

"""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"""







 __UpperCAmelCase			:      Tuple     	=			IFImgaImgPipeline
 __UpperCAmelCase			:      int     	=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __UpperCAmelCase			:      str     	=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __UpperCAmelCase			:      str     	=			PipelineTesterMixin.required_optional_params - {"latents"}


 def       _lowercase				(    self							:    List[Any]			):
    return self._get_dummy_components()


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    List[str]=0			):
    if str(UpperCAmelCase__			).startswith("mps"			):
       __lowercase			=		torch.manual_seed(UpperCAmelCase__			)
    else:
       __lowercase			=		torch.Generator(device=UpperCAmelCase__			).manual_seed(UpperCAmelCase__			)

    __lowercase			=		floats_tensor((1, 3, 3_2, 3_2),      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       _lowercase				(    self							:    Any			):
    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       _lowercase				(    self							:    List[Any]			):
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)


 @unittest.skipIf(torch_device != "cuda",      reason="float16 requires CUDA"			)
 def       _lowercase				(    self							:    str			):
    # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
    super().test_save_load_floataa(expected_max_diff=1E-1			)


 @unittest.skipIf(torch_device != "cuda",      reason="float16 requires CUDA"			)
 def       _lowercase				(    self							:    List[str]			):
    super().test_floataa_inference(expected_max_diff=1E-1			)


 def       _lowercase				(    self							:    Tuple			):
    self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)


 def       _lowercase				(    self							:    Dict			):
    self._test_save_load_local()



 def       _lowercase				(    self							:    Optional[Any]			):
    self._test_inference_batch_single_identical(
        expected_max_diff=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
   _a				   =							Args()
   _a				   =							wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _a				   =							dict(logger.config)
   del wandb_args["batch_size"]
   _a				   =							replace(args, **wandb_args)
   _a				   =							args.base_dir + '-' + wandb.run.id
   _a				   =							replace(args, base_dir=base_dir)
   print(args)

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       '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,
   }
   _a   ,		_a				   =							build_tx(**tx_args)

   _a				   =							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,
   )

   _a				   =							None
   _a				   =							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`')
   _a				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)




