
'''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:
		lowerCAmelCase__          =						None

lowerCAmelCase__          =						logging.get_logger(__name__)

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

lowerCAmelCase__          =						{
    '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'
        ),
    },
}

lowerCAmelCase__          =						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

lowerCAmelCase__          =						'▁'







class       __lowercase					(__UpperCAmelCase    ):
	_lowerCamelCase              =  VOCAB_FILES_NAMES
	_lowerCamelCase              =  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase              =  ["input_ids", "attention_mask"]
	_lowerCamelCase              =  BarthezTokenizer

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

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

			UpperCamelCase__				:					Dict					=					vocab_file
			UpperCamelCase__				:					List[Any]					=					False if not self.vocab_file else True

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			UpperCamelCase__				:					Optional[Any]					=					[self.cls_token_id]
			UpperCamelCase__				:					str					=					[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep

	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			UpperCamelCase__				:					Tuple					=					[self.sep_token_id]
			UpperCamelCase__				:					Union[str, Any]					=					[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   :						Optional[int]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = 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(_lowerCamelCase):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory')
					return
			UpperCamelCase__				:					Union[str, Any]					=					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):
					copyfile(self.vocab_file  ,					_lowerCamelCase)

			return (out_vocab_file,)












'''simple docstring'''




from typing import Union

import fire
import torch
from tqdm import tqdm







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = "cpu"     ,      lowerCamelCase_ = None)							->     None:
		UpperCamelCase__				:					List[Any]					=					torch.load(lowerCamelCase_     ,      map_location=lowerCamelCase_)
		for k, v in tqdm(state_dict.items()):
				if not isinstance(lowerCamelCase_     ,      torch.Tensor):
						raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin')
				UpperCamelCase__				:					int					=					v.half()
		if save_path is None:  # overwrite src_path
				UpperCamelCase__				:					List[Any]					=					src_path
		torch.save(lowerCamelCase_     ,      lowerCamelCase_)


if __name__ == "__main__":
		fire.Fire(convert)




