





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


__A							   =							logging.get_logger(__name__)

__A							   =							{"vocab_file": "sentencepiece.bpe.model"}

__A							   =							{
    "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"
        ),
    },
}

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

__A							   =							"▁"



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     VOCAB_FILES_NAMES
 lowercase_						=     PRETRAINED_VOCAB_FILES_MAP
 lowercase_						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_						=     ["input_ids", "attention_mask"]

 def __init__(self							:  Any			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  int="<s>"			,					UpperCAmelCase_							:  List[Any]="</s>"			,					UpperCAmelCase_							:  Optional[int]="</s>"			,					UpperCAmelCase_							:  Any="<s>"			,					UpperCAmelCase_							:  Union[str, Any]="<unk>"			,					UpperCAmelCase_							:  str="<pad>"			,					UpperCAmelCase_							:  Any="<mask>"			,					UpperCAmelCase_							:  Optional[Dict[str, Any]] = None			,					**UpperCAmelCase_							:  List[str]			,					)			->None:



   '''simple docstring'''



   lowerCamelCase__: str         =AddedToken(UpperCAmelCase_			,					lstrip=UpperCAmelCase_			,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_			,					UpperCAmelCase_) else mask_token

   lowerCamelCase__: Optional[Any]         ={} 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_			,					)

   lowerCamelCase__: Optional[int]         =vocab_file
   lowerCamelCase__: List[str]         =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(UpperCAmelCase_))

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

   lowerCamelCase__: int         =len(self.sp_model) - 1
   lowerCamelCase__: Union[str, Any]         ={v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  List[int]			,					UpperCAmelCase_							:  Optional[List[int]] = None)			->List[int]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  List[int]			,					UpperCAmelCase_							:  Optional[List[int]] = None			,					UpperCAmelCase_							:  bool = False)			->List[int]:



   '''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 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  List[int]			,					UpperCAmelCase_							:  Optional[List[int]] = None)			->List[int]:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =[self.sep_token_id]
   lowerCamelCase__: int         =[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 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->int:



   '''simple docstring'''



   return len(self.sp_model)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: List[str]         ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  str)			->List[str]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  Optional[int])			->int:



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__: int         =self.sp_model.PieceToId(UpperCAmelCase_)

   return spm_id if spm_id else self.unk_token_id

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  List[Any])			->Dict:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  str)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: int         =[]
   lowerCamelCase__: Any         =""
   lowerCamelCase__: int         =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
       lowerCamelCase__: int         =True
       lowerCamelCase__: Optional[int]         =[]
     else:
       current_sub_tokens.append(UpperCAmelCase_)
       lowerCamelCase__: Dict         =False
   out_string += self.sp_model.decode(UpperCAmelCase_)
   return out_string.strip()

 def __getstate__(self							:  Optional[int])			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self.__dict__.copy()
   lowerCamelCase__: Union[str, Any]         =None
   return state

 def __setstate__(self							:  Dict			,					UpperCAmelCase_							:  List[str])			->Any:



   '''simple docstring'''



   lowerCamelCase__: Dict         =d

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

   lowerCamelCase__: Any         =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Optional[str] = None)			->Tuple[str]:



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__: List[Any]         =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:
       lowerCamelCase__: str         =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)




import tensorflow as tf

from ...tf_utils import shape_list



class        _a		(tf.keras.layers.Layer							):


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__       ,			A__       ,			A__       ,			A__=1       ,			A__=False       ,			**A__     ):
						super().__init__(**A__     )

						A__				:       Tuple	  =						vocab_size
						A__				:       List[str]	  =						d_embed
						A__				:       int	  =						d_proj

						A__				:       Tuple	  =						cutoffs + [vocab_size]
						A__				:       Any	  =						[0] + self.cutoffs
						A__				:       str	  =						div_val

						A__				:       int	  =						self.cutoffs[0]
						A__				:       List[str]	  =						len(self.cutoffs     ) - 1
						A__				:       Optional[int]	  =						self.shortlist_size + self.n_clusters
						A__				:       List[Any]	  =						keep_order

						A__				:       Optional[int]	  =						[]
						A__				:       Optional[Any]	  =						[]






					def     __A     (					self       ,			A__     ):
						if self.n_clusters > 0:
							A__				:       List[str]	  =						self.add_weight(
							    shape=(self.n_clusters, self.d_embed)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name="""cluster_weight"""     )
							A__				:       Union[str, Any]	  =						self.add_weight(
							    shape=(self.n_clusters,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name="""cluster_bias"""     )

						if self.div_val == 1:
							for i in range(len(self.cutoffs     )     ):
								if self.d_proj != self.d_embed:
									A__				:       List[str]	  =						self.add_weight(
									    shape=(self.d_embed, self.d_proj)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_projs_._{i}"""       ,			)
									self.out_projs.append(A__     )
								else:
									self.out_projs.append(A__     )
								A__				:       int	  =						self.add_weight(
								    shape=(self.vocab_size, self.d_embed)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._weight"""       ,			)
								A__				:       str	  =						self.add_weight(
								    shape=(self.vocab_size,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._bias"""       ,			)
								self.out_layers.append((weight, bias)     )
						else:
							for i in range(len(self.cutoffs     )     ):
								A__  ,  A__				:       Optional[int]	  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
								A__				:       Optional[int]	  =						self.d_embed // (self.div_val**i)

