



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{"vocab_file": "sentencepiece.model"}

_snake_case	=						{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_snake_case	=						{
    "google/rembert": 256,
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	VOCAB_FILES_NAMES
							lowerCamelCase__         =	PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__         =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







							def __init__(     self,    __a,    __a=False,    __a=True,    __a=True,    __a="[CLS]",    __a="[SEP]",    __a="[UNK]",    __a="[SEP]",    __a="[PAD]",    __a="[CLS]",    __a="[MASK]",    **__a,    ):

														'''simple docstring'''

														super().__init__(
														    do_lower_case=__a,    remove_space=__a,    keep_accents=__a,    bos_token=__a,    eos_token=__a,    unk_token=__a,    sep_token=__a,    pad_token=__a,    cls_token=__a,    mask_token=__a,    **__a,    )

														_lowerCAmelCase  :							str				     =					do_lower_case
														_lowerCAmelCase  :							Union[str, Any]				     =					remove_space
														_lowerCAmelCase  :							Any				     =					keep_accents
														_lowerCAmelCase  :							Any				     =					vocab_file

														_lowerCAmelCase  :							List[Any]				     =					spm.SentencePieceProcessor()
														self.sp_model.Load(__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return len(self.sp_model)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					{self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)}
														vocab.update(self.added_tokens_encoder)
														return vocab







							def __getstate__(     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.__dict__.copy()
														_lowerCAmelCase  :							str				     =					None
														return state







							def __setstate__(     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					d
														_lowerCAmelCase  :							str				     =					spm.SentencePieceProcessor()
														self.sp_model.Load(self.vocab_file)







							def      snake_case__   (     self,    __a,    __a=False):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.sp_model.EncodeAsPieces(__a)
														return pieces







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return self.sp_model.PieceToId(__a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return self.sp_model.IdToPiece(__a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					self.sp_model.decode_pieces(__a)
														return out_string







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					[self.sep_token_id]
														_lowerCAmelCase  :							int				     =					[self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep







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

														'''simple docstring'''

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    "You should not supply a second sequence if the provided sequence of "
																												    "ids is already formatted with special tokens for the model.")
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					[self.sep_token_id]
														_lowerCAmelCase  :							Optional[int]				     =					[self.cls_token_id]

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







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														if not os.path.isdir(__a):
																					logger.error("Vocabulary path ({}) should be a directory".format(__a))
																					return
														_lowerCAmelCase  :							Union[str, Any]				     =					os.path.join(
														    __a,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

														if os.path.abspath(self.vocab_file) != os.path.abspath(__a):
																					copyfile(self.vocab_file,    __a)

														return (out_vocab_file,)





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
						)
