




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__magic_name__     :     int  							=					logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			Tuple   =   ["input_values", "attention_mask"]
     def __init__(	self  :		str					,	__lowerCamelCase  :		Optional[int]=1					,	__lowerCamelCase  :		int=1_60_00					,	__lowerCamelCase  :		Any=0.0					,	__lowerCamelCase  :		List[Any]=False					,	__lowerCamelCase  :		List[str]=True					,	**__lowerCamelCase  :		Dict					,	):



       """simple docstring"""
       super().__init__(feature_size=__lowerCamelCase					,	sampling_rate=__lowerCamelCase					,	padding_value=__lowerCamelCase					,	**__lowerCamelCase	)
       lowerCAmelCase__								=			return_attention_mask
       lowerCAmelCase__								=			do_normalize
     @staticmethod
     def 					A__			(	__lowerCamelCase  :		List[np.ndarray]					,	__lowerCamelCase  :		List[np.ndarray]					,	__lowerCamelCase  :		float = 0.0	):



       """simple docstring"""
       if attention_mask is not None:
         lowerCAmelCase__								=			np.array(__lowerCamelCase					,	np.intaa	)
         lowerCAmelCase__								=			[]

         for vector, length in zip(__lowerCamelCase					,	attention_mask.sum(-1	)	):
           lowerCAmelCase__								=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
           if length < normed_slice.shape[0]:
             lowerCAmelCase__								=			padding_value

           normed_input_values.append(__lowerCamelCase	)
       else:
         lowerCAmelCase__								=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

       return normed_input_values



     def __call__(	self  :		Dict					,	__lowerCamelCase  :		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,	__lowerCamelCase  :		Union[bool, str, PaddingStrategy] = False					,	__lowerCamelCase  :		Optional[int] = None					,	__lowerCamelCase  :		bool = False					,	__lowerCamelCase  :		Optional[int] = None					,	__lowerCamelCase  :		Optional[bool] = None					,	__lowerCamelCase  :		Optional[Union[str, TensorType]] = None					,	__lowerCamelCase  :		Optional[int] = None					,	**__lowerCamelCase  :		Optional[int]					,	):



       """simple docstring"""

       if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
           raise ValueError(
               F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
               F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
               F""" {self.sampling_rate} and not {sampling_rate}."""	)
       else:
         logger.warning(
             '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
             '''Failing to do so can result in silent errors that might be hard to debug.'''	)

       lowerCAmelCase__								=			isinstance(__lowerCamelCase					,	np.ndarray	) and len(raw_speech.shape	) > 1
       if is_batched_numpy and len(raw_speech.shape	) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""	)
       lowerCAmelCase__								=			is_batched_numpy or (
           isinstance(__lowerCamelCase					,	(list, tuple)	) and (isinstance(raw_speech[0]					,	(np.ndarray, tuple, list)	))
       )

       # always return batch
       if not is_batched:
         lowerCAmelCase__								=			[raw_speech]

       # convert into correct format for padding
       lowerCAmelCase__								=			BatchFeature({'''input_values''': raw_speech}	)

       lowerCAmelCase__								=			self.pad(
           __lowerCamelCase					,	padding=__lowerCamelCase					,	max_length=__lowerCamelCase					,	truncation=__lowerCamelCase					,	pad_to_multiple_of=__lowerCamelCase					,	return_attention_mask=__lowerCamelCase					,	)

       # convert input values to correct format
       lowerCAmelCase__								=			padded_inputs['''input_values''']
       if not isinstance(input_values[0]					,	np.ndarray	):
         lowerCAmelCase__								=			[np.asarray(__lowerCamelCase					,	dtype=np.floataa	) for array in input_values]
       elif (
           not isinstance(__lowerCamelCase					,	np.ndarray	)
           and isinstance(input_values[0]					,	np.ndarray	)
           and input_values[0].dtype is np.dtype(np.floataa	)
       ):
         lowerCAmelCase__								=			[array.astype(np.floataa	) for array in input_values]
       elif isinstance(__lowerCamelCase					,	np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
         lowerCAmelCase__								=			input_values.astype(np.floataa	)

       # convert attention_mask to correct format
       lowerCAmelCase__								=			padded_inputs.get('''attention_mask'''	)
       if attention_mask is not None:
         lowerCAmelCase__								=			[np.asarray(__lowerCamelCase					,	dtype=np.intaa	) for array in attention_mask]

       # zero-mean and unit-variance normalization
       if self.do_normalize:
         lowerCAmelCase__								=			(
             attention_mask
             if self._get_padding_strategies(__lowerCamelCase					,	max_length=__lowerCamelCase	) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         lowerCAmelCase__								=			self.zero_mean_unit_var_norm(
             padded_inputs['''input_values''']					,	attention_mask=__lowerCamelCase					,	padding_value=self.padding_value	)

       if return_tensors is not None:
         lowerCAmelCase__								=			padded_inputs.convert_to_tensors(__lowerCamelCase	)

       return padded_inputs







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)

