


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

from utils import logger

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  int    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       params

														__snake_case		:      int										=       np.array(lowerCamelCase    )
														__snake_case		:      Any										=       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	:  List[Any]   ,     lowerCamelCase	:  Dict    )     ->   str:
														return (self.token_ids[index], self.lengths[index])

							def __len__(							self	:  Dict    )     ->   Union[str, Any]:
														return len(self.lengths    )

							def    __snake_case (							self	:  Optional[int]    )     ->   str:
														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	:  Union[str, Any]    )     ->   List[str]:
														__snake_case		:      Optional[int]										=       self.params.max_model_input_size
														__snake_case		:      Union[str, Any]										=       self.lengths > max_len
														logger.info(F'Splitting {sum(lowerCamelCase    )} too long sequences.'    )

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

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

														__snake_case		:      str										=       np.array(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       np.array(lowerCamelCase    )

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

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

							def    __snake_case (							self	:  str    )     ->   List[str]:
														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	:  Any   ,     lowerCamelCase	:  Any    )     ->   Union[str, Any]:
														__snake_case		:      str										=       [t[0] for t in batch]
														__snake_case		:      Union[str, Any]										=       [t[1] for t in batch]
														assert len(lowerCamelCase    ) == len(lowerCamelCase    )

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

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

														__snake_case		:      Tuple										=       torch.tensor(tk_    )  # (bs, max_seq_len_)
														__snake_case		:      str										=       torch.tensor(lowerCamelCase    )  # (bs)
														return tk_t, lg_t







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							if index == r:
														for j in range(__lowerCamelCase     ):
																					print(data[j]							,     end=" "     )
														print(" "     )
														return
							#  When no more elements are there to put in data[]
							if i >= n:
														return
							# current is included, put next at next location
							__snake_case		:      Union[str, Any]										=       arr[i]
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     index + 1							,     __lowerCamelCase							,     i + 1     )
							# current is excluded, replace it with
							# next (Note that i+1 is passed, but
							# index is not changed)
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     i + 1     )
							# The main function that prints all combinations
							# of size r in arr[] of size n. This function
							# mainly uses combinationUtil()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# A temporary array to store all combination one by one
							__snake_case		:      Union[str, Any]										=       [0] * r
							# Print all combination using temporary array 'data[]'
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     0							,     __lowerCamelCase							,     0     )


if __name__ == "__main__":
			# Driver code to check the function above
			_snake_case					:		List[str]      =    [10, 20, 30, 40, 50]
			print_combination(arr, len(arr), 3)
			# This code is contributed by Ambuj sahu

