"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


SCREAMING_SNAKE_CASE__      						=  logging.getLogger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase					):



    """simple docstring"""
    _lowerCAmelCase     :    List[str]			  =						"""summarization"""
    _lowerCAmelCase     :    str			  =						["""loss"""]
    _lowerCAmelCase     :    str			  =						ROUGE_KEYS
    _lowerCAmelCase     :    List[Any]			  =						"""rouge2"""

    def __init__(				self		,    lowerCAmelCase		,    **lowerCAmelCase    ):






          """simple docstring"""

          if hparams.sortish_sampler and hparams.gpus > 1:
                snake_case								=			False
          elif hparams.max_tokens_per_batch is not None:
                if hparams.gpus > 1:
                      raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'    )
                if hparams.sortish_sampler:
                      raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'    )

          super().__init__(lowerCAmelCase		,    num_labels=lowerCAmelCase		,    mode=self.mode		,    **lowerCAmelCase    )
          use_task_specific_params(self.model		,    'summarization'    )
          save_git_info(self.hparams.output_dir    )
          snake_case								=			Path(self.output_dir    ) / 'metrics.json'
          snake_case								=			Path(self.output_dir    ) / 'hparams.pkl'
          pickle_save(self.hparams		,    self.hparams_save_path    )
          snake_case								=			0
          snake_case								=			defaultdict(lowerCAmelCase    )
          snake_case								=			self.config.model_type
          snake_case								=			self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

          snake_case								=			{
              'data_dir': self.hparams.data_dir,
              'max_source_length': self.hparams.max_source_length,
              'prefix': self.model.config.prefix or '',
          }
          snake_case								=			{
              'train': self.hparams.n_train,
              'val': self.hparams.n_val,
              'test': self.hparams.n_test,
          }
          snake_case								=			{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

          snake_case								=			{
              'train': self.hparams.max_target_length,
              'val': self.hparams.val_max_target_length,
              'test': self.hparams.test_max_target_length,
          }
          assert self.target_lens["train"] <= self.target_lens["val"], F"""target_lens: {self.target_lens}"""
          assert self.target_lens["train"] <= self.target_lens["test"], F"""target_lens: {self.target_lens}"""
          if self.hparams.freeze_embeds:
                freeze_embeds(self.model    )
          if self.hparams.freeze_encoder:
                freeze_params(self.model.get_encoder()    )
                assert_all_frozen(self.model.get_encoder()    )

          snake_case								=			get_git_info()['repo_sha']
          snake_case								=			hparams.num_workers
          snake_case								=			None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,    lowerCAmelCase    ):
                snake_case								=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                snake_case								=			self.decoder_start_token_id
          snake_case								=			(
              SeqaSeqDataset if hasattr(self.tokenizer		,    'prepare_seq2seq_batch'    ) else LegacySeqaSeqDataset
          )
          snake_case								=			False
          snake_case								=			self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
          if self.hparams.eval_max_gen_length is not None:
                snake_case								=			self.hparams.eval_max_gen_length
          else:
                snake_case								=			self.model.config.max_length
          snake_case								=			self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			{
              k: self.tokenizer.batch_decode(v.tolist()    ) if 'mask' not in k else v.shape for k, v in batch.items()
          }
          save_json(lowerCAmelCase		,    Path(self.output_dir    ) / 'text_batch.json'    )
          save_json({k: v.tolist() for k, v in batch.items()}		,    Path(self.output_dir    ) / 'tok_batch.json'    )

          snake_case								=			True
          return readable_batch

    def 		snake_case (				self		,    lowerCAmelCase		,    **lowerCAmelCase    ):






