
'''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_barthez import BarthezTokenizer
else:
		lowercase__											=None

lowercase__											=logging.get_logger(__name__)

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

lowercase__											={
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__											={
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

lowercase__											='▁'







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Any       =    VOCAB_FILES_NAMES
			lowerCamelCase__	:			Optional[int]       =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__	:			Tuple       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__	:			int       =    ['input_ids', 'attention_mask']
			lowerCamelCase__	:			int       =    BarthezTokenizer




			def __init__(       self					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase="<s>"					,   UpperCAmelCase="</s>"					,   UpperCAmelCase="</s>"					,   UpperCAmelCase="<s>"					,   UpperCAmelCase="<unk>"					,   UpperCAmelCase="<pad>"					,   UpperCAmelCase="<mask>"					,   **UpperCAmelCase					,   ):
				# Mask token behave like a normal word, i.e. include the space before it
				a_      				=						AddedToken(UpperCAmelCase					,   lstrip=UpperCAmelCase					,   rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase					,   UpperCAmelCase	) else mask_token

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

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = 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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = 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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = 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(UpperCAmelCase	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
				a_      				=						os.path.join(
				    UpperCAmelCase					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

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

				return (out_vocab_file,)


'''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


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
	a_      				=						XLNetConfig.from_json_file(A__						)

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

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

	# Save pytorch-model
	a_      				=						os.path.join(A__  ,      A__						)
	a_      				=						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
		)
