
'''simple docstring'''
from __future__ import annotations

import sys


class     _lowerCAmelCase					:







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Optional[int]:
														_snake_case     =						letter
														_snake_case     =						freq
														_snake_case     =						{}





							def __repr__(self						)  ->     str:
														return f"""{self.letter}:{self.freq}"""


class     _lowerCAmelCase					:







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Optional[Any]:
														_snake_case     =						freq
														_snake_case     =						left
														_snake_case     =						right






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case     =						{}
							with open(_SCREAMING_SNAKE_CASE   ) as f:
														while True:
																					_snake_case     =						f.read(1   )
																					if not c:
																												break
																					_snake_case     =						chars[c] + 1 if c in chars else 1
							return sorted((Letter(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ) for c, f in chars.items())							,      key=lambda _SCREAMING_SNAKE_CASE   : x.freq   )






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case     =						letters  # type: ignore
							while len(_SCREAMING_SNAKE_CASE   ) > 1:
														_snake_case     =						response.pop(0   )
														_snake_case     =						response.pop(0   )
														_snake_case     =						left.freq + right.freq
														_snake_case     =						TreeNode(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )
														response.append(_SCREAMING_SNAKE_CASE   )
														response.sort(key=lambda _SCREAMING_SNAKE_CASE   : x.freq   )
							return response[0]






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							if isinstance(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
														_snake_case     =						bitstring
														return [root]
							_snake_case     =						root  # type: ignore
							_snake_case     =						[]
							letters += traverse_tree(treenode.left							,      bitstring + """0"""   )
							letters += traverse_tree(treenode.right							,      bitstring + """1"""   )
							return letters






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case     =						parse_file(_SCREAMING_SNAKE_CASE   )
							_snake_case     =						build_tree(_SCREAMING_SNAKE_CASE   )
							_snake_case     =						{
							    k: v for letter in traverse_tree(_SCREAMING_SNAKE_CASE							,      """"""   ) for k, v in letter.bitstring.items()
							}
							print(f"""Huffman Coding  of {file_path}: """   )
							with open(_SCREAMING_SNAKE_CASE   ) as f:
														while True:
																					_snake_case     =						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])







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

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

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

				if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
								__lowerCAmelCase  							= tr_dataset.shuffle().select(range(80_000))
								__lowerCAmelCase  							= val_dataset.shuffle().select(range(8_000))

				print(tr_dataset)
				print(val_dataset)

				__lowerCAmelCase  							= FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				__lowerCAmelCase  							= BigBirdTokenizerFast.from_pretrained(args.model_id)
				__lowerCAmelCase  							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

				__lowerCAmelCase  							= {
				    '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  							= build_tx(**tx_args)

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