


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						:Union[str, Any] 							=       params

       a						:Tuple 							=       np.array(_lowerCAmelCase    )
       a						:Tuple 							=       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        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						:str 							=       self.params.max_model_input_size
       a						:Optional[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    )]

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

       a						:List[Any] 							=       np.array(_lowerCAmelCase    )
       a						:Union[str, Any] 							=       np.array(_lowerCAmelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       len(self    )
       a						:List[Any] 							=       self.lengths > 11
       a						:Dict 							=       self.token_ids[indices]
       a						:Optional[Any] 							=       self.lengths[indices]
       a						:Optional[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						:Union[str, Any] 							=       self.params.special_tok_ids["unk_token"]
       a						:str 							=       len(self    )
       a						:Dict 							=       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						:Dict 							=       self.token_ids[indices]
       a						:int 							=       self.lengths[indices]
       a						:Any 							=       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						:Optional[Any] 							=       [t[0] for t in batch]
       a						:Any 							=       [t[1] for t in batch]
       assert len(_lowerCAmelCase    ) == len(_lowerCAmelCase    )

       # Max for paddings
       a						:Union[str, Any] 							=       max(_lowerCAmelCase    )

       # Pad token ids
       if self.params.mlm:
             a						:int 							=       self.params.special_tok_ids["pad_token"]
       else:
             a						:List[Any] 							=       self.params.special_tok_ids["unk_token"]
       a						:List[Any] 							=       [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_    )

       a						:Union[str, Any] 							=       torch.tensor(tk_    )  # (bs, max_seq_len_)
       a						:List[str] 							=       torch.tensor(_lowerCAmelCase    )  # (bs)
       return tk_t, lg_t











import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast




@dataclass
class       __lowerCAmelCase				(							datasets.BuilderConfig):
       _a           =  None







class       __lowerCAmelCase				(							datasets.ArrowBasedBuilder):
       _a           =  PandasConfig






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return datasets.DatasetInfo(features=self.config.features )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    List[str] ):
         if not self.config.data_files:
           raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}" )
         lowercase			:int							=  dl_manager.download_and_extract(self.config.data_files )
         if isinstance(_lowerCAmelCase				,    (str, list, tuple) ):
           lowercase			:Union[str, Any]							=  data_files
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:str							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[int]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"files": files} )]
         lowercase			:List[Any]							=  []
         for split_name, files in data_files.items():
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:Optional[Any]							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[Any]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           splits.append(datasets.SplitGenerator(name=_lowerCAmelCase				,    gen_kwargs={"files": files} ) )
         return splits






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    pa.Table ):
         if self.config.features is not None:
           # more expensive cast to support nested features with keys in a different order
           # allows str <-> int/float or str to Audio for example
           lowercase			:Any							=  table_cast(_lowerCAmelCase				,    self.config.features.arrow_schema )
         return pa_table






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Union[str, Any] ):
         for i, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase ) ):
           with open(_lowerCAmelCase				,    "rb" ) as f:
             lowercase			:int							=  pa.Table.from_pandas(pd.read_pickle(_lowerCAmelCase ) )
             yield i, self._cast_table(_lowerCAmelCase )



