

'''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


_UpperCAmelCase	:     str		       = logging.get_logger(__name__)

_UpperCAmelCase	:     List[str]		       = {"""vocab_file""": """sentencepiece.bpe.model"""}

_UpperCAmelCase	:     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"""
        ),
    },
}

_UpperCAmelCase	:     Dict		       = {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

_UpperCAmelCase	:     Tuple		       = """▁"""







class 				lowercase       ( UpperCamelCase__    ):
				__SCREAMING_SNAKE_CASE      :				Union[str, Any]     =    VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE      :				List[str]     =    PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE      :				int     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE      :				Any     =    ['''input_ids''', '''attention_mask''']




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

											snake_case_           =					{} if sp_model_kwargs is None else sp_model_kwargs

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

											snake_case_           =					vocab_file
											snake_case_           =					spm.SentencePieceProcessor(**self.sp_model_kwargs )
											self.sp_model.Load(str(_a ) )

											snake_case_           =					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

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




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):

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




				def 					a							(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = False ):
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=_a   ,     token_ids_a=_a   ,     already_has_special_tokens=_a )

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




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					[self.sep_token_id]
											snake_case_           =					[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 					a							(      self ):
											return len(self.sp_model )




				def 					a							(      self ):
											snake_case_           =					{self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
											vocab.update(self.added_tokens_encoder )
											return vocab




				def 					a							(      self   ,     snake_case ):
											return self.sp_model.encode(_a   ,     out_type=_a )




				def 					a							(      self   ,     snake_case ):
											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											snake_case_           =					self.sp_model.PieceToId(_a )

											return spm_id if spm_id else self.unk_token_id




				def 					a							(      self   ,     snake_case ):
											if index in self.fairseq_ids_to_tokens:
																		return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(_a )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					[]
											snake_case_           =					""""""
											snake_case_           =					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(_a ) + token
																									snake_case_           =					True
																									snake_case_           =					[]
																		else:
																									current_sub_tokens.append(_a )
																									snake_case_           =					False
											out_string += self.sp_model.decode(_a )
											return out_string.strip()




				def __getstate__(      self ):
											snake_case_           =					self.__dict__.copy()
											snake_case_           =					None
											return state




				def __setstate__(      self   ,     snake_case ):
											snake_case_           =					d

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

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




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											if not os.path.isdir(_a ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																		return
											snake_case_           =					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 ) and os.path.isfile(self.vocab_file ):
																		copyfile(self.vocab_file   ,     _a )
											elif not os.path.isfile(self.vocab_file ):
																		with open(_a   ,     'wb' ) as fi:
																									snake_case_           =					self.sp_model.serialized_model_proto()
																									fi.write(_a )

											return (out_vocab_file,)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCAmelCase	:     Any		       = {
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Any		       = [
					    """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LongT5EncoderModel""",
					    """LongT5ForConditionalGeneration""",
					    """LongT5Model""",
					    """LongT5PreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Optional[int]		       = [
					    """FlaxLongT5ForConditionalGeneration""",
					    """FlaxLongT5Model""",
					    """FlaxLongT5PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longta import (
										    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongTaEncoderModel,
										    LongTaForConditionalGeneration,
										    LongTaModel,
										    LongTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_longta import (
										    FlaxLongTaForConditionalGeneration,
										    FlaxLongTaModel,
										    FlaxLongTaPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase	:     Optional[Any]		       = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




