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

from utils import logger


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase)       ->	Optional[int]:
        __UpperCamelCase     :Any			  =		params

        __UpperCamelCase     :Optional[int]			  =		np.array(__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		np.array([len(__lowercase) 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 , __lowercase)       ->	Union[str, Any]:
        return (self.token_ids[index], self.lengths[index])







    def __len__(	self)       ->	Optional[Any]:
        return len(self.lengths)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        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 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Optional[Any]			  =		self.params.max_model_input_size
        __UpperCamelCase     :int			  =		self.lengths > max_len
        logger.info(f"""Splitting {sum(__lowercase)} too long sequences.""")

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

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

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

        __UpperCamelCase     :str			  =		np.array(__lowercase)
        __UpperCamelCase     :str			  =		np.array(__lowercase)







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







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







    def 			UpperCamelCase__   (	self)       ->	int:
        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 			UpperCamelCase__   (	self , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		[t[0] for t in batch]
        __UpperCamelCase     :Optional[int]			  =		[t[1] for t in batch]
        assert len(__lowercase) == len(__lowercase)

        # Max for paddings
        __UpperCamelCase     :List[Any]			  =		max(__lowercase)

        # Pad token ids
        if self.params.mlm:
            __UpperCamelCase     :Optional[int]			  =		self.params.special_tok_ids['''pad_token''']
        else:
            __UpperCamelCase     :Any			  =		self.params.special_tok_ids['''unk_token''']
        __UpperCamelCase     :str			  =		[list(t.astype(__lowercase)) + [pad_idx] * (max_seq_len_ - len(__lowercase)) for t in token_ids]
        assert len(tk_) == len(__lowercase)
        assert all(len(__lowercase) == max_seq_len_ for t in tk_)

        __UpperCamelCase     :Optional[int]			  =		torch.tensor(tk_)  # (bs, max_seq_len_)
        __UpperCamelCase     :List[Any]			  =		torch.tensor(__lowercase)  # (bs)
        return tk_t, lg_t






import argparse
import json

from tqdm import tqdm



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser()

    # Required parameters
    parser.add_argument(
        '''--src_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''biencoder-nq-dev.json'''  ,			help='''Path to raw DPR training data'''  ,			)
    parser.add_argument(
        '''--evaluation_set'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''where to store parsed evaluation_set file'''  ,			)
    parser.add_argument(
        '''--gold_data_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''where to store parsed gold_data_path file'''  ,			)
    __UpperCamelCase     :str			  =		parser.parse_args()

    with open(args.src_path  ,			'''r'''	) as src_file, open(args.evaluation_set  ,			'''w'''	) as eval_file, open(
        args.gold_data_path  ,			'''w'''	) as gold_file:
        __UpperCamelCase     :List[str]			  =		json.load(SCREAMING_SNAKE_CASE	)
        for dpr_record in tqdm(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :List[str]			  =		dpr_record['''question''']
            __UpperCamelCase     :Tuple			  =		[context['''title'''] for context in dpr_record['''positive_ctxs''']]
            eval_file.write(question + '''\n'''	)
            gold_file.write('''\t'''.join(SCREAMING_SNAKE_CASE	) + '''\n'''	)


if __name__ == "__main__":
    main()





