






import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union





class     __a						(					enum.Enum ):
 _lowerCAmelCase : List[Any] 				=				'''timm'''
 _lowerCAmelCase : Any 				=				'''transformers'''







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			Optional[Any]:
     if stage_names is None:
          raise ValueError("Stage_names must be set for transformers backbones"    )

     if out_features is not None:
          if not isinstance(__lowerCAmelCase      ,					(list,)    ):
               raise ValueError(f'out_features must be a list {type(__lowerCAmelCase    )}'    )
          if any(feat not in stage_names for feat in out_features    ):
               raise ValueError(f'out_features must be a subset of stage_names: {stage_names} got {out_features}'    )

     if out_indices is not None:
          if not isinstance(__lowerCAmelCase      ,					(list, tuple)    ):
               raise ValueError(f'out_indices must be a list or tuple, got {type(__lowerCAmelCase    )}'    )
          if any(idx >= len(__lowerCAmelCase    ) for idx in out_indices    ):
               raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"    )

     if out_features is not None and out_indices is not None:
          if len(__lowerCAmelCase    ) != len(__lowerCAmelCase    ):
               raise ValueError("out_features and out_indices should have the same length if both are set"    )
          if out_features != [stage_names[idx] for idx in out_indices]:
               raise ValueError("out_features and out_indices should correspond to the same stages if both are set"    )







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					)				->			List[str]:
     if out_indices is None and out_features is None:
          UpperCamelCase__    :     Dict        =						[len(__lowerCAmelCase    ) - 1]
          UpperCamelCase__    :     Optional[int]        =						[stage_names[-1]]
     elif out_indices is None and out_features is not None:
          UpperCamelCase__    :     List[Any]        =						[stage_names.index(__lowerCAmelCase    ) for layer in out_features]
     elif out_features is None and out_indices is not None:
          UpperCamelCase__    :     Any        =						[stage_names[idx] for idx in out_indices]
     return out_features, out_indices







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					)				->			Tuple[List[str], List[int]]:
     verify_out_features_out_indices(out_features=__lowerCAmelCase      ,					out_indices=__lowerCAmelCase      ,					stage_names=__lowerCAmelCase    )
     UpperCamelCase__       ,  UpperCamelCase__    :     Tuple        =						_align_output_features_output_indices(
         out_features=__lowerCAmelCase      ,					out_indices=__lowerCAmelCase      ,					stage_names=__lowerCAmelCase    )
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=__lowerCAmelCase      ,					out_indices=__lowerCAmelCase      ,					stage_names=__lowerCAmelCase    )
     return output_features, output_indices





class     __a						:
 _lowerCAmelCase : Optional[BackboneType] 				=				None







 def       __lowercase      (	self      :     str							,     SCREAMING_SNAKE_CASE      :     Dict		):


      '''simple docstring'''






      if getattr(self							,     "_backbone"							,     SCREAMING_SNAKE_CASE		) is None:
           raise ValueError("self._backbone must be set before calling _init_timm_backbone"		)

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      UpperCamelCase__    :     Any        =						[stage["module"] for stage in self._backbone.feature_info.info]
      UpperCamelCase__    :     List[str]        =						[stage["num_chs"] for stage in self._backbone.feature_info.info]
      UpperCamelCase__    :     Union[str, Any]        =						self._backbone.feature_info.out_indices
      UpperCamelCase__    :     Union[str, Any]        =						self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=SCREAMING_SNAKE_CASE							,     out_indices=SCREAMING_SNAKE_CASE							,     stage_names=self.stage_names		)
      UpperCamelCase__       ,  UpperCamelCase__    :     Union[str, Any]        =						out_features, out_indices







 def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[str]        =						getattr(SCREAMING_SNAKE_CASE							,     "stage_names"		)
      UpperCamelCase__    :     List[str]        =						getattr(SCREAMING_SNAKE_CASE							,     "out_features"							,     SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     List[Any]        =						getattr(SCREAMING_SNAKE_CASE							,     "out_indices"							,     SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Any        =						stage_names
      UpperCamelCase__       ,  UpperCamelCase__    :     str        =						get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,     out_indices=SCREAMING_SNAKE_CASE							,     stage_names=SCREAMING_SNAKE_CASE		)
      # Number of channels for each stage. This is set in the transformer backbone model init
      UpperCamelCase__    :     Tuple        =						None







 def       __lowercase      (	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						config

