from typing import Dict, Iterable, Union

import torch
import transformers
from torch.utils.data import Dataset
from tqdm.auto import tqdm
from transformers import AutoTokenizer


class NamedEntityRecognitionDataset(Dataset):
    """
    Dataset for NER task.
    """

    def __init__(
        self,
        texts: Iterable[Iterable[str]],
        tags: Iterable[Iterable[str]] = None,
        tokenizer: Union[
            str, transformers.tokenization_utils.PreTrainedTokenizer
        ] = 'distilbert-base-uncased',
        max_seq_len: int = None,
        lazy_mode: bool = True,
    ):
        self.tags = tags
        self.texts = texts

        if isinstance(tokenizer, str):
            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer)
        elif isinstance(tokenizer, transformers.tokenization_utils.PreTrainedTokenizer):
            self.tokenizer = tokenizer
        else:
            raise TypeError(
                "You pass wrong type of tokenizer. It should be a model name or PreTrainedTokenizer."
            )

        self.max_seq_len = max_seq_len
        self.length = len(texts)

        if self.max_seq_len < 3:
            raise ValueError("Max sequence length should be greather than 2")

        if not lazy_mode:
            self.encoded = [
                self._getitem_lazy(idx)
                for idx in tqdm(range(self.length), desc='tokenizing texts')
            ]
            del self.texts
            del self.tags

        self._getitem_fn = self._getitem_lazy if lazy_mode else self._getitem_encoded

    def __len__(self) -> int:
        return self.length

    def _getitem_encoded(self, index: int) -> Dict[str, torch.Tensor]:
        return self.encoded[index]

    def _getitem_lazy(self, index: int) -> Dict[str, torch.Tensor]:
        sentence = self.texts[index]
        tag = self.tags[index]

        input_ids = []
        target_tag = []

        for i, word in enumerate(sentence):
            words_piece_ids = self.tokenizer.encode(
                word,
                max_length=self.max_seq_len,
                truncation=True,
                add_special_tokens=False,
            )
            input_ids.extend(words_piece_ids)
            target_tag.extend([tag[i]] * len(words_piece_ids))

        input_ids = (
            [self.tokenizer.cls_token_id]
            + input_ids[: self.max_seq_len - 2]
            + [self.tokenizer.sep_token_id]
        )

        attention_mask = [1] * len(input_ids)

        padding_len = self.max_seq_len - len(input_ids)
        input_ids = input_ids + ([self.tokenizer.pad_token_id] * padding_len)
        attention_mask = attention_mask + ([0] * padding_len)

        target_tag = [0] + target_tag[: self.max_seq_len - 2] + [0]
        target_tag = target_tag + ([0] * padding_len)

        return {
            'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long),
            'target_tag': torch.tensor(target_tag, dtype=torch.long),
        }

    def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:
        return self._getitem_fn(index)

a3b57aa2a05057e5bdbc741d8604971e1cbd0606
<mask token>


class NamedEntityRecognitionDataset(Dataset):
    <mask token>
    <mask token>

    def __len__(self) ->int:
        return self.length

    def _getitem_encoded(self, index: int) ->Dict[str, torch.Tensor]:
        return self.encoded[index]
    <mask token>

    def __getitem__(self, index: int) ->Dict[str, torch.Tensor]:
        return self._getitem_fn(index)

<mask token>


class NamedEntityRecognitionDataset(Dataset):
    <mask token>
    <mask token>

    def __len__(self) ->int:
        return self.length

    def _getitem_encoded(self, index: int) ->Dict[str, torch.Tensor]:
        return self.encoded[index]

    def _getitem_lazy(self, index: int) ->Dict[str, torch.Tensor]:
        sentence = self.texts[index]
        tag = self.tags[index]
        input_ids = []
        target_tag = []
        for i, word in enumerate(sentence):
            words_piece_ids = self.tokenizer.encode(word, max_length=self.
                max_seq_len, truncation=True, add_special_tokens=False)
            input_ids.extend(words_piece_ids)
            target_tag.extend([tag[i]] * len(words_piece_ids))
        input_ids = [self.tokenizer.cls_token_id] + input_ids[:self.
            max_seq_len - 2] + [self.tokenizer.sep_token_id]
        attention_mask = [1] * len(input_ids)
        padding_len = self.max_seq_len - len(input_ids)
        input_ids = input_ids + [self.tokenizer.pad_token_id] * padding_len
        attention_mask = attention_mask + [0] * padding_len
        target_tag = [0] + target_tag[:self.max_seq_len - 2] + [0]
        target_tag = target_tag + [0] * padding_len
        return {'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long
            ), 'target_tag': torch.tensor(target_tag, dtype=torch.long)}

    def __getitem__(self, index: int) ->Dict[str, torch.Tensor]:
        return self._getitem_fn(index)