								A__				:       Optional[Any]	  =						self.add_weight(
								    shape=(d_emb_i, self.d_proj)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_projs_._{i}"""     )
								self.out_projs.append(A__     )
								A__				:       List[str]	  =						self.add_weight(
								    shape=(r_idx - l_idx, d_emb_i)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._weight"""       ,			)
								A__				:       Optional[int]	  =						self.add_weight(
								    shape=(r_idx - l_idx,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._bias"""       ,			)
								self.out_layers.append((weight, bias)     )
						super().build(A__     )






					@staticmethod
					def     __A     (					A__       ,			A__       ,			A__       ,			A__=None     ):
						A__				:       Tuple	  =						x
						if proj is not None:
							A__				:       Tuple	  =						tf.einsum("""ibd,ed->ibe"""       ,			A__       ,			A__     )
						return tf.einsum("""ibd,nd->ibn"""       ,			A__       ,			A__     ) + b






					@staticmethod
					def     __A     (					A__       ,			A__     ):
						A__				:       Optional[Any]	  =						shape_list(A__     )
						A__				:       Optional[Any]	  =						tf.range(lp_size[0]       ,			dtype=target.dtype     )
						A__				:       List[str]	  =						tf.stack([r, target]       ,			1     )
						return tf.gather_nd(A__       ,			A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__=True       ,			A__=False     ):
						A__				:       str	  =						0
						if self.n_clusters == 0:
							A__				:       List[str]	  =						self._logit(A__       ,			self.out_layers[0][0]       ,			self.out_layers[0][1]       ,			self.out_projs[0]     )
							if target is not None:
								A__				:       Union[str, Any]	  =						tf.nn.sparse_softmax_cross_entropy_with_logits(labels=A__       ,			logits=A__     )
							A__				:       int	  =						tf.nn.log_softmax(A__       ,			axis=-1     )
						else:
							A__				:       Any	  =						shape_list(A__     )
							A__				:       int	  =						[]
							A__				:       Union[str, Any]	  =						tf.zeros(hidden_sizes[:2]     )
							for i in range(len(self.cutoffs     )     ):
								A__  ,  A__				:       Union[str, Any]	  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
								if target is not None:
									A__				:       Any	  =						(target >= l_idx) & (target < r_idx)
									A__				:       Optional[Any]	  =						tf.where(A__     )
									A__				:       int	  =						tf.boolean_mask(A__       ,			A__     ) - l_idx

								if self.div_val == 1:
									A__				:       Any	  =						self.out_layers[0][0][l_idx:r_idx]
									A__				:       List[Any]	  =						self.out_layers[0][1][l_idx:r_idx]
								else:
									A__				:       Optional[int]	  =						self.out_layers[i][0]
									A__				:       Tuple	  =						self.out_layers[i][1]

								if i == 0:
									A__				:       Union[str, Any]	  =						tf.concat([cur_W, self.cluster_weight]       ,			0     )
									A__				:       Optional[int]	  =						tf.concat([cur_b, self.cluster_bias]       ,			0     )

									A__				:       Any	  =						self._logit(A__       ,			A__       ,			A__       ,			self.out_projs[0]     )
									A__				:       Optional[Any]	  =						tf.nn.log_softmax(A__     )
									out.append(head_logprob[..., : self.cutoffs[0]]     )
									if target is not None:
										A__				:       Optional[Any]	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[str]	  =						self._gather_logprob(A__       ,			A__     )
								else:
									A__				:       str	  =						self._logit(A__       ,			A__       ,			A__       ,			self.out_projs[i]     )
									A__				:       Dict	  =						tf.nn.log_softmax(A__     )
									A__				:       List[str]	  =						self.cutoffs[0] + i - 1  # No probability for the head cluster
									A__				:       Optional[Any]	  =						head_logprob[..., cluster_prob_idx, None] + tail_logprob
									out.append(A__     )
									if target is not None:
										A__				:       Any	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[Any]	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[Any]	  =						self._gather_logprob(A__       ,			A__     )
										cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
								if target is not None:
									loss += tf.scatter_nd(A__       ,			-cur_logprob       ,			shape_list(A__     )     )
							A__				:       List[str]	  =						tf.concat(A__       ,			axis=-1     )

						if target is not None:
							if return_mean:
								A__				:       List[Any]	  =						tf.reduce_mean(A__     )
							# Add the training-time loss value to the layer using `self.add_loss()`.
							self.add_loss(A__     )

							# Log the loss as a metric (we could log arbitrary metrics,
							# including different metrics for training and inference.
							self.add_metric(A__       ,			name=self.name       ,			aggregation="""mean""" if return_mean else """"""     )

						return out



