


'''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


a_						:       List[Any]      			=				logging.getLogger(__name__)





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"summarization"
  _lowerCamelCase						=						["loss"]
  _lowerCamelCase						=						ROUGE_KEYS
  _lowerCamelCase						=						"rouge2"






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







   """simple docstring"""
   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__(UpperCamelCase    ,						num_labels=UpperCamelCase    ,						mode=self.mode    ,						**UpperCamelCase  )
   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(UpperCamelCase  )
   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    ,						UpperCamelCase  ):
    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    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{
       k: self.tokenizer.batch_decode(v.tolist()  ) if "mask" not in k else v.shape for k, v in batch.items()
   }
   save_json(UpperCamelCase    ,						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    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.model(UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.tokenizer.batch_decode(
       UpperCamelCase    ,						skip_special_tokens=UpperCamelCase    ,						clean_up_tokenization_spaces=UpperCamelCase  )
   return lmap(str.strip    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.tokenizer.pad_token_id
   lowerCamelCase_				,lowerCamelCase_  				=						batch["input_ids"], batch["attention_mask"]
   lowerCamelCase_  				=						batch["labels"]
   if isinstance(self.model    ,						UpperCamelCase  ):
    lowerCamelCase_  				=						self.model._shift_right(UpperCamelCase  )
   else:
    lowerCamelCase_  				=						shift_tokens_right(UpperCamelCase    ,						UpperCamelCase  )
   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(UpperCamelCase  )

   lowerCamelCase_  				=						self(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						decoder_input_ids=UpperCamelCase    ,						use_cache=UpperCamelCase  )
   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=UpperCamelCase  )

    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(UpperCamelCase    ,						dim=-1  )
    lowerCamelCase_				,lowerCamelCase_  				=						label_smoothed_nll_loss(
        UpperCamelCase    ,						UpperCamelCase    ,						self.hparams.label_smoothing    ,						ignore_index=UpperCamelCase  )
   return (loss,)






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.tokenizer.pad_token_id






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._step(UpperCamelCase  )

   lowerCamelCase_  				=						dict(zip(self.loss_names    ,						UpperCamelCase  )  )
   # 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    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return self._generative_step(UpperCamelCase  )






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







   """simple docstring"""
   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(UpperCamelCase  ).type_as(UpperCamelCase  )
   generative_metrics.update({k: v.item() for k, v in losses.items()}  )
   losses.update(UpperCamelCase  )
   lowerCamelCase_  				=						{f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
   lowerCamelCase_  				=						self.step_count
   self.metrics[prefix].append(UpperCamelCase  )  # 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    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return calculate_rouge(UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   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=UpperCamelCase    ,						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(UpperCamelCase  )
   lowerCamelCase_  				=						self.ids_to_clean_text(batch["labels"]  )
   lowerCamelCase_  				=						self._step(UpperCamelCase  )
   lowerCamelCase_  				=						dict(zip(self.loss_names    ,						UpperCamelCase  )  )
   lowerCamelCase_  				=						self.calc_generative_metrics(UpperCamelCase    ,						UpperCamelCase  )
   lowerCamelCase_  				=						np.mean(lmap(UpperCamelCase    ,						UpperCamelCase  )  )
   base_metrics.update(gen_time=UpperCamelCase    ,						gen_len=UpperCamelCase    ,						preds=UpperCamelCase    ,						target=UpperCamelCase    ,						**UpperCamelCase  )
   return base_metrics






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return self._generative_step(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   return self.validation_epoch_end(UpperCamelCase    ,						prefix="test"  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.n_obs[type_path]
   lowerCamelCase_  				=						self.target_lens[type_path]
   lowerCamelCase_  				=						self.dataset_class(
       self.tokenizer    ,						type_path=UpperCamelCase    ,						n_obs=UpperCamelCase    ,						max_target_length=UpperCamelCase    ,						**self.dataset_kwargs    ,						)
   return dataset






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







   """simple docstring"""
   lowerCamelCase_  				=						self.get_dataset(UpperCamelCase  )

