



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

from utils import logger



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    params

														_lowerCAmelCase     :							List[str]    =    np.array(__a)
														_lowerCAmelCase     :							Optional[Any]    =    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):

														'''simple docstring'''

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







							def __len__(   self):

														'''simple docstring'''

														return len(self.lengths)







							def    snake_case__      (   self):

														'''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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.params.max_model_input_size
														_lowerCAmelCase     :							List[str]    =    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)]

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

														_lowerCAmelCase     :							Any    =    np.array(__a)
														_lowerCAmelCase     :							Optional[Any]    =    np.array(__a)







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''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    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    [t[0] for t in batch]
														_lowerCAmelCase     :							Any    =    [t[1] for t in batch]
														assert len(__a) == len(__a)

														# Max for paddings
														_lowerCAmelCase     :							List[Any]    =    max(__a)

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

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





def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if bit_count < 0:
														raise ValueError("The given input must be positive"  )

							# get the generated string sequence
							_lowerCAmelCase     :							List[str]    =    gray_code_sequence_string(_lowerCamelCase  )
							#
							# convert them to integers
							for i in range(len(_lowerCamelCase  )  ):
														_lowerCAmelCase     :							List[str]    =    int(sequence[i]				, 2  )

							return sequence






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							_lowerCAmelCase     :							List[Any]    =    1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							_lowerCAmelCase     :							Optional[int]    =    gray_code_sequence_string(bit_count - 1  )

							_lowerCAmelCase     :							str    =    []

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2  ):
														_lowerCAmelCase     :							Dict    =    "0" + smaller_sequence[i]
														sequence.append(_lowerCamelCase  )

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2  )  ):
														_lowerCAmelCase     :							Optional[Any]    =    "1" + smaller_sequence[i]
														sequence.append(_lowerCamelCase  )

							return sequence


if __name__ == "__main__":
						import doctest

						doctest.testmod()
