





"""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 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'summarization'
      _SCREAMING_SNAKE_CASE					=      ['loss']
      _SCREAMING_SNAKE_CASE					=      ROUGE_KEYS
      _SCREAMING_SNAKE_CASE					=      'rouge2'





      def __init__(       self      ,						lowercase      ,						**lowercase )		->     str:
            if hparams.sortish_sampler and hparams.gpus > 1:
                  lowerCAmelCase  						=       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__(lowercase      ,						num_labels=lowercase      ,						mode=self.mode      ,						**lowercase )
            use_task_specific_params(self.model      ,						"""summarization""" )
            save_git_info(self.hparams.output_dir )
            lowerCAmelCase  						=       Path(self.output_dir ) / """metrics.json"""
            lowerCAmelCase  						=       Path(self.output_dir ) / """hparams.pkl"""
            pickle_save(self.hparams      ,						self.hparams_save_path )
            lowerCAmelCase  						=       0
            lowerCAmelCase  						=       defaultdict(lowercase )
            lowerCAmelCase  						=       self.config.model_type
            lowerCAmelCase  						=       self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

            lowerCAmelCase  						=       {
                """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() )

            lowerCAmelCase  						=       get_git_info()["""repo_sha"""]
            lowerCAmelCase  						=       hparams.num_workers
            lowerCAmelCase  						=       None  # default to config
            if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,						lowercase ):
                  lowerCAmelCase  						=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                  lowerCAmelCase  						=       self.decoder_start_token_id
            lowerCAmelCase  						=       (
                SeqaSeqDataset if hasattr(self.tokenizer      ,						"""prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
            )
            lowerCAmelCase  						=       False
            lowerCAmelCase  						=       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:
                  lowerCAmelCase  						=       self.hparams.eval_max_gen_length
            else:
                  lowerCAmelCase  						=       self.model.config.max_length
            lowerCAmelCase  						=       self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





      def     _snake_case			(       self      ,						lowercase )		->     Dict[str, List[str]]:
            lowerCAmelCase  						=       {
                k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
            }
            save_json(lowercase      ,						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""" )

            lowerCAmelCase  						=       True
            return readable_batch





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     Union[str, Any]:
            return self.model(lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.tokenizer.batch_decode(
                lowercase      ,						skip_special_tokens=lowercase      ,						clean_up_tokenization_spaces=lowercase )
            return lmap(str.strip      ,						lowercase )





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

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

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





      @property
      def     _snake_case			(       self )		->     int:
            return self.tokenizer.pad_token_id





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Dict:
            lowerCAmelCase  						=       self._step(lowercase )

            lowerCAmelCase  						=       dict(zip(self.loss_names      ,						lowercase ) )
            # tokens per batch
            lowerCAmelCase  						=       batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
            lowerCAmelCase  						=       batch["""input_ids"""].shape[0]
            lowerCAmelCase  						=       batch["""input_ids"""].eq(self.pad ).sum()
            lowerCAmelCase  						=       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      ,						lowercase      ,						lowercase )		->     Dict:
            return self._generative_step(lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase="val" )		->     Dict:
            self.step_count += 1
            lowerCAmelCase  						=       {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
            lowerCAmelCase  						=       losses["""loss"""]
            lowerCAmelCase  						=       {
                k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
            }
            lowerCAmelCase  						=       (
                generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
            )
            lowerCAmelCase  						=       torch.tensor(lowercase ).type_as(lowercase )
            generative_metrics.update({k: v.item() for k, v in losses.items()} )
            losses.update(lowercase )
            lowerCAmelCase  						=       {f'{prefix}_avg_{k}': x for k, x in losses.items()}
            lowerCAmelCase  						=       self.step_count
            self.metrics[prefix].append(lowercase )  # callback writes this to self.metrics_save_path
            lowerCAmelCase  						=       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      ,						lowercase      ,						lowercase )		->     Dict:
            return calculate_rouge(lowercase      ,						lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     dict:
            lowerCAmelCase  						=       time.time()

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





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Dict:
            return self._generative_step(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     int:
            return self.validation_epoch_end(lowercase      ,						prefix="""test""" )





      def     _snake_case			(       self      ,						lowercase )		->     SeqaSeqDataset:
            lowerCAmelCase  						=       self.n_obs[type_path]
            lowerCAmelCase  						=       self.target_lens[type_path]
            lowerCAmelCase  						=       self.dataset_class(
                self.tokenizer      ,						type_path=lowercase      ,						n_obs=lowercase      ,						max_target_length=lowercase      ,						**self.dataset_kwargs      ,						)
            return dataset





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = False )		->     DataLoader:
            lowerCAmelCase  						=       self.get_dataset(lowercase )

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

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





      def     _snake_case			(       self )		->     DataLoader:
            lowerCAmelCase  						=       self.get_dataloader("""train"""      ,						batch_size=self.hparams.train_batch_size      ,						shuffle=lowercase )
            return dataloader





      def     _snake_case			(       self )		->     DataLoader:
            return self.get_dataloader("""val"""      ,						batch_size=self.hparams.eval_batch_size )





      def     _snake_case			(       self )		->     DataLoader:
            return self.get_dataloader("""test"""      ,						batch_size=self.hparams.eval_batch_size )