          """simple docstring"""

          return self.model(lowerCAmelCase		,    **lowerCAmelCase    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			self.tokenizer.batch_decode(
              lowerCAmelCase		,    skip_special_tokens=lowerCAmelCase		,    clean_up_tokenization_spaces=lowerCAmelCase    )
          return lmap(str.strip		,    lowerCAmelCase    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			self.tokenizer.pad_token_id
          snake_case					,snake_case								=			batch['input_ids'], batch['attention_mask']
          snake_case								=			batch['labels']
          if isinstance(self.model		,    lowerCAmelCase    ):
                snake_case								=			self.model._shift_right(lowerCAmelCase    )
          else:
                snake_case								=			shift_tokens_right(lowerCAmelCase		,    lowerCAmelCase    )
          if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                snake_case								=			decoder_input_ids
                self.save_readable_batch(lowerCAmelCase    )

          snake_case								=			self(lowerCAmelCase		,    attention_mask=lowerCAmelCase		,    decoder_input_ids=lowerCAmelCase		,    use_cache=lowerCAmelCase    )
          snake_case								=			outputs['logits']
          if self.hparams.label_smoothing == 0:
                # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                snake_case								=			nn.CrossEntropyLoss(ignore_index=lowerCAmelCase    )

                assert lm_logits.shape[-1] == self.vocab_size
                snake_case								=			ce_loss_fct(lm_logits.view(-1		,    lm_logits.shape[-1]    )		,    tgt_ids.view(-1    )    )
          else:
                snake_case								=			nn.functional.log_softmax(lowerCAmelCase		,    dim=-1    )
                snake_case					,snake_case								=			label_smoothed_nll_loss(
                    lowerCAmelCase		,    lowerCAmelCase		,    self.hparams.label_smoothing		,    ignore_index=lowerCAmelCase    )
          return (loss,)

    @property
    def 		snake_case (				self    ):






          """simple docstring"""

          return self.tokenizer.pad_token_id

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			self._step(lowerCAmelCase    )

          snake_case								=			dict(zip(self.loss_names		,    lowerCAmelCase    )    )
          # tokens per batch
          snake_case								=			batch['input_ids'].ne(self.pad    ).sum() + batch['labels'].ne(self.pad    ).sum()
          snake_case								=			batch['input_ids'].shape[0]
          snake_case								=			batch['input_ids'].eq(self.pad    ).sum()
          snake_case								=			batch['input_ids'].eq(self.pad    ).float().mean()
          # TODO(SS): make a wandb summary metric for this
          return {"loss": loss_tensors[0], "log": logs}

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          return self._generative_step(lowerCAmelCase    )

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase="val"    ):






          """simple docstring"""

          self.step_count += 1
          snake_case								=			{k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
          snake_case								=			losses['loss']
          snake_case								=			{
              k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
          }
          snake_case								=			(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          snake_case								=			torch.tensor(lowerCAmelCase    ).type_as(lowerCAmelCase    )
          generative_metrics.update({k: v.item() for k, v in losses.items()}    )
          losses.update(lowerCAmelCase    )
          snake_case								=			{F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
          snake_case								=			self.step_count
          self.metrics[prefix].append(lowerCAmelCase    )  # callback writes this to self.metrics_save_path
          snake_case								=			flatten_list([x['preds'] for x in outputs]    )
          return {
              "log": all_metrics,
              "preds": preds,
              F"""{prefix}_loss""": loss,
              F"""{prefix}_{self.val_metric}""": metric_tensor,
          }

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          return calculate_rouge(lowerCAmelCase		,    lowerCAmelCase    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			time.time()

          # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
          snake_case								=			self.model.generate(
              batch['input_ids']		,    attention_mask=batch['attention_mask']		,    use_cache=lowerCAmelCase		,    decoder_start_token_id=self.decoder_start_token_id		,    num_beams=self.eval_beams		,    max_length=self.eval_max_length		,    )
          snake_case								=			(time.time() - ta) / batch['input_ids'].shape[0]
          snake_case								=			self.ids_to_clean_text(lowerCAmelCase    )
          snake_case								=			self.ids_to_clean_text(batch['labels']    )
          snake_case								=			self._step(lowerCAmelCase    )
          snake_case								=			dict(zip(self.loss_names		,    lowerCAmelCase    )    )
          snake_case								=			self.calc_generative_metrics(lowerCAmelCase		,    lowerCAmelCase    )
          snake_case								=			np.mean(lmap(lowerCAmelCase		,    lowerCAmelCase    )    )
          base_metrics.update(gen_time=lowerCAmelCase		,    gen_len=lowerCAmelCase		,    preds=lowerCAmelCase		,    target=lowerCAmelCase		,    **lowerCAmelCase    )
          return base_metrics

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          return self._generative_step(lowerCAmelCase    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          return self.validation_epoch_end(lowerCAmelCase		,    prefix='test'    )

    def 		snake_case (				self		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			self.n_obs[type_path]
          snake_case								=			self.target_lens[type_path]
          snake_case								=			self.dataset_class(
              self.tokenizer		,    type_path=lowerCAmelCase		,    n_obs=lowerCAmelCase		,    max_target_length=lowerCAmelCase		,    **self.dataset_kwargs		,    )
          return dataset

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = False    ):






          """simple docstring"""

          snake_case								=			self.get_dataset(lowerCAmelCase    )

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                snake_case								=			dataset.make_sortish_sampler(lowerCAmelCase		,    distributed=self.hparams.gpus > 1    )
                return DataLoader(
                    lowerCAmelCase		,    batch_size=lowerCAmelCase		,    collate_fn=dataset.collate_fn		,    shuffle=lowerCAmelCase		,    num_workers=self.num_workers		,    sampler=lowerCAmelCase		,    )

          elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                snake_case								=			dataset.make_dynamic_sampler(
                    self.hparams.max_tokens_per_batch		,    distributed=self.hparams.gpus > 1    )
                return DataLoader(
                    lowerCAmelCase		,    batch_sampler=lowerCAmelCase		,    collate_fn=dataset.collate_fn		,    num_workers=self.num_workers		,    )
          else:
                return DataLoader(
                    lowerCAmelCase		,    batch_size=lowerCAmelCase		,    collate_fn=dataset.collate_fn		,    shuffle=lowerCAmelCase		,    num_workers=self.num_workers		,    sampler=lowerCAmelCase		,    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_dataloader('train'		,    batch_size=self.hparams.train_batch_size		,    shuffle=lowerCAmelCase    )
          return dataloader

    def 		snake_case (				self    ):






          """simple docstring"""

          return self.get_dataloader('val'		,    batch_size=self.hparams.eval_batch_size    )

    def 		snake_case (				self    ):






          """simple docstring"""

          return self.get_dataloader('test'		,    batch_size=self.hparams.eval_batch_size    )

