




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

from utils import logger
class 			a__    (					snake_case__      ):






						def __init__(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       params

											__lowerCAmelCase				   =       np.array(_A			)
											__lowerCAmelCase				   =       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 				__SCREAMING_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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.params.max_model_input_size
											__lowerCAmelCase				   =       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				   =       []
											__lowerCAmelCase				   =       []
											if self.params.mlm:
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
											else:
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       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				   =       []
																					for sub_s in divide_chunks(seq_		,			max_len - 2			):
																										if sub_s[0] != cls_id:
																															__lowerCAmelCase				   =       np.insert(_A		,			0		,			_A			)
																										if sub_s[-1] != sep_id:
																															__lowerCAmelCase				   =       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				   =       np.array(_A			)
											__lowerCAmelCase				   =       np.array(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       len(self			)
											__lowerCAmelCase				   =       self.lengths > 1_1
											__lowerCAmelCase				   =       self.token_ids[indices]
											__lowerCAmelCase				   =       self.lengths[indices]
											__lowerCAmelCase				   =       len(self			)
											logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_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 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [t[0] for t in batch]
											__lowerCAmelCase				   =       [t[1] for t in batch]
											assert len(_A			) == len(_A			)

											# Max for paddings
											__lowerCAmelCase				   =       max(_A			)

											# Pad token ids
											if self.params.mlm:
																__lowerCAmelCase				   =       self.params.special_tok_ids["pad_token"]
											else:
																__lowerCAmelCase				   =       self.params.special_tok_ids["unk_token"]
											__lowerCAmelCase				   =       [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				   =       torch.tensor(tk_			)  # (bs, max_seq_len_)
											__lowerCAmelCase				   =       torch.tensor(_A			)  # (bs)
											return tk_t, lg_t










from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