      @staticmethod
      def     _snake_case			(       lowercase      ,						lowercase )		->     Optional[int]:
            BaseTransformer.add_model_specific_args(lowercase      ,						lowercase )
            add_generic_args(lowercase      ,						lowercase )
            parser.add_argument(
                """--max_source_length"""      ,						default=1_024      ,						type=lowercase      ,						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=lowercase      ,						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=142      ,						type=lowercase      ,						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=142      ,						type=lowercase      ,						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=lowercase )
            parser.add_argument("""--overwrite_output_dir"""      ,						action="""store_true"""      ,						default=lowercase )
            parser.add_argument("""--max_tokens_per_batch"""      ,						type=lowercase      ,						default=lowercase )
            parser.add_argument("""--logger_name"""      ,						type=lowercase      ,						choices=["""default""", """wandb""", """wandb_shared"""]      ,						default="""default""" )
            parser.add_argument("""--n_train"""      ,						type=lowercase      ,						default=-1      ,						required=lowercase      ,						help="""# examples. -1 means use all.""" )
            parser.add_argument("""--n_val"""      ,						type=lowercase      ,						default=500      ,						required=lowercase      ,						help="""# examples. -1 means use all.""" )
            parser.add_argument("""--n_test"""      ,						type=lowercase      ,						default=-1      ,						required=lowercase      ,						help="""# examples. -1 means use all.""" )
            parser.add_argument(
                """--task"""      ,						type=lowercase      ,						default="""summarization"""      ,						required=lowercase      ,						help="""# examples. -1 means use all.""" )
            parser.add_argument("""--label_smoothing"""      ,						type=lowercase      ,						default=0.0      ,						required=lowercase )
            parser.add_argument("""--src_lang"""      ,						type=lowercase      ,						default=""""""      ,						required=lowercase )
            parser.add_argument("""--tgt_lang"""      ,						type=lowercase      ,						default=""""""      ,						required=lowercase )
            parser.add_argument("""--eval_beams"""      ,						type=lowercase      ,						default=lowercase      ,						required=lowercase )
            parser.add_argument(
                """--val_metric"""      ,						type=lowercase      ,						default=lowercase      ,						required=lowercase      ,						choices=["""bleu""", """rouge2""", """loss""", None] )
            parser.add_argument("""--eval_max_gen_length"""      ,						type=lowercase      ,						default=lowercase      ,						help="""never generate more than n tokens""" )
            parser.add_argument("""--save_top_k"""      ,						type=lowercase      ,						default=1      ,						required=lowercase      ,						help="""How many checkpoints to save""" )
            parser.add_argument(
                """--early_stopping_patience"""      ,						type=lowercase      ,						default=-1      ,						required=lowercase      ,						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 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'translation'
      _SCREAMING_SNAKE_CASE					=      ['loss']
      _SCREAMING_SNAKE_CASE					=      ['bleu']
      _SCREAMING_SNAKE_CASE					=      'bleu'





      def __init__(       self      ,						lowercase      ,						**lowercase )		->     Union[str, Any]:
            super().__init__(lowercase      ,						**lowercase )
            lowerCAmelCase  						=       hparams.src_lang
            lowerCAmelCase  						=       hparams.tgt_lang





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     dict:
            return calculate_bleu(lowercase      ,						lowercase )







def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						int=None			):







      '''simple docstring'''




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

      if model is None:
            if "summarization" in args.task:
                  lowerCAmelCase  						=       SummarizationModule(SCREAMING_SNAKE_CASE			)
            else:
                  lowerCAmelCase  						=       TranslationModule(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       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"""			)
      ):
            lowerCAmelCase  						=       True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            lowerCAmelCase  						=       os.environ.get("""WANDB_PROJECT"""							,						SCREAMING_SNAKE_CASE			)
            lowerCAmelCase  						=       WandbLogger(name=model.output_dir.name							,						project=SCREAMING_SNAKE_CASE			)

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

            lowerCAmelCase  						=       WandbLogger(name=model.output_dir.name							,						project=F'hf_{dataset}'			)

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

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

      lowerCAmelCase  						=       """"""
      lowerCAmelCase  						=       sorted(glob.glob(os.path.join(args.output_dir							,						"""*.ckpt"""			)							,						recursive=SCREAMING_SNAKE_CASE			)			)
      if checkpoints:
            lowerCAmelCase  						=       checkpoints[-1]
            lowerCAmelCase  						=       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)










import requests
from bsa import BeautifulSoup
def 				UpperCAmelCase_					(							__snake_case = "https://www.worldometers.info/coronavirus"						)     ->			dict:







 """simple docstring"""


 _lowercase							      =BeautifulSoup(requests.get(__snake_case						).text     ,						'''html.parser'''						)
 _lowercase							      =soup.findAll('''h1'''						)
 _lowercase							      =soup.findAll('''div'''     ,						{'''class''': '''maincounter-number'''}						)
 keys += soup.findAll('''span'''     ,						{'''class''': '''panel-title'''}						)
 values += soup.findAll('''div'''     ,						{'''class''': '''number-table-main'''}						)
 return {key.text.strip(): value.text.strip() for key, value in zip(__snake_case     ,						__snake_case						)}


if __name__ == "__main__":
  print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
  for key, value in world_covidaa_stats().items():
    print(f'''{key}\n{value}\n''')

