



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


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):







							def __init__(     self,    **__a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					BeautifulSoup(__a,    "html.parser")

														_lowerCAmelCase  :							Optional[int]				     =					[]
														_lowerCAmelCase  :							List[str]				     =					[]
														_lowerCAmelCase  :							List[Any]				     =					[]

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

																												_lowerCAmelCase  :							Dict				     =					html.unescape(__a).strip()
																												if not text_in_this_tag:
																																			continue

																												all_doc_strings.append(__a)

																												_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					self.xpath_soup(__a)
																												stringaxtag_seq.append(__a)
																												stringaxsubs_seq.append(__a)

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

														return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







							def      snake_case__   (     self,    __a,    __a):

														'''simple docstring'''

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







							def __call__(     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					False

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

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

														_lowerCAmelCase  :							Optional[Any]				     =					bool(isinstance(__a,    (list, tuple)) and (isinstance(html_strings[0],    __a)))

														if not is_batched:
																					_lowerCAmelCase  :							Tuple				     =					[html_strings]

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

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

														return encoded_inputs





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


_snake_case	=						{
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					XLNetConfig.from_json_file(_lowerCamelCase						)

							_lowerCAmelCase  :							Any				     =					finetuning_task.lower() if finetuning_task is not None else ""
							if finetuning_task in GLUE_TASKS_NUM_LABELS:
														print(F"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"						)
														_lowerCAmelCase  :							Any				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					GLUE_TASKS_NUM_LABELS[finetuning_task]
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetForSequenceClassification(_lowerCamelCase						)
							elif "squad" in finetuning_task:
														_lowerCAmelCase  :							Union[str, Any]				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					XLNetForQuestionAnswering(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetLMHeadModel(_lowerCamelCase						)

							# Load weights from tf checkpoint
							load_tf_weights_in_xlnet(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)

							# Save pytorch-model
							_lowerCAmelCase  :							Optional[int]				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							Dict				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							print(F"Save PyTorch model to {os.path.abspath(_lowerCamelCase						)}"						)
							torch.save(model.state_dict()       ,				_lowerCamelCase						)
							print(F"Save configuration file to {os.path.abspath(_lowerCamelCase						)}"						)
							with open(_lowerCamelCase       ,				"w"       ,				encoding="utf-8"						) as f:
														f.write(config.to_json_string()						)


if __name__ == "__main__":
						_snake_case	=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
						)
						parser.add_argument(
						    "--xlnet_config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained XLNet model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the folder to store the PyTorch model or dataset/vocab.",
						)
						parser.add_argument(
						    "--finetuning_task",
						    default=None,
						    type=str,
						    help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
						)
						_snake_case	=						parser.parse_args()
						print(args)

						convert_xlnet_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
						)
