






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:
							UpperCamelCase        =				None

UpperCamelCase        =				logging.get_logger(__name__)

UpperCamelCase        =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase        =				{
    "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"
        ),
    },
}

UpperCamelCase        =				{
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

UpperCamelCase        =				"▁"

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			VOCAB_FILES_NAMES
				_snake_case				:   Any        =			PRETRAINED_VOCAB_FILES_MAP
				_snake_case				:   Any        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case				:   Any        =			["""input_ids""", """attention_mask"""]
				_snake_case				:   Tuple        =			BarthezTokenizer







				def __init__( self     :Union[str, Any]  ,     lowerCamelCase__     :str=None  ,     lowerCamelCase__     :Union[str, Any]=None  ,     lowerCamelCase__     :List[Any]="<s>"  ,     lowerCamelCase__     :Optional[int]="</s>"  ,     lowerCamelCase__     :Dict="</s>"  ,     lowerCamelCase__     :Optional[int]="<s>"  ,     lowerCamelCase__     :List[Any]="<unk>"  ,     lowerCamelCase__     :Union[str, Any]="<pad>"  ,     lowerCamelCase__     :int="<mask>"  ,     **lowerCamelCase__     :Any  ,     ):
					# Mask token behave like a normal word, i.e. include the space before it
					UpperCamelCase__ :Any						=      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__ :int						=      vocab_file
					UpperCamelCase__ :Optional[int]						=      False if not self.vocab_file else True







				def 				__a   ( self     :Optional[Any]  ,     lowerCamelCase__     :List[int]  ,     lowerCamelCase__     :Optional[List[int]] = None     ):

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







				def 				__a   ( self     :List[Any]  ,     lowerCamelCase__     :List[int]  ,     lowerCamelCase__     :Optional[List[int]] = None     ):
					UpperCamelCase__ :Union[str, Any]						=      [self.sep_token_id]
					UpperCamelCase__ :List[str]						=      [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 				__a   ( self     :Any  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :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__ :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"""




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_			,					A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      List[str]   			=  params

         _lowercase:      str   			=  np.array(A_							)
         _lowercase:      Optional[int]   			=  np.array([len(A_							) for t in data]							)

         self.check()
         self.remove_long_sequences()
         self.remove_empty_sequences()
         self.remove_unknown_sequences()
         self.check()
         self.print_statistics()
       def __getitem__(  self			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         return (self.token_ids[index], self.lengths[index])
       def __len__(  self							)  ->						List[str]:





         """simple docstring"""




         return len(self.lengths							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         assert len(self.token_ids							) == len(self.lengths							)
         assert all(self.lengths[i] == len(self.token_ids[i]							) for i in range(len(self.lengths							)							)							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.params.max_model_input_size
         _lowercase:      Tuple   			=  self.lengths > max_len
         logger.info(f'''Splitting {sum(A_							)} too long sequences.'''							)

         def divide_chunks(A_			,					A_							):
           return [l[i : i + n] for i in range(0			,					len(A_							)			,					A_							)]

         _lowercase:      Dict   			=  []
         _lowercase:      Union[str, Any]   			=  []
         if self.params.mlm:
           _lowercase			,					_lowercase:      int   			=  self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
         else:
           _lowercase			,					_lowercase:      Dict   			=  self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

         for seq_, len_ in zip(self.token_ids			,					self.lengths							):
           assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
           if len_ <= max_len:
             new_tok_ids.append(seq_							)
             new_lengths.append(len_							)
           else:
             _lowercase:      Optional[Any]   			=  []
             for sub_s in divide_chunks(seq_			,					max_len - 2							):
               if sub_s[0] != cls_id:
                 _lowercase:      Any   			=  np.insert(A_			,					0			,					A_							)
               if sub_s[-1] != sep_id:
                 _lowercase:      Optional[int]   			=  np.insert(A_			,					len(A_							)			,					A_							)
               assert len(A_							) <= max_len
               assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
               sub_seqs.append(A_							)

             new_tok_ids.extend(A_							)
             new_lengths.extend([len(A_							) for l in sub_seqs]							)

         _lowercase:      Optional[Any]   			=  np.array(A_							)
         _lowercase:      List[str]   			=  np.array(A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  len(self							)
         _lowercase:      Optional[Any]   			=  self.lengths > 11
         _lowercase:      int   			=  self.token_ids[indices]
         _lowercase:      List[str]   			=  self.lengths[indices]
         _lowercase:      Optional[int]   			=  len(self							)
         logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         if "unk_token" not in self.params.special_tok_ids:
           return
         else:
           _lowercase:      Dict   			=  self.params.special_tok_ids['''unk_token''']
         _lowercase:      int   			=  len(self							)
         _lowercase:      Tuple   			=  np.array([np.count_nonzero(a == unk_token_id							) for a in self.token_ids]							)
         _lowercase:      Dict   			=  (unk_occs / self.lengths) < 0.5
         _lowercase:      str   			=  self.token_ids[indices]
         _lowercase:      Dict   			=  self.lengths[indices]
         _lowercase:      int   			=  len(self							)
         logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         if not self.params.is_master:
           return
         logger.info(f'''{len(self							)} sequences'''							)
         # data_len = sum(self.lengths)
         # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
         # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

         # unk_idx = self.params.special_tok_ids['unk_token']
         # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
         # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')



       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[str]   			=  [t[0] for t in batch]
         _lowercase:      Dict   			=  [t[1] for t in batch]
         assert len(A_							) == len(A_							)

         # Max for paddings
         _lowercase:      Tuple   			=  max(A_							)

         # Pad token ids
         if self.params.mlm:
           _lowercase:      str   			=  self.params.special_tok_ids['''pad_token''']
         else:
           _lowercase:      Union[str, Any]   			=  self.params.special_tok_ids['''unk_token''']
         _lowercase:      int   			=  [list(t.astype(A_							)							) + [pad_idx] * (max_seq_len_ - len(A_							)) for t in token_ids]
         assert len(tk_							) == len(A_							)
         assert all(len(A_							) == max_seq_len_ for t in tk_							)

         _lowercase:      str   			=  torch.tensor(tk_							)  # (bs, max_seq_len_)
         _lowercase:      int   			=  torch.tensor(A_							)  # (bs)
         return tk_t, lg_t