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

   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(
        UpperCamelCase    ,						batch_sampler=UpperCamelCase    ,						collate_fn=dataset.collate_fn    ,						num_workers=self.num_workers    ,						)
   else:
    return DataLoader(
        UpperCamelCase    ,						batch_size=UpperCamelCase    ,						collate_fn=dataset.collate_fn    ,						shuffle=UpperCamelCase    ,						num_workers=self.num_workers    ,						sampler=UpperCamelCase    ,						)






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_dataloader("train"    ,						batch_size=self.hparams.train_batch_size    ,						shuffle=UpperCamelCase  )
   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	(					UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   BaseTransformer.add_model_specific_args(UpperCamelCase    ,						UpperCamelCase  )
   add_generic_args(UpperCamelCase    ,						UpperCamelCase  )
   parser.add_argument(
       "--max_source_length"    ,						default=1024    ,						type=UpperCamelCase    ,						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=UpperCamelCase    ,						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=UpperCamelCase    ,						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=UpperCamelCase    ,						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=UpperCamelCase  )
   parser.add_argument("--overwrite_output_dir"    ,						action="store_true"    ,						default=UpperCamelCase  )
   parser.add_argument("--max_tokens_per_batch"    ,						type=UpperCamelCase    ,						default=UpperCamelCase  )
   parser.add_argument("--logger_name"    ,						type=UpperCamelCase    ,						choices=["default", "wandb", "wandb_shared"]    ,						default="default"  )
   parser.add_argument("--n_train"    ,						type=UpperCamelCase    ,						default=-1    ,						required=UpperCamelCase    ,						help="# examples. -1 means use all."  )
   parser.add_argument("--n_val"    ,						type=UpperCamelCase    ,						default=500    ,						required=UpperCamelCase    ,						help="# examples. -1 means use all."  )
   parser.add_argument("--n_test"    ,						type=UpperCamelCase    ,						default=-1    ,						required=UpperCamelCase    ,						help="# examples. -1 means use all."  )
   parser.add_argument(
       "--task"    ,						type=UpperCamelCase    ,						default="summarization"    ,						required=UpperCamelCase    ,						help="# examples. -1 means use all."  )
   parser.add_argument("--label_smoothing"    ,						type=UpperCamelCase    ,						default=0.0    ,						required=UpperCamelCase  )
   parser.add_argument("--src_lang"    ,						type=UpperCamelCase    ,						default=""    ,						required=UpperCamelCase  )
   parser.add_argument("--tgt_lang"    ,						type=UpperCamelCase    ,						default=""    ,						required=UpperCamelCase  )
   parser.add_argument("--eval_beams"    ,						type=UpperCamelCase    ,						default=UpperCamelCase    ,						required=UpperCamelCase  )
   parser.add_argument(
       "--val_metric"    ,						type=UpperCamelCase    ,						default=UpperCamelCase    ,						required=UpperCamelCase    ,						choices=["bleu", "rouge2", "loss", None]  )
   parser.add_argument("--eval_max_gen_length"    ,						type=UpperCamelCase    ,						default=UpperCamelCase    ,						help="never generate more than n tokens"  )
   parser.add_argument("--save_top_k"    ,						type=UpperCamelCase    ,						default=1    ,						required=UpperCamelCase    ,						help="How many checkpoints to save"  )
   parser.add_argument(
       "--early_stopping_patience"    ,						type=UpperCamelCase    ,						default=-1    ,						required=UpperCamelCase    ,						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   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"translation"
  _lowerCamelCase						=						["loss"]
  _lowerCamelCase						=						["bleu"]
  _lowerCamelCase						=						"bleu"






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







   """simple docstring"""
   super().__init__(UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						hparams.src_lang
   lowerCamelCase_  				=						hparams.tgt_lang







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return calculate_bleu(UpperCamelCase    ,						UpperCamelCase  )





