'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  params

         __UpperCamelCase	:	List[Any]		    =  np.array(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  np.array([len(_lowerCamelCase    ) 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     ,			_lowerCamelCase    ):
         return (self.token_ids[index], self.lengths[index])







     def __len__(		self    ):
         return len(self.lengths    )







     def lowerCAmelCase     (		self    ):
         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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.params.max_model_input_size
         __UpperCamelCase	:	List[Any]		    =  self.lengths > max_len
         logger.info(f"""Splitting {sum(_lowerCamelCase    )} too long sequences."""    )

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

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

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

         __UpperCamelCase	:	List[str]		    =  np.array(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  np.array(_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  len(self    )
         __UpperCamelCase	:	Optional[int]		    =  self.lengths > 1_1
         __UpperCamelCase	:	Dict		    =  self.token_ids[indices]
         __UpperCamelCase	:	List[str]		    =  self.lengths[indices]
         __UpperCamelCase	:	Union[str, Any]		    =  len(self    )
         logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""    )







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







     def lowerCAmelCase     (		self    ):
         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 lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  [t[0] for t in batch]
         __UpperCamelCase	:	Optional[Any]		    =  [t[1] for t in batch]
         assert len(_lowerCamelCase    ) == len(_lowerCamelCase    )

         # Max for paddings
         __UpperCamelCase	:	Union[str, Any]		    =  max(_lowerCamelCase    )

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

         __UpperCamelCase	:	int		    =  torch.tensor(tk_    )  # (bs, max_seq_len_)
         __UpperCamelCase	:	Tuple		    =  torch.tensor(_lowerCamelCase    )  # (bs)
         return tk_t, lg_t




'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a=  logging.get_logger(__name__)


a=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a=  {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

a=  {'''facebook/blenderbot-3B''': 1_2_8}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE__		   =						BlenderbotTokenizer







     def __init__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase="replace"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<mask>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			**_lowerCamelCase     ,			):
         super().__init__(
             _lowerCamelCase     ,			_lowerCamelCase     ,			tokenizer_file=_lowerCamelCase     ,			errors=_lowerCamelCase     ,			bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			sep_token=_lowerCamelCase     ,			cls_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			mask_token=_lowerCamelCase     ,			add_prefix_space=_lowerCamelCase     ,			trim_offsets=_lowerCamelCase     ,			**_lowerCamelCase     ,			)

         __UpperCamelCase	:	str		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
             __UpperCamelCase	:	Any		    =  getattr(_lowerCamelCase     ,			pre_tok_state.pop('type'    )    )
             __UpperCamelCase	:	Dict		    =  add_prefix_space
             __UpperCamelCase	:	Optional[Any]		    =  pre_tok_class(**_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  add_prefix_space

         __UpperCamelCase	:	Optional[int]		    =  'post_processor'
         __UpperCamelCase	:	Tuple		    =  getattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         if tokenizer_component_instance:
             __UpperCamelCase	:	str		    =  json.loads(tokenizer_component_instance.__getstate__()    )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __UpperCamelCase	:	List[Any]		    =  tuple(state['sep']    )
             if "cls" in state:
                 __UpperCamelCase	:	str		    =  tuple(state['cls']    )

             __UpperCamelCase	:	Tuple		    =  False

             if state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
                 __UpperCamelCase	:	Dict		    =  add_prefix_space
                 __UpperCamelCase	:	str		    =  True

             if state.get('trim_offsets'     ,			_lowerCamelCase    ) != trim_offsets:
                 __UpperCamelCase	:	int		    =  trim_offsets
                 __UpperCamelCase	:	Any		    =  True

             if changes_to_apply:
                 __UpperCamelCase	:	Dict		    =  getattr(_lowerCamelCase     ,			state.pop('type'    )    )
                 __UpperCamelCase	:	Any		    =  component_class(**_lowerCamelCase    )
                 setattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def lowerCAmelCase     (		self    ):
         if self._mask_token is None:
             if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.'    )
             return None
         return str(self._mask_token    )







     @mask_token.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  AddedToken(_lowerCamelCase     ,			lstrip=_lowerCamelCase     ,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else value
         __UpperCamelCase	:	Optional[Any]		    =  value







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  self._tokenizer.model.save(_lowerCamelCase     ,			name=_lowerCamelCase    )
         return tuple(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Union[str, Any]		    =  [self.sep_token_id]
         __UpperCamelCase	:	List[Any]		    =  [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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         return token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         for is_user, text in conversation.iter_texts():
             if is_user:
                 # We need to space prefix as it's being done within blenderbot
                 inputs.append(' ' + text    )
             else:
                 # Generated responses should contain them already.
                 inputs.append(_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  '  '.join(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.encode(_lowerCamelCase    )
         if len(_lowerCamelCase    ) > self.model_max_length:
             __UpperCamelCase	:	Tuple		    =  input_ids[-self.model_max_length :]
             logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
         return input_ids



