




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

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

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

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

						print(tr_dataset)
						print(val_dataset)

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

						__SCREAMING_SNAKE_CASE      :				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,
						}
						__SCREAMING_SNAKE_CASE      :				Dict				      =build_tx(**tx_args)

						__SCREAMING_SNAKE_CASE      :				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,
						)

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





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

           if isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.integer     ):
                 lowerCamelCase__ 					=			{'dtype': tf.intaa}
           elif isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.floating     ):
                 lowerCamelCase__ 					=			{'dtype': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A__		,					torch.Tensor     ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
           if hasattr(A__		,					'__array__'     ) and not isinstance(A__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           elif isinstance(A__		,					(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           return self._tensorize(A__     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch