




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
						from .testing_utils import (
						    floats_tensor,
						    load_hf_numpy,
						    load_image,
						    load_numpy,
						    load_pt,
						    nightly,
						    parse_flag_from_env,
						    print_tensor_test,
						    require_torch_a,
						    require_torch_gpu,
						    skip_mps,
						    slow,
						    torch_all_close,
						    torch_device,
						)
						from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


__magic_name__     :     Optional[Any]  							=					get_logger(__name__)
def 		a_       (   __lowerCAmelCase							):
		if version.parse(lowercase__							) < version.parse(lowercase__							):
				if "dev" in min_version:
						lowerCAmelCase__								=			(
						    '''This example requires a source install from HuggingFace diffusers (see '''
						    '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
						)
				else:
						lowerCAmelCase__								=			F"""This example requires a minimum version of {min_version},"""
				error_message += F""" but the version found is {__version__}.\n"""
				raise ImportError(lowercase__							)







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)

