowerCamelCase_       ,   vqvae=lowerCamelCase_       ,   scheduler=lowerCamelCase_     )
        ldm.to(lowerCamelCase_     )
        ldm.set_progress_bar_config(disable=lowerCamelCase_     )

        lowerCamelCase											=self.dummy_image.to(lowerCamelCase_     )

        lowerCamelCase											=torch.Generator(device=lowerCamelCase_     ).manual_seed(0     )
        lowerCamelCase											=ldm(image=lowerCamelCase_       ,   generator=lowerCamelCase_       ,   num_inference_steps=2       ,   output_type="""numpy"""     ).images

        lowerCamelCase											=image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCamelCase											=np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2





     @unittest.skipIf(torch_device != """cuda"""       ,   """This test requires a GPU"""     )
     def 	lowerCamelCase       (  self     )		->							Any:
        lowerCamelCase											=self.dummy_uncond_unet
        lowerCamelCase											=DDIMScheduler()
        lowerCamelCase											=self.dummy_vq_model

        # put models in fp16
        lowerCamelCase											=unet.half()
        lowerCamelCase											=vqvae.half()

        lowerCamelCase											=LDMSuperResolutionPipeline(unet=lowerCamelCase_       ,   vqvae=lowerCamelCase_       ,   scheduler=lowerCamelCase_     )
        ldm.to(lowerCamelCase_     )
        ldm.set_progress_bar_config(disable=lowerCamelCase_     )

        lowerCamelCase											=self.dummy_image.to(lowerCamelCase_     )

        lowerCamelCase											=ldm(lowerCamelCase_       ,   num_inference_steps=2       ,   output_type="""numpy"""     ).images

        assert image.shape == (1, 64, 64, 3)




@slow
@require_torch
class 		lowerCAmelCase_							(     unittest.TestCase   ):





     def 	lowerCamelCase       (  self     )		->							Tuple:
        lowerCamelCase											=load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/vq_diffusion/teddy_bear_pool.png"""     )
        lowerCamelCase											=init_image.resize((64, 64)       ,   resample=PIL_INTERPOLATION["""lanczos"""]     )

        lowerCamelCase											=LDMSuperResolutionPipeline.from_pretrained("""duongna/ldm-super-resolution"""       ,   device_map="""auto"""     )
        ldm.set_progress_bar_config(disable=lowerCamelCase_     )

        lowerCamelCase											=torch.manual_seed(0     )
        lowerCamelCase											=ldm(image=lowerCamelCase_       ,   generator=lowerCamelCase_       ,   num_inference_steps=20       ,   output_type="""numpy"""     ).images

        lowerCamelCase											=image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        lowerCamelCase											=np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2







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

   _UpperCAmelCase				:	List[Any]    						=    load_dataset('json', data_files=args.tr_data_path)['train']
   _UpperCAmelCase				:	Dict    						=    load_dataset('json', data_files=args.val_data_path)['train']

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

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _UpperCAmelCase				:	Optional[Any]    						=    tr_dataset.shuffle().select(range(8_0000))
      _UpperCAmelCase				:	List[Any]    						=    val_dataset.shuffle().select(range(8000))

   print(tr_dataset)
   print(val_dataset)

   _UpperCAmelCase				:	int    						=    FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _UpperCAmelCase				:	Union[str, Any]    						=    BigBirdTokenizerFast.from_pretrained(args.model_id)
   _UpperCAmelCase				:	List[str]    						=    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

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

   _UpperCAmelCase				:	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,
   )

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