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Optional[int]=None	):
 Path(args.output_dir	).mkdir(exist_ok=UpperCAmelCase_	)
 check_output_dir(UpperCAmelCase_      ,       expected_items=3	)

 if model is None:
  if "summarization" in args.task:
   lowerCamelCase_  				=						SummarizationModule(UpperCAmelCase_	)
  else:
   lowerCamelCase_  				=						TranslationModule(UpperCAmelCase_	)
 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"      ,       UpperCAmelCase_	)
  lowerCamelCase_  				=						WandbLogger(name=model.output_dir.name      ,       project=UpperCAmelCase_	)

 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(
     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

 lowerCamelCase_  				=						""
 lowerCamelCase_  				=						sorted(glob.glob(os.path.join(args.output_dir      ,       "*.ckpt"	)      ,       recursive=UpperCAmelCase_	)	)
 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__":
     a_						:       List[str]      			=				argparse.ArgumentParser()
     a_						:       Optional[int]      			=				pl.Trainer.add_argparse_args(parser)
     a_						:       Dict      			=				SummarizationModule.add_model_specific_args(parser, os.getcwd())

     a_						:       int      			=				parser.parse_args()

     main(args)







'''simple docstring'''

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


a_						:       Dict      			=				get_logger(__name__)





class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						"dummy_data"
  _lowerCamelCase						=						"datasets"
  _lowerCamelCase						=						False






  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = False    ,						UpperCamelCase = True    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						0
   lowerCamelCase_  				=						dataset_name
   lowerCamelCase_  				=						cache_dir
   lowerCamelCase_  				=						use_local_dummy_data
   lowerCamelCase_  				=						config
   # download_callbacks take a single url as input
   lowerCamelCase_  				=						download_callbacks or []
   # if False, it doesn't load existing files and it returns the paths of the dummy files relative
   # to the dummy_data zip file root
   lowerCamelCase_  				=						load_existing_dummy_data

   # TODO(PVP, QL) might need to make this more general
   lowerCamelCase_  				=						str(UpperCamelCase  )
   # to be downloaded
   lowerCamelCase_  				=						None
   lowerCamelCase_  				=						None






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._dummy_file is None:
    lowerCamelCase_  				=						self.download_dummy_data()
   return self._dummy_file






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self.config is not None:
    # structure is dummy / config_name / version_name
    return os.path.join("dummy"    ,						self.config.name    ,						self.version_name  )
   # structure is dummy / version_name
   return os.path.join("dummy"    ,						self.version_name  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return os.path.join(self.dummy_data_folder    ,						"dummy_data.zip"  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						(
       self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
   )

   lowerCamelCase_  				=						cached_path(
       UpperCamelCase    ,						cache_dir=self.cache_dir    ,						extract_compressed_file=UpperCamelCase    ,						force_extract=UpperCamelCase  )

   return os.path.join(UpperCamelCase    ,						self.dummy_file_name  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return os.path.join(self.datasets_scripts_dir    ,						self.dataset_name    ,						self.dummy_zip_file  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._bucket_url is None:
    lowerCamelCase_  				=						hf_github_url(self.dataset_name    ,						self.dummy_zip_file.replace(os.sep    ,						"/"  )  )
   return self._bucket_url






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   # return full path if its a dir
   if os.path.isdir(self.dummy_file  ):
    return self.dummy_file
   # else cut off path to file -> example `xsum`.
   return "/".join(self.dummy_file.replace(os.sep    ,						"/"  ).split("/"  )[:-1]  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   if self.load_existing_dummy_data:
    # dummy data is downloaded and tested
    lowerCamelCase_  				=						self.dummy_file
   else:
    # dummy data cannot be downloaded and only the path to dummy file is returned
    lowerCamelCase_  				=						self.dummy_file_name

   # special case when data_url is a dict
   if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
    return self.create_dummy_data_dict(UpperCamelCase    ,						UpperCamelCase  )
   elif isinstance(UpperCamelCase    ,						(list, tuple)  ):
    return self.create_dummy_data_list(UpperCamelCase    ,						UpperCamelCase  )
   else:
    return self.create_dummy_data_single(UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   return self.download_and_extract(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return self.download_and_extract(UpperCamelCase  )