    @staticmethod
    def 		snake_case (				lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          BaseTransformer.add_model_specific_args(lowerCAmelCase		,    lowerCAmelCase    )
          add_generic_args(lowerCAmelCase		,    lowerCAmelCase    )
          parser.add_argument(
              '--max_source_length'		,    default=10_24		,    type=lowerCAmelCase		,    help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )		,    )
          parser.add_argument(
              '--max_target_length'		,    default=56		,    type=lowerCAmelCase		,    help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )		,    )
          parser.add_argument(
              '--val_max_target_length'		,    default=1_42		,    type=lowerCAmelCase		,    help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )		,    )
          parser.add_argument(
              '--test_max_target_length'		,    default=1_42		,    type=lowerCAmelCase		,    help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )		,    )
          parser.add_argument('--freeze_encoder'		,    action='store_true'    )
          parser.add_argument('--freeze_embeds'		,    action='store_true'    )
          parser.add_argument('--sortish_sampler'		,    action='store_true'		,    default=lowerCAmelCase    )
          parser.add_argument('--overwrite_output_dir'		,    action='store_true'		,    default=lowerCAmelCase    )
          parser.add_argument('--max_tokens_per_batch'		,    type=lowerCAmelCase		,    default=lowerCAmelCase    )
          parser.add_argument('--logger_name'		,    type=lowerCAmelCase		,    choices=['default', 'wandb', 'wandb_shared']		,    default='default'    )
          parser.add_argument('--n_train'		,    type=lowerCAmelCase		,    default=-1		,    required=lowerCAmelCase		,    help='# examples. -1 means use all.'    )
          parser.add_argument('--n_val'		,    type=lowerCAmelCase		,    default=5_00		,    required=lowerCAmelCase		,    help='# examples. -1 means use all.'    )
          parser.add_argument('--n_test'		,    type=lowerCAmelCase		,    default=-1		,    required=lowerCAmelCase		,    help='# examples. -1 means use all.'    )
          parser.add_argument(
              '--task'		,    type=lowerCAmelCase		,    default='summarization'		,    required=lowerCAmelCase		,    help='# examples. -1 means use all.'    )
          parser.add_argument('--label_smoothing'		,    type=lowerCAmelCase		,    default=0.0		,    required=lowerCAmelCase    )
          parser.add_argument('--src_lang'		,    type=lowerCAmelCase		,    default=''		,    required=lowerCAmelCase    )
          parser.add_argument('--tgt_lang'		,    type=lowerCAmelCase		,    default=''		,    required=lowerCAmelCase    )
          parser.add_argument('--eval_beams'		,    type=lowerCAmelCase		,    default=lowerCAmelCase		,    required=lowerCAmelCase    )
          parser.add_argument(
              '--val_metric'		,    type=lowerCAmelCase		,    default=lowerCAmelCase		,    required=lowerCAmelCase		,    choices=['bleu', 'rouge2', 'loss', None]    )
          parser.add_argument('--eval_max_gen_length'		,    type=lowerCAmelCase		,    default=lowerCAmelCase		,    help='never generate more than n tokens'    )
          parser.add_argument('--save_top_k'		,    type=lowerCAmelCase		,    default=1		,    required=lowerCAmelCase		,    help='How many checkpoints to save'    )
          parser.add_argument(
              '--early_stopping_patience'		,    type=lowerCAmelCase		,    default=-1		,    required=lowerCAmelCase		,    help=(
                  '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
                  ' val_check_interval will effect it.'
              )		,    )
          return parser





class   lowerCAmelCase_    (			lowerCAmelCase					):



    """simple docstring"""
    _lowerCAmelCase     :    Tuple			  =						"""translation"""
    _lowerCAmelCase     :    Tuple			  =						["""loss"""]
    _lowerCAmelCase     :    Tuple			  =						["""bleu"""]
    _lowerCAmelCase     :    Optional[int]			  =						"""bleu"""

    def __init__(				self		,    lowerCAmelCase		,    **lowerCAmelCase    ):






          """simple docstring"""

          super().__init__(lowerCAmelCase		,    **lowerCAmelCase    )
          snake_case								=			hparams.src_lang
          snake_case								=			hparams.tgt_lang

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          return calculate_bleu(lowerCAmelCase		,    lowerCAmelCase    )





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				str=None					)					->				SummarizationModule:





      """simple docstring"""






      Path(args.output_dir					).mkdir(exist_ok=_UpperCamelCase					)
      check_output_dir(_UpperCamelCase ,			expected_items=3					)

      if model is None:
            if "summarization" in args.task:
                  snake_case								=			SummarizationModule(_UpperCamelCase					)
            else:
                  snake_case								=			TranslationModule(_UpperCamelCase					)
      snake_case								=			Path(args.data_dir					).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir					).startswith('/tmp'					)
          or str(args.output_dir					).startswith('/var'					)
      ):
            snake_case								=			True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            snake_case								=			os.environ.get('WANDB_PROJECT' ,			_UpperCamelCase					)
            snake_case								=			WandbLogger(name=model.output_dir.name ,			project=_UpperCamelCase					)