<mask token>


class NamedEntityRecognitionDataset(Dataset):
    <mask token>

    def __init__(self, texts: Iterable[Iterable[str]], tags: Iterable[
        Iterable[str]]=None, tokenizer: Union[str, transformers.
        tokenization_utils.PreTrainedTokenizer]='distilbert-base-uncased',
        max_seq_len: int=None, lazy_mode: bool=True):
        self.tags = tags
        self.texts = texts
        if isinstance(tokenizer, str):
            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer)
        elif isinstance(tokenizer, transformers.tokenization_utils.
            PreTrainedTokenizer):
            self.tokenizer = tokenizer
        else:
            raise TypeError(
                'You pass wrong type of tokenizer. It should be a model name or PreTrainedTokenizer.'
                )
        self.max_seq_len = max_seq_len
        self.length = len(texts)
        if self.max_seq_len < 3:
            raise ValueError('Max sequence length should be greather than 2')
        if not lazy_mode:
            self.encoded = [self._getitem_lazy(idx) for idx in tqdm(range(
                self.length), desc='tokenizing texts')]
            del self.texts
            del self.tags
        self._getitem_fn = (self._getitem_lazy if lazy_mode else self.
            _getitem_encoded)

    def __len__(self) ->int:
        return self.length

    def _getitem_encoded(self, index: int) ->Dict[str, torch.Tensor]:
        return self.encoded[index]

    def _getitem_lazy(self, index: int) ->Dict[str, torch.Tensor]:
        sentence = self.texts[index]
        tag = self.tags[index]
        input_ids = []
        target_tag = []
        for i, word in enumerate(sentence):
            words_piece_ids = self.tokenizer.encode(word, max_length=self.
                max_seq_len, truncation=True, add_special_tokens=False)
            input_ids.extend(words_piece_ids)
            target_tag.extend([tag[i]] * len(words_piece_ids))
        input_ids = [self.tokenizer.cls_token_id] + input_ids[:self.
            max_seq_len - 2] + [self.tokenizer.sep_token_id]
        attention_mask = [1] * len(input_ids)
        padding_len = self.max_seq_len - len(input_ids)
        input_ids = input_ids + [self.tokenizer.pad_token_id] * padding_len
        attention_mask = attention_mask + [0] * padding_len
        target_tag = [0] + target_tag[:self.max_seq_len - 2] + [0]
        target_tag = target_tag + [0] * padding_len
        return {'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long
            ), 'target_tag': torch.tensor(target_tag, dtype=torch.long)}

    def __getitem__(self, index: int) ->Dict[str, torch.Tensor]:
        return self._getitem_fn(index)

from typing import Dict, Iterable, Union
import torch
import transformers
from torch.utils.data import Dataset
from tqdm.auto import tqdm
from transformers import AutoTokenizer


class NamedEntityRecognitionDataset(Dataset):
    """
    Dataset for NER task.
    """

    def __init__(self, texts: Iterable[Iterable[str]], tags: Iterable[
        Iterable[str]]=None, tokenizer: Union[str, transformers.
        tokenization_utils.PreTrainedTokenizer]='distilbert-base-uncased',
        max_seq_len: int=None, lazy_mode: bool=True):
        self.tags = tags
        self.texts = texts
        if isinstance(tokenizer, str):
            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer)
        elif isinstance(tokenizer, transformers.tokenization_utils.
            PreTrainedTokenizer):
            self.tokenizer = tokenizer
        else:
            raise TypeError(
                'You pass wrong type of tokenizer. It should be a model name or PreTrainedTokenizer.'
                )
        self.max_seq_len = max_seq_len
        self.length = len(texts)
        if self.max_seq_len < 3:
            raise ValueError('Max sequence length should be greather than 2')
        if not lazy_mode:
            self.encoded = [self._getitem_lazy(idx) for idx in tqdm(range(
                self.length), desc='tokenizing texts')]
            del self.texts
            del self.tags
        self._getitem_fn = (self._getitem_lazy if lazy_mode else self.
            _getitem_encoded)

