






"""simple docstring"""



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 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    @property
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				1
     UpperCAmelCase__ 						=				3
     UpperCAmelCase__ 						=				(32, 32)

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


    @property
    def 		A__      (      self			):
     torch.manual_seed(0			)
     UpperCAmelCase__ 						=				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 		A__      (      self			):
     torch.manual_seed(0			)
     UpperCAmelCase__ 						=				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 		A__      (      self			):
     UpperCAmelCase__ 						=				'cpu'
     UpperCAmelCase__ 						=				self.dummy_uncond_unet
     UpperCAmelCase__ 						=				DDIMScheduler()
     UpperCAmelCase__ 						=				self.dummy_vq_model

     UpperCAmelCase__ 						=				LDMSuperResolutionPipeline(unet=SCREAMING_SNAKE_CASE_   ,			vqvae=SCREAMING_SNAKE_CASE_   ,			scheduler=SCREAMING_SNAKE_CASE_			)
     ldm.to(SCREAMING_SNAKE_CASE_			)
     ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

     UpperCAmelCase__ 						=				self.dummy_image.to(SCREAMING_SNAKE_CASE_			)

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

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

     assert image.shape == (1, 64, 64, 3)
     UpperCAmelCase__ 						=				np.array([0.8_678, 0.8_245, 0.6_381, 0.6_830, 0.4_385, 0.5_599, 0.4_641, 0.6_201, 0.5_150]			)

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






    @unittest.skipIf(torch_device != """cuda"""   ,			"""This test requires a GPU"""			)
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.dummy_uncond_unet
     UpperCAmelCase__ 						=				DDIMScheduler()
     UpperCAmelCase__ 						=				self.dummy_vq_model

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

     UpperCAmelCase__ 						=				LDMSuperResolutionPipeline(unet=SCREAMING_SNAKE_CASE_   ,			vqvae=SCREAMING_SNAKE_CASE_   ,			scheduler=SCREAMING_SNAKE_CASE_			)
     ldm.to(SCREAMING_SNAKE_CASE_			)
     ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

     UpperCAmelCase__ 						=				self.dummy_image.to(SCREAMING_SNAKE_CASE_			)

     UpperCAmelCase__ 						=				ldm(SCREAMING_SNAKE_CASE_   ,			num_inference_steps=2   ,			output_type="""numpy"""			).images

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



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



    '''simple docstring'''


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

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

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

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

     assert image.shape == (1, 256, 256, 3)
     UpperCAmelCase__ 						=				np.array([0.7_644, 0.7_679, 0.7_642, 0.7_633, 0.7_666, 0.7_560, 0.7_425, 0.7_257, 0.6_907]			)

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








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

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

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

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





