


'''simple docstring'''



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


__A		={
    '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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None   ):
			# Initialise PyTorch model
			UpperCAmelCase__					:   List[str]       				=       XLNetConfig.from_json_file(UpperCamelCase__   )

			UpperCAmelCase__					:   List[str]       				=       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}'''   )
						UpperCAmelCase__					:   Optional[Any]       				=       finetuning_task
						UpperCAmelCase__					:   Any       				=       GLUE_TASKS_NUM_LABELS[finetuning_task]
						UpperCAmelCase__					:   Optional[int]       				=       XLNetForSequenceClassification(UpperCamelCase__   )
			elif "squad" in finetuning_task:
						UpperCAmelCase__					:   List[Any]       				=       finetuning_task
						UpperCAmelCase__					:   Tuple       				=       XLNetForQuestionAnswering(UpperCamelCase__   )
			else:
						UpperCAmelCase__					:   Any       				=       XLNetLMHeadModel(UpperCamelCase__   )

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

			# Save pytorch-model
			UpperCAmelCase__					:   Any       				=       os.path.join(UpperCamelCase__	, UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       os.path.join(UpperCamelCase__	, UpperCamelCase__   )
			print(f'''Save PyTorch model to {os.path.abspath(UpperCamelCase__   )}'''   )
			torch.save(model.state_dict()	, UpperCamelCase__   )
			print(f'''Save configuration file to {os.path.abspath(UpperCamelCase__   )}'''   )
			with open(UpperCamelCase__	, """w"""	, encoding="""utf-8"""   ) as f:
						f.write(config.to_json_string()   )


if __name__ == "__main__":
		__A		=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',
		)
		__A		=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
		)



'''simple docstring'''



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__A		=logging.get_logger(__name__)

__A		={'vocab_file': 'spiece.model'}

__A		={
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}


class 						_snake_case   (       a__		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=False							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<sep>"							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="<cls>"							,							_lowerCamelCase="<mask>"							,							_lowerCamelCase=["<eop>", "<eod>"]							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Dict       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else mask_token

								UpperCAmelCase__					:   Any       				=       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=_lowerCamelCase							,							remove_space=_lowerCamelCase							,							keep_accents=_lowerCamelCase							,							bos_token=_lowerCamelCase							,							eos_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							sep_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							cls_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**_lowerCamelCase							,							)

								UpperCAmelCase__					:   List[str]       				=       3

								UpperCAmelCase__					:   str       				=       do_lower_case
								UpperCAmelCase__					:   Tuple       				=       remove_space
								UpperCAmelCase__					:   List[str]       				=       keep_accents
								UpperCAmelCase__					:   Any       				=       vocab_file

								UpperCAmelCase__					:   str       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(_lowerCamelCase)

								try:
											import jieba
								except ModuleNotFoundError as error:
											raise error.__class__(
											    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
											    """See https://pypi.org/project/jieba/ for installation.""")
								UpperCAmelCase__					:   Union[str, Any]       				=       jieba
								UpperCAmelCase__					:   int       				=       str.maketrans(""" \n"""							,							"""\u2582\u2583""")




					@property
					# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
					def 			snake_case__  ( self):
								return len(self.sp_model)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       {self.convert_ids_to_tokens(_lowerCamelCase): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




					def __getstate__( self):
								UpperCAmelCase__					:   List[str]       				=       self.__dict__.copy()
								UpperCAmelCase__					:   Optional[int]       				=       None
								return state




					def __setstate__( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       d

								# for backward compatibility
								if not hasattr(self							,							"""sp_model_kwargs"""):
											UpperCAmelCase__					:   Tuple       				=       {}

								UpperCAmelCase__					:   Union[str, Any]       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if self.remove_space:
											UpperCAmelCase__					:   Tuple       				=       """ """.join(inputs.strip().split())
								else:
											UpperCAmelCase__					:   str       				=       inputs
								UpperCAmelCase__					:   List[Any]       				=       outputs.replace("""``"""							,							"""\"""").replace("""''"""							,							"""\"""")

								if not self.keep_accents:
											UpperCAmelCase__					:   str       				=       unicodedata.normalize("""NFKD"""							,							_lowerCamelCase)
											UpperCAmelCase__					:   Any       				=       """""".join([c for c in outputs if not unicodedata.combining(_lowerCamelCase)])
								if self.do_lower_case:
											UpperCAmelCase__					:   Optional[int]       				=       outputs.lower()

								return outputs




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       self.preprocess_text(_lowerCamelCase)
								UpperCAmelCase__					:   Dict       				=       self.sp_model.encode(_lowerCamelCase							,							out_type=_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       []
								for piece in pieces:
											if len(_lowerCamelCase) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
														UpperCAmelCase__					:   str       				=       self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase							,							""""""))
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																	if len(cur_pieces[0]) == 1:
																				UpperCAmelCase__					:   Optional[int]       				=       cur_pieces[1:]
																	else:
																				UpperCAmelCase__					:   Optional[int]       				=       cur_pieces[0][1:]
														cur_pieces.append(piece[-1])
														new_pieces.extend(_lowerCamelCase)
											else:
														new_pieces.append(_lowerCamelCase)

								return new_pieces




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.PieceToId(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.IdToPiece(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   str       				=       """""".join(_lowerCamelCase).replace(_lowerCamelCase							,							""" """).strip()
								return out_string




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   Optional[Any]       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Optional[int]       				=       [self.cls_token_id]
								if token_ids_a is None:
											return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):

								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_lowerCamelCase							,							token_ids_a=_lowerCamelCase							,							already_has_special_tokens=_lowerCamelCase)

								if token_ids_a is not None:
											return ([0] * len(_lowerCamelCase)) + [1] + ([0] * len(_lowerCamelCase)) + [1, 1]
								return ([0] * len(_lowerCamelCase)) + [1, 1]




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   int       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Tuple       				=       [2]

								if token_ids_a is None:
											return len(token_ids_a + sep) * [0] + cls_segment_id
								return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__					:   str       				=       os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

								if os.path.abspath(self.vocab_file) != os.path.abspath(_lowerCamelCase) and os.path.isfile(self.vocab_file):
											copyfile(self.vocab_file							,							_lowerCamelCase)
								elif not os.path.isfile(self.vocab_file):
											with open(_lowerCamelCase							,							"""wb""") as fi:
														UpperCAmelCase__					:   str       				=       self.sp_model.serialized_model_proto()
														fi.write(_lowerCamelCase)

								return (out_vocab_file,)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       super()._decode(*_lowerCamelCase							,							**_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       text.replace(""" """							,							"""""").replace("""\u2582"""							,							""" """).replace("""\u2583"""							,							"""\n""")
								return text