    def __len__(self) ->int:
        return self.length

    def _getitem_encoded(self, index: int) ->Dict[str, torch.Tensor]:
        return self.encoded[index]

    def _getitem_lazy(self, index: int) ->Dict[str, torch.Tensor]:
        sentence = self.texts[index]
        tag = self.tags[index]
        input_ids = []
        target_tag = []
        for i, word in enumerate(sentence):
            words_piece_ids = self.tokenizer.encode(word, max_length=self.
                max_seq_len, truncation=True, add_special_tokens=False)
            input_ids.extend(words_piece_ids)
            target_tag.extend([tag[i]] * len(words_piece_ids))
        input_ids = [self.tokenizer.cls_token_id] + input_ids[:self.
            max_seq_len - 2] + [self.tokenizer.sep_token_id]
        attention_mask = [1] * len(input_ids)
        padding_len = self.max_seq_len - len(input_ids)
        input_ids = input_ids + [self.tokenizer.pad_token_id] * padding_len
        attention_mask = attention_mask + [0] * padding_len
        target_tag = [0] + target_tag[:self.max_seq_len - 2] + [0]
        target_tag = target_tag + [0] * padding_len
        return {'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long
            ), 'target_tag': torch.tensor(target_tag, dtype=torch.long)}

    def __getitem__(self, index: int) ->Dict[str, torch.Tensor]:
        return self._getitem_fn(index)

from typing import Dict, Iterable, Union

import torch
import transformers
from torch.utils.data import Dataset
from tqdm.auto import tqdm
from transformers import AutoTokenizer


class NamedEntityRecognitionDataset(Dataset):
    """
    Dataset for NER task.
    """

    def __init__(
        self,
        texts: Iterable[Iterable[str]],
        tags: Iterable[Iterable[str]] = None,
        tokenizer: Union[
            str, transformers.tokenization_utils.PreTrainedTokenizer
        ] = 'distilbert-base-uncased',
        max_seq_len: int = None,
        lazy_mode: bool = True,
    ):
        self.tags = tags
        self.texts = texts

        if isinstance(tokenizer, str):
            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer)
        elif isinstance(tokenizer, transformers.tokenization_utils.PreTrainedTokenizer):
            self.tokenizer = tokenizer
        else:
            raise TypeError(
                "You pass wrong type of tokenizer. It should be a model name or PreTrainedTokenizer."
            )

        self.max_seq_len = max_seq_len
        self.length = len(texts)

        if self.max_seq_len < 3:
            raise ValueError("Max sequence length should be greather than 2")

        if not lazy_mode:
            self.encoded = [
                self._getitem_lazy(idx)
                for idx in tqdm(range(self.length), desc='tokenizing texts')
            ]
            del self.texts
            del self.tags

        self._getitem_fn = self._getitem_lazy if lazy_mode else self._getitem_encoded

    def __len__(self) -> int:
        return self.length

    def _getitem_encoded(self, index: int) -> Dict[str, torch.Tensor]:
        return self.encoded[index]

    def _getitem_lazy(self, index: int) -> Dict[str, torch.Tensor]:
        sentence = self.texts[index]
        tag = self.tags[index]

        input_ids = []
        target_tag = []

        for i, word in enumerate(sentence):
            words_piece_ids = self.tokenizer.encode(
                word,
                max_length=self.max_seq_len,
                truncation=True,
                add_special_tokens=False,
            )
            input_ids.extend(words_piece_ids)
            target_tag.extend([tag[i]] * len(words_piece_ids))

        input_ids = (
            [self.tokenizer.cls_token_id]
            + input_ids[: self.max_seq_len - 2]
            + [self.tokenizer.sep_token_id]
        )

        attention_mask = [1] * len(input_ids)

        padding_len = self.max_seq_len - len(input_ids)
        input_ids = input_ids + ([self.tokenizer.pad_token_id] * padding_len)
        attention_mask = attention_mask + ([0] * padding_len)

        target_tag = [0] + target_tag[: self.max_seq_len - 2] + [0]
        target_tag = target_tag + ([0] * padding_len)

        return {
            'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long),
            'target_tag': torch.tensor(target_tag, dtype=torch.long),
        }

    def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:
        return self._getitem_fn(index)
