"""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


__A						       =      logging.get_logger(__name__)





class    snake_case     (    __snake_case  ):

				def __init__(     self							:    Any					,  **UpperCamelCase__							:    str)->       Dict:



						'''simple docstring'''


						requires_backends(self					,  ["bs4"])
						super().__init__(**UpperCamelCase__)

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    List[Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      []
						__lowerCAmelCase:	Any   				=      []
						__lowerCAmelCase:	Optional[int]   				=      element if element.name else element.parent
						for parent in child.parents:  # type: bs4.element.Tag
								__lowerCAmelCase:	Dict   				=      parent.find_all(child.name					,  recursive=UpperCamelCase__)
								xpath_tags.append(child.name)
								xpath_subscripts.append(
								    0 if 1 == len(UpperCamelCase__) else next(i for i, s in enumerate(UpperCamelCase__					,  1) if s is child))
								__lowerCAmelCase:	str   				=      parent
						xpath_tags.reverse()
						xpath_subscripts.reverse()
						return xpath_tags, xpath_subscripts

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Any)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      BeautifulSoup(UpperCamelCase__					,  "html.parser")

						__lowerCAmelCase:	Dict   				=      []
						__lowerCAmelCase:	Union[str, Any]   				=      []
						__lowerCAmelCase:	Optional[Any]   				=      []

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

										__lowerCAmelCase:	str   				=      html.unescape(UpperCamelCase__).strip()
										if not text_in_this_tag:
												continue

										all_doc_strings.append(UpperCamelCase__)

										__lowerCAmelCase:	List[Any]   				=      self.xpath_soup(UpperCamelCase__)
										stringaxtag_seq.append(UpperCamelCase__)
										stringaxsubs_seq.append(UpperCamelCase__)

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

						return all_doc_strings, stringaxtag_seq, stringaxsubs_seq

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[str])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      ""
						for tagname, subs in zip(UpperCamelCase__					,  UpperCamelCase__):
								xpath += f"/{tagname}"
								if subs != 0:
										xpath += f"[{subs}]"
						return xpath




				def __call__(     self							:    Tuple					,  UpperCamelCase__							:    Optional[Any])->       BatchFeature:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      False

						# Check that strings has a valid type
						if isinstance(UpperCamelCase__					,  UpperCamelCase__):
								__lowerCAmelCase:	Union[str, Any]   				=      True
						elif isinstance(UpperCamelCase__					,  (list, tuple)):
								if len(UpperCamelCase__) == 0 or isinstance(html_strings[0]					,  UpperCamelCase__):
										__lowerCAmelCase:	Tuple   				=      True

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

						__lowerCAmelCase:	int   				=      bool(isinstance(UpperCamelCase__					,  (list, tuple)) and (isinstance(html_strings[0]					,  UpperCamelCase__)))

						if not is_batched:
								__lowerCAmelCase:	Any   				=      [html_strings]

						# Get nodes + xpaths
						__lowerCAmelCase:	Optional[int]   				=      []
						__lowerCAmelCase:	int   				=      []
						for html_string in html_strings:
								__lowerCAmelCase:	str   				=      self.get_three_from_single(UpperCamelCase__)
								nodes.append(UpperCamelCase__)
								__lowerCAmelCase:	Any   				=      []
								for node, tag_list, sub_list in zip(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__):
										__lowerCAmelCase:	Optional[int]   				=      self.construct_xpath(UpperCamelCase__					,  UpperCamelCase__)
										xpath_strings.append(UpperCamelCase__)
								xpaths.append(UpperCamelCase__)

						# return as Dict
						__lowerCAmelCase:	Optional[int]   				=      {"nodes": nodes, "xpaths": xpaths}
						__lowerCAmelCase:	Dict   				=      BatchFeature(data=UpperCamelCase__					,  tensor_type=UpperCamelCase__)

						return encoded_inputs





"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:	Optional[int]   				=      model
						__lowerCAmelCase:	Tuple   				=      2
						__lowerCAmelCase:	List[Any]   				=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)
		lightning_model.load_state_dict(ckpt["state_dict"]			)

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# transfer weights
		longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()			)
		longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()			)
		longformer_for_qa.eval()

		# save model
		longformer_for_qa.save_pretrained(__SCREAMING_SNAKE_CASE			)

		print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)