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







   """simple docstring"""
   return path






  def 						snake_case	(					self  ):







   """simple docstring"""
   return {}






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{}
   for key, single_urls in data_url.items():
    for download_callback in self.download_callbacks:
     if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
      for single_url in single_urls:
       download_callback(UpperCamelCase  )
     else:
      lowerCamelCase_  				=						single_urls
      download_callback(UpperCamelCase  )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
     lowerCamelCase_  				=						[os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(Path(UpperCamelCase  ).name  )  ) for x in single_urls]
    else:
     lowerCamelCase_  				=						single_urls
     lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(Path(UpperCamelCase  ).name  )  )
    lowerCamelCase_  				=						value

   # make sure that values are unique
   if all(isinstance(UpperCamelCase    ,						UpperCamelCase  ) for i in dummy_data_dict.values()  ) and len(set(dummy_data_dict.values()  )  ) < len(
       dummy_data_dict.values()  ):
    # append key to value to make its name unique
    lowerCamelCase_  				=						{key: value + key for key, value in dummy_data_dict.items()}

   return dummy_data_dict






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
   lowerCamelCase_  				=						all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"    ,						UpperCamelCase  )  ) for url in data_url  )
   lowerCamelCase_  				=						all(
       url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"  ) for url in data_url  )
   if data_url and (is_tf_records or is_pubmed_records):
    lowerCamelCase_  				=						[data_url[0]] * len(UpperCamelCase  )
   for single_url in data_url:
    for download_callback in self.download_callbacks:
     download_callback(UpperCamelCase  )
    # we force the name of each key to be the last file / folder name of the url path
    # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(single_url.split("/"  )[-1]  )  )
    dummy_data_list.append(UpperCamelCase  )
   return dummy_data_list






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   for download_callback in self.download_callbacks:
    download_callback(UpperCamelCase  )
   # we force the name of each key to be the last file / folder name of the url path
   # if the url has arguments, we need to encode them with urllib.parse.quote_plus
   lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(data_url.split("/"  )[-1]  )  )
   if os.path.exists(UpperCamelCase  ) or not self.load_existing_dummy_data:
    return value
   else:
    # Backward compatibility, maybe deprecate at one point.
    # For many datasets with single url calls to dl_manager.download_and_extract,
    # the dummy_data.zip file is actually the zipped downloaded file
    # while now we expected the dummy_data.zip file to be a directory containing
    # the downloaded file.
    return path_to_dummy_data






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   def _iter_archive_members(UpperCamelCase  ):
    # this preserves the order of the members inside the ZIP archive
    lowerCamelCase_  				=						Path(self.dummy_file  ).parent
    lowerCamelCase_  				=						path.relative_to(UpperCamelCase  )
    with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
     lowerCamelCase_  				=						zip_file.namelist()
    for member in members:
     if member.startswith(relative_path.as_posix()  ):
      yield dummy_parent_path.joinpath(UpperCamelCase  )

   lowerCamelCase_  				=						Path(UpperCamelCase  )
   lowerCamelCase_  				=						_iter_archive_members(UpperCamelCase  ) if self.use_local_dummy_data else path.rglob("*"  )
   for file_path in file_paths:
    if file_path.is_file() and not file_path.name.startswith((".", "__")  ):
     yield file_path.relative_to(UpperCamelCase  ).as_posix(), file_path.open("rb"  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   if not isinstance(UpperCamelCase    ,						UpperCamelCase  ):
    lowerCamelCase_  				=						[paths]
   for path in paths:
    if os.path.isfile(UpperCamelCase  ):
     if os.path.basename(UpperCamelCase  ).startswith((".", "__")  ):
      return
     yield path
    else:
     for dirpath, dirnames, filenames in os.walk(UpperCamelCase  ):
      if os.path.basename(UpperCamelCase  ).startswith((".", "__")  ):
       continue
      dirnames.sort()
      for filename in sorted(UpperCamelCase  ):
       if filename.startswith((".", "__")  ):
        continue
       yield os.path.join(UpperCamelCase    ,						UpperCamelCase  )



