

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 SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['bs4']							)
				super().__init__(**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= []
				snake_case_      		= []
				snake_case_      		= element if element.name else element.parent
				for parent in child.parents:  # type: bs4.element.Tag
					snake_case_      		= 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							)							)
					snake_case_      		= parent
				xpath_tags.reverse()
				xpath_subscripts.reverse()
				return xpath_tags, xpath_subscripts


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= BeautifulSoup(__UpperCamelCase ,				'html.parser'							)

				snake_case_      		= []
				snake_case_      		= []
				snake_case_      		= []

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

						snake_case_      		= html.unescape(__UpperCamelCase							).strip()
						if not text_in_this_tag:
							continue

						all_doc_strings.append(__UpperCamelCase							)

						snake_case_		,       snake_case_      		= 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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


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







			def __call__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= False

				# Check that strings has a valid type
				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= True
				elif isinstance(__UpperCamelCase ,				(list, tuple)							):
					if len(__UpperCamelCase							) == 0 or isinstance(html_strings[0] ,				__UpperCamelCase							):
						snake_case_      		= 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							)}."""							)

				snake_case_      		= bool(isinstance(__UpperCamelCase ,				(list, tuple)							) and (isinstance(html_strings[0] ,				__UpperCamelCase							))							)

				if not is_batched:
					snake_case_      		= [html_strings]

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

				# return as Dict
				snake_case_      		= {'nodes': nodes, 'xpaths': xpaths}
				snake_case_      		= BatchFeature(data=__UpperCamelCase ,				tensor_type=__UpperCamelCase							)

				return encoded_inputs



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_      		= TapasConfig.from_json_file(lowercase__	)
	# set absolute/relative position embeddings parameter
	snake_case_      		= reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= True
		# hparam_utils.py hparams
		snake_case_      		= 0.66_4694
		snake_case_      		= 0.20_7951
		snake_case_      		= 0.12_1194
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= False
		snake_case_      		= 0.035_2513

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= False
		# hparam_utils.py hparams
		snake_case_      		= 36.4519
		snake_case_      		= 0.90_3421
		snake_case_      		= 222.088
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= 0.76_3141

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "TABFACT":
		snake_case_      		= TapasForSequenceClassification(config=lowercase__	)
	elif task == "MLM":
		snake_case_      		= TapasForMaskedLM(config=lowercase__	)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_      		= TapasModel(config=lowercase__	)
	else:
		raise ValueError(f"""Task {task} not supported."""	)

	print(f"""Building PyTorch model from configuration: {config}"""	)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(lowercase__			, lowercase__			, lowercase__	)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
	model.save_pretrained(lowercase__	)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""	)
	snake_case_      		= TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'			, model_max_length=512	)
	tokenizer.save_pretrained(lowercase__	)

	print('Used relative position embeddings:'			, model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
		)
		parser.add_argument(
		    '--reset_position_index_per_cell',
		    default=False,
		    action='store_true',
		    help='Whether to use relative position embeddings or not. Defaults to True.',
		)
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--tapas_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained TAPAS model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		A											=     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
