


"""simple docstring"""




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

from utils import logger







class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:List[str]										=	params

       a						:Optional[int]										=	np.array(lowercase_   )
       a						:Optional[int]										=	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 ,  _lowerCamelCase   ):
       return (self.token_ids[index], self.lengths[index])




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




 def 	SCREAMING_SNAKE_CASE__    (		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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	self.params.max_model_input_size
       a						:Optional[Any]										=	self.lengths > max_len
       logger.info(F'''Splitting {sum(lowercase_   )} too long sequences.'''   )

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

       a						:Tuple										=	[]
       a						:Optional[int]										=	[]
       if self.params.mlm:
             a							,    a						:List[str]										=	self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
       else:
             a							,    a						:Union[str, 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:
                   a						:Tuple										=	[]
                   for sub_s in divide_chunks(seq_ ,  max_len - 2   ):
                         if sub_s[0] != cls_id:
                               a						:Tuple										=	np.insert(lowercase_ ,  0 ,  lowercase_   )
                         if sub_s[-1] != sep_id:
                               a						:Optional[int]										=	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]   )

       a						:Optional[Any]										=	np.array(lowercase_   )
       a						:Any										=	np.array(lowercase_   )




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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):







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




 def 	SCREAMING_SNAKE_CASE__    (		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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Union[str, Any]										=	[t[0] for t in batch]
       a						:List[str]										=	[t[1] for t in batch]
       assert len(lowercase_   ) == len(lowercase_   )

       # Max for paddings
       a						:Any										=	max(lowercase_   )

       # Pad token ids
       if self.params.mlm:
             a						:Optional[Any]										=	self.params.special_tok_ids['''pad_token''']
       else:
             a						:Union[str, Any]										=	self.params.special_tok_ids['''unk_token''']
       a						: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_   )

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









from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





