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

from utils import logger







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




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

            lowercase					=		np.array(A__)
            lowercase					=		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):
            lowercase					=		self.params.max_model_input_size
            lowercase					=		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__)]

            lowercase					=		[]
            lowercase					=		[]
            if self.params.mlm:
                 lowercase			,		lowercase					=		self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
            else:
                 lowercase			,		lowercase					=		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:
                      lowercase					=		[]
                      for sub_s in divide_chunks(seq_     ,max_len - 2):
                           if sub_s[0] != cls_id:
                                lowercase					=		np.insert(A__     ,0     ,A__)
                           if sub_s[-1] != sep_id:
                                lowercase					=		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])

            lowercase					=		np.array(A__)
            lowercase					=		np.array(A__)




       def 				A__   (       self):
            lowercase					=		len(self)
            lowercase					=		self.lengths > 1_1
            lowercase					=		self.token_ids[indices]
            lowercase					=		self.lengths[indices]
            lowercase					=		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:
                 lowercase					=		self.params.special_tok_ids['''unk_token''']
            lowercase					=		len(self)
            lowercase					=		np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
            lowercase					=		(unk_occs / self.lengths) < 0.5
            lowercase					=		self.token_ids[indices]
            lowercase					=		self.lengths[indices]
            lowercase					=		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__):
            lowercase					=		[t[0] for t in batch]
            lowercase					=		[t[1] for t in batch]
            assert len(A__) == len(A__)

            # Max for paddings
            lowercase					=		max(A__)

            # Pad token ids
            if self.params.mlm:
                 lowercase					=		self.params.special_tok_ids['''pad_token''']
            else:
                 lowercase					=		self.params.special_tok_ids['''unk_token''']
            lowercase					=		[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_)

            lowercase					=		torch.tensor(tk_)  # (bs, max_seq_len_)
            lowercase					=		torch.tensor(A__)  # (bs)
            return tk_t, lg_t






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




