"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 							_UpperCAmelCase		(       lowerCAmelCase_     ):





		"""simple docstring"""




		if (
		    (cp >= 0X4e00 and cp <= 0X9fff)
		    or (cp >= 0X3400 and cp <= 0X4dbf)  #
		    or (cp >= 0X20000 and cp <= 0X2a6df)  #
		    or (cp >= 0X2a700 and cp <= 0X2b73f)  #
		    or (cp >= 0X2b740 and cp <= 0X2b81f)  #
		    or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
		    or (cp >= 0Xf900 and cp <= 0Xfaff)
		    or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
		):  #
				return True

		return False







def 							_UpperCAmelCase		(       lowerCAmelCase_     ):





		"""simple docstring"""




		for char in word:
				lowerCAmelCase__        =   ord(lowerCAmelCase_     )
				if not _is_chinese_char(lowerCAmelCase_     ):
						return 0
		return 1







def 							_UpperCAmelCase		(       lowerCAmelCase_     ):





		"""simple docstring"""




		lowerCAmelCase__        =   set()

		for token in tokens:
				lowerCAmelCase__        =   len(lowerCAmelCase_     ) > 1 and is_chinese(lowerCAmelCase_     )
				if chinese_word:
						word_set.add(lowerCAmelCase_     )
		lowerCAmelCase__        =   list(lowerCAmelCase_     )
		return word_list







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_     ):





		"""simple docstring"""




		if not chinese_word_set:
				return bert_tokens
		lowerCAmelCase__        =   max([len(lowerCAmelCase_     ) for w in chinese_word_set]     )

		lowerCAmelCase__        =   bert_tokens
		lowerCAmelCase__        =   0, len(lowerCAmelCase_     )
		while start < end:
				lowerCAmelCase__        =   True
				if is_chinese(bert_word[start]     ):
						lowerCAmelCase__        =   min(end - start			,  lowerCAmelCase_     )
						for i in range(lowerCAmelCase_			,  1			,  -1     ):
								lowerCAmelCase__        =   """""".join(bert_word[start : start + i]     )
								if whole_word in chinese_word_set:
										for j in range(start + 1			,  start + i     ):
												lowerCAmelCase__        =   """##""" + bert_word[j]
										lowerCAmelCase__        =   start + i
										lowerCAmelCase__        =   False
										break
				if single_word:
						start += 1
		return bert_word







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []

		for i in range(0			,  len(lowerCAmelCase_     )			,  100     ):
				lowerCAmelCase__        =   ltp_tokenizer.pipeline(lines[i : i + 100]			,  tasks=["""cws"""]     ).cws
				lowerCAmelCase__        =   [get_chinese_word(lowerCAmelCase_     ) for r in res]
				ltp_res.extend(lowerCAmelCase_     )
		assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

		lowerCAmelCase__        =   []
		for i in range(0			,  len(lowerCAmelCase_     )			,  100     ):
				lowerCAmelCase__        =   bert_tokenizer(lines[i : i + 100]			,  add_special_tokens=lowerCAmelCase_			,  truncation=lowerCAmelCase_			,  max_length=512     )
				bert_res.extend(res["""input_ids"""]     )
		assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

		lowerCAmelCase__        =   []
		for input_ids, chinese_word in zip(lowerCAmelCase_			,  lowerCAmelCase_     ):
				lowerCAmelCase__        =   []
				for id in input_ids:
						lowerCAmelCase__        =   bert_tokenizer._convert_id_to_token(lowerCAmelCase_     )
						input_tokens.append(lowerCAmelCase_     )
				lowerCAmelCase__        =   add_sub_symbol(lowerCAmelCase_			,  lowerCAmelCase_     )
				lowerCAmelCase__        =   []
				# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
				for i, token in enumerate(lowerCAmelCase_     ):
						if token[:2] == "##":
								lowerCAmelCase__        =   token[2:]
								# save chinese tokens' pos
								if len(lowerCAmelCase_     ) == 1 and _is_chinese_char(ord(lowerCAmelCase_     )     ):
										ref_id.append(lowerCAmelCase_     )
				ref_ids.append(lowerCAmelCase_     )

		assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

		return ref_ids







def 							_UpperCAmelCase		(       lowerCAmelCase_     ):





		"""simple docstring"""




		with open(args.file_name			,  """r"""			,  encoding="""utf-8"""     ) as f:
				lowerCAmelCase__        =   f.readlines()
		lowerCAmelCase__        =   [line.strip() for line in data if len(lowerCAmelCase_     ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
		lowerCAmelCase__        =   LTP(args.ltp     )  # faster in GPU device
		lowerCAmelCase__        =   BertTokenizer.from_pretrained(args.bert     )

		lowerCAmelCase__        =   prepare_ref(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_     )

		with open(args.save_path			,  """w"""			,  encoding="""utf-8"""     ) as f:
				lowerCAmelCase__        =   [json.dumps(lowerCAmelCase_     ) + """\n""" for ref in ref_ids]
				f.writelines(lowerCAmelCase_     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser(description="prepare_chinese_ref")
			parser.add_argument(
			    "--file_name",
			    required=False,
			    type=str,
			    default="./resources/chinese-demo.txt",
			    help="file need process, same as training data in lm",
			)
			parser.add_argument(
			    "--ltp",
			    required=False,
			    type=str,
			    default="./resources/ltp",
			    help="resources for LTP tokenizer, usually a path",
			)
			parser.add_argument(
			    "--bert",
			    required=False,
			    type=str,
			    default="./resources/robert",
			    help="resources for Bert tokenizer",
			)
			parser.add_argument(
			    "--save_path",
			    required=False,
			    type=str,
			    default="./resources/ref.txt",
			    help="path to save res",
			)

			__lowerCAmelCase    :							List[str]													=						parser.parse_args()
			main(args)





"""simple docstring"""




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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__        =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   True
				# hparam_utils.py hparams
				lowerCAmelCase__        =   0.66_46_94
				lowerCAmelCase__        =   0.20_79_51
				lowerCAmelCase__        =   0.12_11_94
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   False
				lowerCAmelCase__        =   0.0_35_25_13

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   False
				# hparam_utils.py hparams
				lowerCAmelCase__        =   36.45_19
				lowerCAmelCase__        =   0.90_34_21
				lowerCAmelCase__        =   2_22.0_88
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   0.76_31_41

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   TapasModel(config=lowerCamelCase__     )
		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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

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

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

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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						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,
			)