      UpperCamelCase__    :     str        =						getattr(SCREAMING_SNAKE_CASE							,     "use_timm_backbone"							,     SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
           self._init_timm_backbone(SCREAMING_SNAKE_CASE		)
      elif self.backbone_type == BackboneType.TRANSFORMERS:
           self._init_transformers_backbone(SCREAMING_SNAKE_CASE		)
      else:
           raise ValueError(F'backbone_type {self.backbone_type} not supported.'		)







 @property
 def       __lowercase      (	self      :     List[Any]		):


      '''simple docstring'''






      return self._out_features







 @out_features.setter
 def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__       ,  UpperCamelCase__    :     str        =						get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,     out_indices=SCREAMING_SNAKE_CASE							,     stage_names=self.stage_names		)







 @property
 def       __lowercase      (	self      :     Optional[Any]		):


      '''simple docstring'''






      return self._out_indices







 @out_indices.setter
 def       __lowercase      (	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Union[Tuple[int], List[int]]		):


      '''simple docstring'''






      UpperCamelCase__       ,  UpperCamelCase__    :     str        =						get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,     out_indices=SCREAMING_SNAKE_CASE							,     stage_names=self.stage_names		)







 @property
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names		)}







 @property
 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      return [self.out_feature_channels[name] for name in self.out_features]







 def       __lowercase      (	self      :     List[str]							,     *SCREAMING_SNAKE_CASE      :     List[str]							,     **SCREAMING_SNAKE_CASE      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     List[str]        =						dict(inspect.signature(self.forward		).parameters		)
      UpperCamelCase__    :     Optional[int]        =						{k: v for k, v in kwargs.items() if k in signature}
      return self(*SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)







 def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Optional[bool] = None							,     SCREAMING_SNAKE_CASE      :     Optional[bool] = None							,     SCREAMING_SNAKE_CASE      :     Optional[bool] = None							,     ):


      '''simple docstring'''






      raise NotImplementedError("This method should be implemented by the derived class."		)







 def       __lowercase      (	self      :     Tuple		):


      '''simple docstring'''






      UpperCamelCase__    :     Tuple        =						super().to_dict()
      UpperCamelCase__    :     Optional[int]        =						output.pop("_out_features"		)
      UpperCamelCase__    :     int        =						output.pop("_out_indices"		)
      return output







class     __a						:







 @property
 def       __lowercase      (	self      :     Union[str, Any]		):


      '''simple docstring'''






      return self._out_features







 @out_features.setter
 def       __lowercase      (	self      :     Any							,     SCREAMING_SNAKE_CASE      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__       ,  UpperCamelCase__    :     str        =						get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,     out_indices=SCREAMING_SNAKE_CASE							,     stage_names=self.stage_names		)







 @property
 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      return self._out_indices







 @out_indices.setter
 def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Union[Tuple[int], List[int]]		):


      '''simple docstring'''






      UpperCamelCase__       ,  UpperCamelCase__    :     Dict        =						get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,     out_indices=SCREAMING_SNAKE_CASE							,     stage_names=self.stage_names		)







 def       __lowercase      (	self      :     Union[str, Any]		):


      '''simple docstring'''






      UpperCamelCase__    :     Dict        =						super().to_dict()
      UpperCamelCase__    :     str        =						output.pop("_out_features"		)
      UpperCamelCase__    :     List[Any]        =						output.pop("_out_indices"		)
      return output







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

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

 # drop extra batch for now
 lowerCamelCase			:							List[Any]       =range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
 lowerCamelCase			:							Union[str, Any]       =tr_dataset.shuffle().select(indices)
 lowerCamelCase			:							List[str]       =range(len(val_dataset) - len(val_dataset) % args.batch_size)
 lowerCamelCase			:							Dict       =val_dataset.shuffle().select(indices)

 if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
  lowerCamelCase			:							Optional[int]       =tr_dataset.shuffle().select(range(80000))
  lowerCamelCase			:							List[Any]       =val_dataset.shuffle().select(range(8000))

 print(tr_dataset)
 print(val_dataset)

 lowerCamelCase			:							int       =FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 lowerCamelCase			:							Union[str, Any]       =BigBirdTokenizerFast.from_pretrained(args.model_id)
 lowerCamelCase			:							Dict       =DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

 lowerCamelCase			:							List[str]       ={
     '''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,
 }
 lowerCamelCase       ,    lowerCamelCase			:							Tuple       =build_tx(**tx_args)

 lowerCamelCase			:							Optional[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,
 )

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