



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

from utils import logger

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           __a						       =				params

           __a						       =				np.array(__SCREAMING_SNAKE_CASE)
           __a						       =				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							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

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



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


           '''simple docstring'''

           return len(self.lengths)



       def 		_lowerCamelCase			(		self							:     str):


           '''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 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				self.params.max_model_input_size
           __a						       =				self.lengths > max_len
           logger.info(F'Splitting {sum(__SCREAMING_SNAKE_CASE)} too long sequences.')

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

           __a						       =				[]
           __a						       =				[]
           if self.params.mlm:
               __a       ,   __a						       =				self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
           else:
               __a       ,   __a						       =				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						       =				[]
                   for sub_s in divide_chunks(seq_     ,		max_len - 2):
                       if sub_s[0] != cls_id:
                           __a						       =				np.insert(__SCREAMING_SNAKE_CASE     ,		0     ,		__SCREAMING_SNAKE_CASE)
                       if sub_s[-1] != sep_id:
                           __a						       =				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])

           __a						       =				np.array(__SCREAMING_SNAKE_CASE)
           __a						       =				np.array(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				len(self)
           __a						       =				self.lengths > 11
           __a						       =				self.token_ids[indices]
           __a						       =				self.lengths[indices]
           __a						       =				len(self)
           logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.')



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Any):


           '''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 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				[t[0] for t in batch]
           __a						       =				[t[1] for t in batch]
           assert len(__SCREAMING_SNAKE_CASE) == len(__SCREAMING_SNAKE_CASE)

           # Max for paddings
           __a						       =				max(__SCREAMING_SNAKE_CASE)

           # Pad token ids
           if self.params.mlm:
               __a						       =				self.params.special_tok_ids['''pad_token''']
           else:
               __a						       =				self.params.special_tok_ids['''unk_token''']
           __a						       =				[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_)

           __a						       =				torch.tensor(tk_)  # (bs, max_seq_len_)
           __a						       =				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)





