
"""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


_lowercase							=				logging.getLogger(__name__)

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Union[str, Any]          =   """summarization"""
							_lowerCamelCase					:			Optional[Any]          =   ["""loss"""]
							_lowerCamelCase					:			str          =   ROUGE_KEYS
							_lowerCamelCase					:			Dict          =   """rouge2"""
							def __init__(     self  ,    _lowerCamelCase  ,    **_lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										if hparams.sortish_sampler and hparams.gpus > 1:
													__lowercase       =       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       )
										__lowercase       =       Path(self.output_dir       ) / "metrics.json"
										__lowercase       =       Path(self.output_dir       ) / "hparams.pkl"
										pickle_save(self.hparams  ,    self.hparams_save_path       )
										__lowercase       =       0
										__lowercase       =       defaultdict(_lowerCamelCase       )
										__lowercase       =       self.config.model_type
										__lowercase       =       self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

										__lowercase       =       {
										    "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()       )

										__lowercase       =       get_git_info()["repo_sha"]
										__lowercase       =       hparams.num_workers
										__lowercase       =       None  # default to config
										if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer  ,    _lowerCamelCase       ):
													__lowercase       =       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
													__lowercase       =       self.decoder_start_token_id
										__lowercase       =       (
										    SeqaSeqDataset if hasattr(self.tokenizer  ,    "prepare_seq2seq_batch"       ) else LegacySeqaSeqDataset
										)
										__lowercase       =       False
										__lowercase       =       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:
													__lowercase       =       self.hparams.eval_max_gen_length
										else:
													__lowercase       =       self.model.config.max_length
										__lowercase       =       self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Dict[str, List[str]]:




										'''simple docstring'''
										__lowercase       =       {
										    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"       )

										__lowercase       =       True
										return readable_batch
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    **_lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										return self.model(_lowerCamelCase  ,    **_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.tokenizer.batch_decode(
										    _lowerCamelCase  ,    skip_special_tokens=_lowerCamelCase  ,    clean_up_tokenization_spaces=_lowerCamelCase       )
										return lmap(str.strip  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




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

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

													assert lm_logits.shape[-1] == self.vocab_size
													__lowercase       =       ce_loss_fct(lm_logits.view(-1  ,    lm_logits.shape[-1]       )  ,    tgt_ids.view(-1       )       )
										else:
													__lowercase       =       nn.functional.log_softmax(_lowerCamelCase  ,    dim=-1       )
													__lowercase   ,					__lowercase       =       label_smoothed_nll_loss(
													    _lowerCamelCase  ,    _lowerCamelCase  ,    self.hparams.label_smoothing  ,    ignore_index=_lowerCamelCase       )
										return (loss,)
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self.tokenizer.pad_token_id
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       self._step(_lowerCamelCase       )

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




										'''simple docstring'''
										return self._generative_step(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase="val"       )			->							Dict:




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




										'''simple docstring'''
										return calculate_rouge(_lowerCamelCase  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							dict:




										'''simple docstring'''
										__lowercase       =       time.time()

										# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
										__lowercase       =       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  ,    )
										__lowercase       =       (time.time() - ta) / batch["input_ids"].shape[0]
										__lowercase       =       self.ids_to_clean_text(_lowerCamelCase       )
										__lowercase       =       self.ids_to_clean_text(batch["labels"]       )
										__lowercase       =       self._step(_lowerCamelCase       )
										__lowercase       =       dict(zip(self.loss_names  ,    _lowerCamelCase       )       )
										__lowercase       =       self.calc_generative_metrics(_lowerCamelCase  ,    _lowerCamelCase       )
										__lowercase       =       np.mean(lmap(_lowerCamelCase  ,    _lowerCamelCase       )       )
										base_metrics.update(gen_time=_lowerCamelCase  ,    gen_len=_lowerCamelCase  ,    preds=_lowerCamelCase  ,    target=_lowerCamelCase  ,    **_lowerCamelCase       )
										return base_metrics
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										return self._generative_step(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return self.validation_epoch_end(_lowerCamelCase  ,    prefix="test"       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							SeqaSeqDataset:




										'''simple docstring'''
										__lowercase       =       self.n_obs[type_path]
										__lowercase       =       self.target_lens[type_path]
										__lowercase       =       self.dataset_class(
										    self.tokenizer  ,    type_path=_lowerCamelCase  ,    n_obs=_lowerCamelCase  ,    max_target_length=_lowerCamelCase  ,    **self.dataset_kwargs  ,    )
										return dataset
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = False       )			->							DataLoader:




										'''simple docstring'''
										__lowercase       =       self.get_dataset(_lowerCamelCase       )

										if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
													__lowercase       =       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":
													__lowercase       =       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   SCREAMING_SNAKE_CASE						(     self       )			->							DataLoader:




										'''simple docstring'''
										__lowercase       =       self.get_dataloader("train"  ,    batch_size=self.hparams.train_batch_size  ,    shuffle=_lowerCamelCase       )
										return dataloader
							def   SCREAMING_SNAKE_CASE						(     self       )			->							DataLoader:




										'''simple docstring'''
										return self.get_dataloader("val"  ,    batch_size=self.hparams.eval_batch_size       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							DataLoader:




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





							@staticmethod
							def   SCREAMING_SNAKE_CASE						(     _lowerCamelCase  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										BaseTransformer.add_model_specific_args(_lowerCamelCase  ,    _lowerCamelCase       )
										add_generic_args(_lowerCamelCase  ,    _lowerCamelCase       )
										parser.add_argument(
										    "--max_source_length"  ,    default=1_024  ,    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=142  ,    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=142  ,    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=500  ,    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 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Union[str, Any]          =   """translation"""
							_lowerCamelCase					:			int          =   ["""loss"""]
							_lowerCamelCase					:			Union[str, Any]          =   ["""bleu"""]
							_lowerCamelCase					:			List[str]          =   """bleu"""
							def __init__(     self  ,    _lowerCamelCase  ,    **_lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										super().__init__(_lowerCamelCase  ,    **_lowerCamelCase       )
										__lowercase       =       hparams.src_lang
										__lowercase       =       hparams.tgt_lang





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							dict:




										'''simple docstring'''
										return calculate_bleu(_lowerCamelCase  ,    _lowerCamelCase       )





def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__=None      )						->SummarizationModule:
			Path(args.output_dir      ).mkdir(exist_ok=__magic_name__      )
			check_output_dir(__magic_name__			,   expected_items=3      )

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

						__lowercase       =       os.environ.get("WANDB_PROJECT"			,   __magic_name__      )
						__lowercase       =       WandbLogger(name=model.output_dir.name			,   project=__magic_name__      )

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

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

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

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

			__lowercase       =       ""
			__lowercase       =       sorted(glob.glob(os.path.join(args.output_dir			,   "*.ckpt"      )			,   recursive=__magic_name__      )      )
			if checkpoints:
						__lowercase       =       checkpoints[-1]
						__lowercase       =       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__":
					_lowercase							=				argparse.ArgumentParser()
					_lowercase							=				pl.Trainer.add_argparse_args(parser)
					_lowercase							=				SummarizationModule.add_model_specific_args(parser, os.getcwd())

					_lowercase							=				parser.parse_args()

					main(args)







"""simple docstring"""






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
					import bsa
					from bsa import BeautifulSoup


_lowercase							=				logging.get_logger(__name__)

class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    **_lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										requires_backends(self  ,    ["bs4"]       )
										super().__init__(**_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       []
										__lowercase       =       []
										__lowercase       =       element if element.name else element.parent
										for parent in child.parents:  # type: bs4.element.Tag
													__lowercase       =       parent.find_all(child.name  ,    recursive=_lowerCamelCase       )
													xpath_tags.append(child.name       )
													xpath_subscripts.append(
													    0 if 1 == len(_lowerCamelCase       ) else next(i for i, s in enumerate(_lowerCamelCase  ,    1       ) if s is child       )       )
													__lowercase       =       parent
										xpath_tags.reverse()
										xpath_subscripts.reverse()
										return xpath_tags, xpath_subscripts
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       BeautifulSoup(_lowerCamelCase  ,    "html.parser"       )

										__lowercase       =       []
										__lowercase       =       []
										__lowercase       =       []

										for element in html_code.descendants:
													if type(_lowerCamelCase       ) == bsa.element.NavigableString:
																if type(element.parent       ) != bsa.element.Tag:
																			continue

																__lowercase       =       html.unescape(_lowerCamelCase       ).strip()
																if not text_in_this_tag:
																			continue

																all_doc_strings.append(_lowerCamelCase       )

																__lowercase   ,					__lowercase       =       self.xpath_soup(_lowerCamelCase       )
																stringaxtag_seq.append(_lowerCamelCase       )
																stringaxsubs_seq.append(_lowerCamelCase       )

										if len(_lowerCamelCase       ) != len(_lowerCamelCase       ):
													raise ValueError("Number of doc strings and xtags does not correspond"       )
										if len(_lowerCamelCase       ) != len(_lowerCamelCase       ):
													raise ValueError("Number of doc strings and xsubs does not correspond"       )

										return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       ""
										for tagname, subs in zip(_lowerCamelCase  ,    _lowerCamelCase       ):
													xpath += f'''/{tagname}'''
													if subs != 0:
																xpath += f'''[{subs}]'''
										return xpath





							def __call__(     self  ,    _lowerCamelCase       )			->							BatchFeature:




										'''simple docstring'''
										__lowercase       =       False

										# Check that strings has a valid type
										if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
													__lowercase       =       True
										elif isinstance(_lowerCamelCase  ,    (list, tuple)       ):
													if len(_lowerCamelCase       ) == 0 or isinstance(html_strings[0]  ,    _lowerCamelCase       ):
																__lowercase       =       True

										if not valid_strings:
													raise ValueError(
													    "HTML strings must of type `str`, `List[str]` (batch of examples), "
													    f'''but is of type {type(_lowerCamelCase       )}.'''       )

										__lowercase       =       bool(isinstance(_lowerCamelCase  ,    (list, tuple)       ) and (isinstance(html_strings[0]  ,    _lowerCamelCase       ))       )

										if not is_batched:
													__lowercase       =       [html_strings]

										# Get nodes + xpaths
										__lowercase       =       []
										__lowercase       =       []
										for html_string in html_strings:
													__lowercase   ,					__lowercase   ,					__lowercase       =       self.get_three_from_single(_lowerCamelCase       )
													nodes.append(_lowerCamelCase       )
													__lowercase       =       []
													for node, tag_list, sub_list in zip(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       ):
																__lowercase       =       self.construct_xpath(_lowerCamelCase  ,    _lowerCamelCase       )
																xpath_strings.append(_lowerCamelCase       )
													xpaths.append(_lowerCamelCase       )

										# return as Dict
										__lowercase       =       {"nodes": nodes, "xpaths": xpaths}
										__lowercase       =       BatchFeature(data=_lowerCamelCase  ,    tensor_type=_lowerCamelCase       )

										return encoded_inputs





