





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

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

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

     if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
          _UpperCamelCase    =	tr_dataset.shuffle().select(range(8_0000))
          _UpperCamelCase    =	val_dataset.shuffle().select(range(8000))

     print(tr_dataset)
     print(val_dataset)

     _UpperCamelCase    =	FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     _UpperCamelCase    =	BigBirdTokenizerFast.from_pretrained(args.model_id)
     _UpperCamelCase    =	DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

     _UpperCamelCase    =	{
         "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,
     }
     _UpperCamelCase    =	build_tx(**tx_args)

     _UpperCamelCase    =	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,
     )

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








from __future__ import annotations

import sys




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Union[str, Any]:






        '''simple docstring'''






        __snake_case				:			str            =    letter
        __snake_case				:			int            =    freq
        __snake_case				:			dict[str, str]            =    {}







      def __repr__(	self				)    ->				str:






        '''simple docstring'''






        return F"""{self.letter}:{self.freq}"""




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			int            =    freq
        __snake_case				:			Letter | TreeNode            =    left
        __snake_case				:			Letter | TreeNode            =    right







def  lowerCAmelCase__(		lowercase    :      str			)	->      list[Letter]:
  __snake_case				:			dict[str, int]            =    {}
  with open(lowercase			) as f:
    while True:
      __snake_case				:			List[Any]            =    f.read(1			)
      if not c:
        break
      __snake_case				:			Optional[Any]            =    chars[c] + 1 if c in chars else 1
  return sorted((Letter(lowercase				,       lowercase			) for c, f in chars.items())				,       key=lambda lowercase			: x.freq			)







def  lowerCAmelCase__(		lowercase    :      list[Letter]			)	->      Letter | TreeNode:
  __snake_case				:			list[Letter | TreeNode]            =    letters  # type: ignore
  while len(lowercase			) > 1:
    __snake_case				:			Any            =    response.pop(0			)
    __snake_case				:			Optional[int]            =    response.pop(0			)
    __snake_case				:			str            =    left.freq + right.freq
    __snake_case				:			Dict            =    TreeNode(lowercase				,       lowercase				,       lowercase			)
    response.append(lowercase			)
    response.sort(key=lambda lowercase			: x.freq			)
  return response[0]







def  lowerCAmelCase__(		lowercase    :      Letter | TreeNode				,       lowercase    :      str			)	->      list[Letter]:
  if isinstance(lowercase				,       lowercase			):
    __snake_case				:			List[str]            =    bitstring
    return [root]
  __snake_case				:			TreeNode            =    root  # type: ignore
  __snake_case				:			Union[str, Any]            =    []
  letters += traverse_tree(treenode.left				,       bitstring + "0"			)
  letters += traverse_tree(treenode.right				,       bitstring + "1"			)
  return letters







def  lowerCAmelCase__(		lowercase    :      str			)	->      None:
  __snake_case				:			List[Any]            =    parse_file(lowercase			)
  __snake_case				:			List[str]            =    build_tree(lowercase			)
  __snake_case				:			Optional[int]            =    {
      k: v for letter in traverse_tree(lowercase				,       ""			) for k, v in letter.bitstring.items()
  }
  print(f"""Huffman Coding  of {file_path}: """			)
  with open(lowercase			) as f:
    while True:
      __snake_case				:			int            =    f.read(1			)
      if not c:
        break
      print(letters[c]				,       end=" "			)
  print()


if __name__ == "__main__":
     # pass the file path to the huffman function
     huffman(sys.argv[1])

