





'''simple docstring'''







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_camembert import CamembertTokenizer
else:
					lowerCAmelCase_					:    List[str]      	=			None


lowerCAmelCase_					:    str      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Any      	=			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_					:    Optional[Any]      	=			{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_					:    Union[str, Any]      	=			{
    'camembert-base': 5_12,
}

lowerCAmelCase_					:    List[str]      	=			'▁'
class 				__SCREAMING_SNAKE_CASE  (snake_case_      ):






				"""simple docstring"""







				__a            =VOCAB_FILES_NAMES
				__a            =PRETRAINED_VOCAB_FILES_MAP
				__a            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a            =['input_ids', 'attention_mask']
				__a            =CamembertTokenizer




				def __init__(  self     :					List[str]					,				__a     :					Optional[int]=None					,				__a     :					Union[str, Any]=None					,				__a     :					Optional[Any]="<s>"					,				__a     :					Union[str, Any]="</s>"					,				__a     :					Union[str, Any]="</s>"					,				__a     :					Optional[Any]="<s>"					,				__a     :					str="<unk>"					,				__a     :					Tuple="<pad>"					,				__a     :					Tuple="<mask>"					,				__a     :					Dict=["<s>NOTUSED", "</s>NOTUSED"]					,				**__a     :					str					,				):
								# Mask token behave like a normal word, i.e. include the space before it
								_a								= AddedToken(__a					,				lstrip=__a					,				rstrip=__a					) if isinstance(__a					,				__a					) else mask_token

								super().__init__(
								    __a					,				tokenizer_file=__a					,				bos_token=__a					,				eos_token=__a					,				sep_token=__a					,				cls_token=__a					,				unk_token=__a					,				pad_token=__a					,				mask_token=__a					,				additional_special_tokens=__a					,				**__a					,				)

								_a								= vocab_file
								_a								= False if not self.vocab_file else True




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					int					,				__a     :					List[Any] = None					):

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




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Tuple					,				__a     :					Dict = None					):
								_a								= [self.sep_token_id]
								_a								= [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 + sep + token_ids_a + sep					) * [0]




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					str					,				__a     :					List[Any] = None					):
								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
												    "tokenizer."					)

								if not os.path.isdir(__a					):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
												return
								_a								= 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


lowercase  							=					{
    "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			(     a_							,     a_							,     a_							,     a_=None):
							# Initialise PyTorch model
							snake_case_				=							XLNetConfig.from_json_file(a_)

							snake_case_				=							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}''')
														snake_case_				=							finetuning_task
														snake_case_				=							GLUE_TASKS_NUM_LABELS[finetuning_task]
														snake_case_				=							XLNetForSequenceClassification(a_)
							elif "squad" in finetuning_task:
														snake_case_				=							finetuning_task
														snake_case_				=							XLNetForQuestionAnswering(a_)
							else:
														snake_case_				=							XLNetLMHeadModel(a_)

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

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


if __name__ == "__main__":
	lowercase  							=					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",
	)
	lowercase  							=					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
	)





