





'''simple docstring'''

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

from utils import logger


class 					a_     (				__UpperCAmelCase       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			params

        _SCREAMING_SNAKE_CASE									=			np.array(__SCREAMING_SNAKE_CASE       )
        _SCREAMING_SNAKE_CASE									=			np.array([len(__SCREAMING_SNAKE_CASE       ) 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       )		->   List[Any]:
        return (self.token_ids[index], self.lengths[index])


    def __len__(		self       )		->   Optional[Any]:
        return len(self.lengths       )


    def 			snake_case_(		self       )		->   Any:
        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 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.params.max_model_input_size
        _SCREAMING_SNAKE_CASE									=			self.lengths > max_len
        logger.info(f'Splitting {sum(__SCREAMING_SNAKE_CASE       )} too long sequences.'       )

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

        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			[]
        if self.params.mlm:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
        else:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			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:
                _SCREAMING_SNAKE_CASE									=			[]
                for sub_s in divide_chunks(seq_ ,		max_len - 2       ):
                    if sub_s[0] != cls_id:
                        _SCREAMING_SNAKE_CASE									=			np.insert(__SCREAMING_SNAKE_CASE ,		0 ,		__SCREAMING_SNAKE_CASE       )
                    if sub_s[-1] != sep_id:
                        _SCREAMING_SNAKE_CASE									=			np.insert(__SCREAMING_SNAKE_CASE ,		len(__SCREAMING_SNAKE_CASE       ) ,		__SCREAMING_SNAKE_CASE       )
                    assert len(__SCREAMING_SNAKE_CASE       ) <= max_len
                    assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                    sub_seqs.append(__SCREAMING_SNAKE_CASE       )

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

        _SCREAMING_SNAKE_CASE									=			np.array(__SCREAMING_SNAKE_CASE       )
        _SCREAMING_SNAKE_CASE									=			np.array(__SCREAMING_SNAKE_CASE       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			len(self       )
        _SCREAMING_SNAKE_CASE									=			self.lengths > 11
        _SCREAMING_SNAKE_CASE									=			self.token_ids[indices]
        _SCREAMING_SNAKE_CASE									=			self.lengths[indices]
        _SCREAMING_SNAKE_CASE									=			len(self       )
        logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.'       )


    def 			snake_case_(		self       )		->   Tuple:

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


    def 			snake_case_(		self       )		->   List[Any]:

        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 			snake_case_(		self ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			[t[0] for t in batch]
        _SCREAMING_SNAKE_CASE									=			[t[1] for t in batch]
        assert len(__SCREAMING_SNAKE_CASE       ) == len(__SCREAMING_SNAKE_CASE       )

        # Max for paddings
        _SCREAMING_SNAKE_CASE									=			max(__SCREAMING_SNAKE_CASE       )

        # Pad token ids
        if self.params.mlm:
            _SCREAMING_SNAKE_CASE									=			self.params.special_tok_ids["""pad_token"""]
        else:
            _SCREAMING_SNAKE_CASE									=			self.params.special_tok_ids["""unk_token"""]
        _SCREAMING_SNAKE_CASE									=			[list(t.astype(__SCREAMING_SNAKE_CASE       )       ) + [pad_idx] * (max_seq_len_ - len(__SCREAMING_SNAKE_CASE       )) for t in token_ids]
        assert len(tk_       ) == len(__SCREAMING_SNAKE_CASE       )
        assert all(len(__SCREAMING_SNAKE_CASE       ) == max_seq_len_ for t in tk_       )

        _SCREAMING_SNAKE_CASE									=			torch.tensor(tk_       )  # (bs, max_seq_len_)
        _SCREAMING_SNAKE_CASE									=			torch.tensor(__SCREAMING_SNAKE_CASE       )  # (bs)
        return tk_t, lg_t








import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

__snake_case						:Optional[Any]      		=     list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__snake_case						:Any      		=     [file for file in filepaths if file != file.lower()]
if upper_files:
 print(f'{len(upper_files)} files contain uppercase characters:')
 print('''\n'''.join(upper_files) + '''\n''')

__snake_case						:Tuple      		=     [file for file in filepaths if ''' ''' in file]
if space_files:
 print(f'{len(space_files)} files contain space characters:')
 print('''\n'''.join(space_files) + '''\n''')

__snake_case						:Optional[int]      		=     [file for file in filepaths if '''-''' in file]
if hyphen_files:
 print(f'{len(hyphen_files)} files contain hyphen characters:')
 print('''\n'''.join(hyphen_files) + '''\n''')

__snake_case						:Optional[int]      		=     [file for file in filepaths if os.sep not in file]
if nodir_files:
 print(f'{len(nodir_files)} files are not in a directory:')
 print('''\n'''.join(nodir_files) + '''\n''')

__snake_case						:int      		=     len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)





