


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

from utils import logger

class       __magic_name__	(     __lowerCAmelCase):


					def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     Union[str, Any]			)  ->      List[str]:





						'''simple docstring'''






						UpperCamelCase__					:    int          =   params

						UpperCamelCase__					:    str          =   np.array(lowercase_			)
						UpperCamelCase__					:    Dict          =   np.array([len(lowercase_			) 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							:     List[str]							,	lowerCamelCase__							:     str			)  ->      List[Any]:





						'''simple docstring'''






						return (self.token_ids[index], self.lengths[index])


					def __len__(      self							:     Union[str, Any]			)  ->      str:





						'''simple docstring'''






						return len(self.lengths			)


					def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Union[str, Any]:





						'''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      UpperCAmelCase__	(      self							:     List[Any]			)  ->      List[str]:





						'''simple docstring'''






						UpperCamelCase__					:    Any          =   self.params.max_model_input_size
						UpperCamelCase__					:    Any          =   self.lengths > max_len
						logger.info(F"Splitting {sum(lowercase_			)} too long sequences."			)

						def divide_chunks(lowerCamelCase__							:     Any							,	lowerCamelCase__							:     Union[str, Any]			):
							return [l[i : i + n] for i in range(0							,	len(lowercase_			)							,	lowercase_			)]

						UpperCamelCase__					:    List[Any]          =   []
						UpperCamelCase__					:    Optional[Any]          =   []
						if self.params.mlm:
							UpperCamelCase__       ,				UpperCamelCase__					:    str          =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
						else:
							UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   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:
								UpperCamelCase__					:    Tuple          =   []
								for sub_s in divide_chunks(seq_							,	max_len - 2			):
									if sub_s[0] != cls_id:
										UpperCamelCase__					:    Tuple          =   np.insert(lowercase_							,	0							,	lowercase_			)
									if sub_s[-1] != sep_id:
										UpperCamelCase__					:    Dict          =   np.insert(lowercase_							,	len(lowercase_			)							,	lowercase_			)
									assert len(lowercase_			) <= max_len
									assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
									sub_seqs.append(lowercase_			)

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

						UpperCamelCase__					:    Dict          =   np.array(lowercase_			)
						UpperCamelCase__					:    int          =   np.array(lowercase_			)


					def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





						'''simple docstring'''






						UpperCamelCase__					:    Optional[Any]          =   len(self			)
						UpperCamelCase__					:    str          =   self.lengths > 11
						UpperCamelCase__					:    Union[str, Any]          =   self.token_ids[indices]
						UpperCamelCase__					:    Optional[int]          =   self.lengths[indices]
						UpperCamelCase__					:    List[Any]          =   len(self			)
						logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences."			)


					def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





						'''simple docstring'''






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


					def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Tuple:





						'''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      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     Union[str, Any]			)  ->      Union[str, Any]:





						'''simple docstring'''






						UpperCamelCase__					:    Any          =   [t[0] for t in batch]
						UpperCamelCase__					:    List[str]          =   [t[1] for t in batch]
						assert len(lowercase_			) == len(lowercase_			)

						# Max for paddings
						UpperCamelCase__					:    Union[str, Any]          =   max(lowercase_			)

						# Pad token ids
						if self.params.mlm:
							UpperCamelCase__					:    Dict          =   self.params.special_tok_ids['''pad_token''']
						else:
							UpperCamelCase__					:    List[str]          =   self.params.special_tok_ids['''unk_token''']
						UpperCamelCase__					:    List[str]          =   [list(t.astype(lowercase_			)			) + [pad_idx] * (max_seq_len_ - len(lowercase_			)) for t in token_ids]
						assert len(tk_			) == len(lowercase_			)
						assert all(len(lowercase_			) == max_seq_len_ for t in tk_			)

						UpperCamelCase__					:    Any          =   torch.tensor(tk_			)  # (bs, max_seq_len_)
						UpperCamelCase__					:    Optional[Any]          =   torch.tensor(lowercase_			)  # (bs)
						return tk_t, lg_t






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_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

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

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   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__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     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]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [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      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      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__					:    List[str]          =   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,)