      elif args.logger_name == "wandb_shared":
            from pytorch_lightning.loggers import WandbLogger

            snake_case								=			WandbLogger(name=model.output_dir.name ,			project=f"""hf_{dataset}"""					)

      if args.early_stopping_patience >= 0:
            snake_case								=			get_early_stopping_callback(model.val_metric ,			args.early_stopping_patience					)
      else:
            snake_case								=			False

      snake_case								=			args.val_metric == 'loss'
      snake_case								=			generic_train(
          _UpperCamelCase ,			_UpperCamelCase ,			logging_callback=SeqaSeqLoggingCallback() ,			checkpoint_callback=get_checkpoint_callback(
              args.output_dir ,			model.val_metric ,			args.save_top_k ,			_UpperCamelCase					) ,			early_stopping_callback=_UpperCamelCase ,			logger=_UpperCamelCase ,			)
      pickle_save(model.hparams ,			model.output_dir / 'hparams.pkl'					)
      if not args.do_predict:
            return model

      snake_case								=			''
      snake_case								=			sorted(glob.glob(os.path.join(args.output_dir ,			'*.ckpt'					) ,			recursive=_UpperCamelCase					)					)
      if checkpoints:
            snake_case								=			checkpoints[-1]
            snake_case								=			checkpoints[-1]
      trainer.logger.log_hyperparams(model.hparams					)

      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__      						=  argparse.ArgumentParser()
     SCREAMING_SNAKE_CASE__      						=  pl.Trainer.add_argparse_args(parser)
     SCREAMING_SNAKE_CASE__      						=  SummarizationModule.add_model_specific_args(parser, os.getcwd())

     SCREAMING_SNAKE_CASE__      						=  parser.parse_args()

     main(args)



"""simple docstring"""
class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[
										    [],
										    [],
										    [],
										]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										try:
																if len(self.queues[priority]    ) >= 1_00:
																						raise OverflowError('Maximum queue size is 100'    )
																self.queues[priority].append(lowerCAmelCase    )
										except IndexError:
																raise ValueError('Valid priorities are 0, 1, and 2'    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										for queue in self.queues:
																if queue:
																						return queue.pop(0    )
										raise UnderFlowError('All queues are empty'    )

				def __str__(				self    ):






										"""simple docstring"""

										return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues    )    )





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[]

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if len(self.queue    ) == 1_00:
																raise OverFlowError('Maximum queue size is 100'    )
										self.queue.append(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if not self.queue:
																raise UnderFlowError('The queue is empty'    )
										else:
																snake_case								=			min(self.queue    )
																self.queue.remove(lowerCAmelCase    )
																return data

				def __str__(				self    ):






										"""simple docstring"""

										return str(self.queue    )





def    lowerCAmelCase__    (			)					->				int:





						"""simple docstring"""






						snake_case								=			FixedPriorityQueue()
						fpq.enqueue(0 ,			1_0					)
						fpq.enqueue(1 ,			7_0					)
						fpq.enqueue(0 ,			1_0_0					)
						fpq.enqueue(2 ,			1					)
						fpq.enqueue(2 ,			5					)
						fpq.enqueue(1 ,			7					)
						fpq.enqueue(2 ,			4					)
						fpq.enqueue(1 ,			6_4					)
						fpq.enqueue(0 ,			1_2_8					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)





def    lowerCAmelCase__    (			)					->				List[str]:





						"""simple docstring"""






						snake_case								=			ElementPriorityQueue()
						epq.enqueue(1_0					)
						epq.enqueue(7_0					)
						epq.enqueue(1_0_0					)
						epq.enqueue(1					)
						epq.enqueue(5					)
						epq.enqueue(7					)
						epq.enqueue(4					)
						epq.enqueue(6_4					)
						epq.enqueue(1_2_8					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()


