






"""simple docstring"""






import os
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 logging


SCREAMING_SNAKE_CASE						:						List[str]					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						Any					   =       {'''vocab_file''': '''sentencepiece.bpe.model'''}

SCREAMING_SNAKE_CASE						:						Dict					   =       {
    '''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'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:						Optional[int]					   =       {
    '''moussaKam/mbarthez''': 1_024,
    '''moussaKam/barthez''': 1_024,
    '''moussaKam/barthez-orangesum-title''': 1_024,
}

SCREAMING_SNAKE_CASE						:						Optional[int]					   =       '''▁'''





class       snake_case_       (    __snake_case       ):
	SCREAMING_SNAKE_CASE_:							Dict   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Tuple   			=					["""input_ids""", """attention_mask"""]


	def __init__(      self  ,					__a  ,					__a="<s>"  ,					__a="</s>"  ,					__a="</s>"  ,					__a="<s>"  ,					__a="<unk>"  ,					__a="<pad>"  ,					__a="<mask>"  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       AddedToken(__UpperCamelCase  ,					lstrip=__UpperCamelCase  ,					rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase  ,					__UpperCamelCase						) else mask_token

					A__							 =       {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=__UpperCamelCase  ,					eos_token=__UpperCamelCase  ,					unk_token=__UpperCamelCase  ,					sep_token=__UpperCamelCase  ,					cls_token=__UpperCamelCase  ,					pad_token=__UpperCamelCase  ,					mask_token=__UpperCamelCase  ,					sp_model_kwargs=self.sp_model_kwargs  ,					**__UpperCamelCase  ,					)

					A__							 =       vocab_file
					A__							 =       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.Load(str(__UpperCamelCase						)						)

					A__							 =       {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

					A__							 =       len(self.sp_model						) - 1
					A__							 =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}


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


					"""simple docstring"""

					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  ,					__a  ,					__a = None  ,					__a = False						):


					"""simple docstring"""

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

					if token_ids_a is None:
									return [1] + ([0] * len(__UpperCamelCase						)) + [1]
					return [1] + ([0] * len(__UpperCamelCase						)) + [1, 1] + ([0] * len(__UpperCamelCase						)) + [1]


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


					"""simple docstring"""

					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]


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.sp_model						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       {self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
					vocab.update(self.added_tokens_encoder						)
					return vocab


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.sp_model.encode(__UpperCamelCase  ,					out_type=__UpperCamelCase						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					A__							 =       self.sp_model.PieceToId(__UpperCamelCase						)

					return spm_id if spm_id else self.unk_token_id


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(__UpperCamelCase						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					A__							 =       ''
					A__							 =       False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(__UpperCamelCase						) + token
													A__							 =       True
													A__							 =       []
									else:
													current_sub_tokens.append(__UpperCamelCase						)
													A__							 =       False
					out_string += self.sp_model.decode(__UpperCamelCase						)
					return out_string.strip()


	def __getstate__(      self						):


					"""simple docstring"""

					A__							 =       self.__dict__.copy()
					A__							 =       None
					return state


	def __setstate__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       d

					# for backward compatibility
					if not hasattr(self  ,					'sp_model_kwargs'						):
									A__							 =       {}

					A__							 =       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.Load(self.vocab_file						)






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


					"""simple docstring"""

					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						) and os.path.isfile(self.vocab_file						):
									copyfile(self.vocab_file  ,					__UpperCamelCase						)
					elif not os.path.isfile(self.vocab_file						):
									with open(__UpperCamelCase  ,					'wb'						) as fi:
													A__							 =       self.sp_model.serialized_model_proto()
													fi.write(__UpperCamelCase						)

					return (out_vocab_file,)










"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE						:						Any					   =       logging.get_logger(__name__)





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""timm_backbone"""


	def __init__(      self  ,					__a=None  ,					__a=3  ,					__a=True  ,					__a=True  ,					__a=None  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(**__a						)
					A__							 =       backbone
					A__							 =       num_channels
					A__							 =       features_only
					A__							 =       use_pretrained_backbone
					A__							 =       True
					A__							 =       out_indices if out_indices is not None else (-1,)


