





'''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


__lowerCamelCase : str											=			logging.get_logger(__name__)

__lowerCamelCase : Any											=			{"vocab_file": "sentencepiece.bpe.model"}

__lowerCamelCase : 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"
        ),
    },
}

__lowerCamelCase : List[str]											=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__lowerCamelCase : int											=			"▁"

class       UpperCAmelCase (						_UpperCAmelCase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		VOCAB_FILES_NAMES
		UpperCAmelCase					:	List[Any] 			=		PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase					:	Union[str, Any] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase					:	Any 			=		['''input_ids''', '''attention_mask''']




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

						lowercase         = {} 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_							,			)

						lowercase         = vocab_file
						lowercase         = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
						self.sp_model.Load(str(A_				)				)

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

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




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None				)					-> List[int]:

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




		def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None							,			A__      :	bool = False				)					-> List[int]:
						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 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	List[int]							,			A__      :	Optional[List[int]] = None				)					-> List[int]:
						lowercase         = [self.sep_token_id]
						lowercase         = [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      :	int				)					-> Union[str, Any]:
						return len(self.sp_model				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> List[str]:
						lowercase         = {self.convert_ids_to_tokens(A_				): i for i in range(self.vocab_size				)}
						vocab.update(self.added_tokens_encoder				)
						return vocab




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	str				)					-> List[str]:
						return self.sp_model.encode(A_							,			out_type=A_				)




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	Union[str, Any]				)					-> List[Any]:
						if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
						lowercase         = self.sp_model.PieceToId(A_				)

						return spm_id if spm_id else self.unk_token_id




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	str				)					-> Dict:
						if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(A_				)




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	int				)					-> Union[str, Any]:
						lowercase         = []
						lowercase         = ""
						lowercase         = 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
														lowercase         = True
														lowercase         = []
										else:
														current_sub_tokens.append(A_				)
														lowercase         = False
						out_string += self.sp_model.decode(A_				)
						return out_string.strip()




		def __getstate__(self      :	Dict				)					-> int:
						lowercase         = self.__dict__.copy()
						lowercase         = None
						return state




		def __setstate__(self      :	Union[str, Any]							,			A__      :	List[str]				)					-> List[Any]:
						lowercase         = d

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

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





		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	str							,			A__      :	Optional[str] = None				)					-> Tuple[str]:
						if not os.path.isdir(A_				):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
										return
						lowercase         = 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:
														lowercase         = self.sp_model.serialized_model_proto()
														fi.write(A_				)

						return (out_vocab_file,)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




