

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

from utils import logger



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__    ):
						A__							:					int       =	params

						A__							:					Tuple       =	np.array(A__    )
						A__							:					Tuple       =	np.array([len(A__    ) 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__    ):
						return (self.token_ids[index], self.lengths[index])






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






					def 						__A    (				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 						__A    (				self    ):
						A__							:					List[str]       =	self.params.max_model_input_size
						A__							:					Optional[Any]       =	self.lengths > max_len
						logger.info(F"""Splitting {sum(A__    )} too long sequences."""    )

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

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

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

						A__							:					int       =	np.array(A__    )
						A__							:					List[str]       =	np.array(A__    )






					def 						__A    (				self    ):
						A__							:					Any       =	len(self    )
						A__							:					Tuple       =	self.lengths > 11
						A__							:					List[Any]       =	self.token_ids[indices]
						A__							:					str       =	self.lengths[indices]
						A__							:					str       =	len(self    )
						logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""    )






					def 						__A    (				self    ):
						if "unk_token" not in self.params.special_tok_ids:
							return
						else:
							A__							:					List[str]       =	self.params.special_tok_ids["""unk_token"""]
						A__							:					List[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__							:					Union[str, Any]       =	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 						__A    (				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 						__A    (				self					, A__    ):
						A__							:					Tuple       =	[t[0] for t in batch]
						A__							:					Any       =	[t[1] for t in batch]
						assert len(A__    ) == len(A__    )

						# Max for paddings
						A__							:					List[str]       =	max(A__    )

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

						A__							:					Any       =	torch.tensor(tk_    )  # (bs, max_seq_len_)
						A__							:					Any       =	torch.tensor(A__    )  # (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

A_ :				Dict						 =    list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_ :				Optional[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')

A_ :				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')

A_ :				Any						 =    [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')

A_ :				List[str]						 =    [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')

A_ :				Any						 =    len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)



