


"""simple docstring"""







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowercase    					=      logging.getLogger(__name__)






@dataclass
class 						_A  :







    """simple docstring"""




    UpperCAmelCase       :							str
    UpperCAmelCase       :							List[str]
    UpperCAmelCase       :							Optional[List[str]]






@dataclass
class 						_A  :







    """simple docstring"""




    UpperCAmelCase       :							List[int]
    UpperCAmelCase       :							List[int]
    UpperCAmelCase       :							Optional[List[int]]	=       None
    UpperCAmelCase       :							Optional[List[int]]	=       None






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       """train"""
    UpperCAmelCase       :							Any	=       """dev"""
    UpperCAmelCase       :							Tuple	=       """test"""






class 						_A  :







    """simple docstring"""




    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    str				, __UpperCAmelCase				:    Union[Split, str]):
      raise NotImplementedError






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    str):
      raise NotImplementedError






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    List[InputExample]				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    PreTrainedTokenizer				, __UpperCAmelCase				:    Optional[int]=False				, __UpperCAmelCase				:    str="[CLS]"				, __UpperCAmelCase				:    List[Any]=1				, __UpperCAmelCase				:    Any="[SEP]"				, __UpperCAmelCase				:    Any=False				, __UpperCAmelCase				:    List[Any]=False				, __UpperCAmelCase				:    List[str]=0				, __UpperCAmelCase				:    Optional[Any]=0				, __UpperCAmelCase				:    List[Any]=-100				, __UpperCAmelCase				:    Any=0				, __UpperCAmelCase				:    Union[str, Any]=True				, ):
      a						:		str      							=			{label: i for i, label in enumerate(__UpperCAmelCase)}

      a						:		Optional[Any]      							=			[]
      for ex_index, example in enumerate(__UpperCAmelCase):
        if ex_index % 10000 == 0:
          logger.info("Writing example %d of %d"				, __UpperCAmelCase				, len(__UpperCAmelCase))

        a						:		str      							=			[]
        a						:		int      							=			[]
        for word, label in zip(example.words				, example.labels):
          a						:		Tuple      							=			tokenizer.tokenize(__UpperCAmelCase)

          # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
          if len(__UpperCAmelCase) > 0:
            tokens.extend(__UpperCAmelCase)
            # Use the real label id for the first token of the word, and padding ids for the remaining tokens
            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__UpperCAmelCase) - 1))

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        a						:		Dict      							=			tokenizer.num_special_tokens_to_add()
        if len(__UpperCAmelCase) > max_seq_length - special_tokens_count:
          a						:		Dict      							=			tokens[: (max_seq_length - special_tokens_count)]
          a						:		str      							=			label_ids[: (max_seq_length - special_tokens_count)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
        if sep_token_extra:
          # roberta uses an extra separator b/w pairs of sentences
          tokens += [sep_token]
          label_ids += [pad_token_label_id]
        a						:		Union[str, Any]      							=			[sequence_a_segment_id] * len(__UpperCAmelCase)

        if cls_token_at_end:
          tokens += [cls_token]
          label_ids += [pad_token_label_id]
          segment_ids += [cls_token_segment_id]
        else:
          a						:		List[str]      							=			[cls_token] + tokens
          a						:		Tuple      							=			[pad_token_label_id] + label_ids
          a						:		List[Any]      							=			[cls_token_segment_id] + segment_ids

        a						:		str      							=			tokenizer.convert_tokens_to_ids(__UpperCAmelCase)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        a						:		int      							=			[1 if mask_padding_with_zero else 0] * len(__UpperCAmelCase)

        # Zero-pad up to the sequence length.
        a						:		str      							=			max_seq_length - len(__UpperCAmelCase)
        if pad_on_left:
          a						:		Union[str, Any]      							=			([pad_token] * padding_length) + input_ids
          a						:		Tuple      							=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
          a						:		Optional[Any]      							=			([pad_token_segment_id] * padding_length) + segment_ids
          a						:		int      							=			([pad_token_label_id] * padding_length) + label_ids
        else:
          input_ids += [pad_token] * padding_length
          input_mask += [0 if mask_padding_with_zero else 1] * padding_length
          segment_ids += [pad_token_segment_id] * padding_length
          label_ids += [pad_token_label_id] * padding_length

        assert len(__UpperCAmelCase) == max_seq_length
        assert len(__UpperCAmelCase) == max_seq_length
        assert len(__UpperCAmelCase) == max_seq_length
        assert len(__UpperCAmelCase) == max_seq_length

        if ex_index < 5:
          logger.info("*** Example ***")
          logger.info("guid: %s"				, example.guid)
          logger.info("tokens: %s"				, " ".join([str(__UpperCAmelCase) for x in tokens]))
          logger.info("input_ids: %s"				, " ".join([str(__UpperCAmelCase) for x in input_ids]))
          logger.info("input_mask: %s"				, " ".join([str(__UpperCAmelCase) for x in input_mask]))
          logger.info("segment_ids: %s"				, " ".join([str(__UpperCAmelCase) for x in segment_ids]))
          logger.info("label_ids: %s"				, " ".join([str(__UpperCAmelCase) for x in label_ids]))

        if "token_type_ids" not in tokenizer.model_input_names:
          a						:		Optional[Any]      							=			None

        features.append(
            InputFeatures(
                input_ids=__UpperCAmelCase				, attention_mask=__UpperCAmelCase				, token_type_ids=__UpperCAmelCase				, label_ids=__UpperCAmelCase))
      return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset






 class 						_A  (  _a  ):







     """simple docstring"""




     UpperCAmelCase       :							List[InputFeatures]
     UpperCAmelCase       :							int	=       nn.CrossEntropyLoss().ignore_index






     def __init__(					self				:    Optional[Any]				, __UpperCAmelCase				:    TokenClassificationTask				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    PreTrainedTokenizer				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[int] = None				, __UpperCAmelCase				:    int=False				, __UpperCAmelCase				:    Split = Split.train				, ):
       # Load data features from cache or dataset file
       a						:		int      							=			os.path.join(
           __UpperCAmelCase				, "cached_{}_{}_{}".format(mode.value				, tokenizer.__class__.__name__				, str(__UpperCAmelCase))				, )

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       a						:		Dict      							=			cached_features_file + ".lock"
       with FileLock(__UpperCAmelCase):
         if os.path.exists(__UpperCAmelCase) and not overwrite_cache:
           logger.info(f'''Loading features from cached file {cached_features_file}''')
           a						:		Optional[int]      							=			torch.load(__UpperCAmelCase)
         else:
           logger.info(f'''Creating features from dataset file at {data_dir}''')
           a						:		Tuple      							=			token_classification_task.read_examples_from_file(__UpperCAmelCase				, __UpperCAmelCase)
           # TODO clean up all this to leverage built-in features of tokenizers
           a						:		Any      							=			token_classification_task.convert_examples_to_features(
               __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, cls_token_at_end=bool(model_type in ["xlnet"])				, cls_token=tokenizer.cls_token				, cls_token_segment_id=2 if model_type in ["xlnet"] else 0				, sep_token=tokenizer.sep_token				, sep_token_extra=__UpperCAmelCase				, pad_on_left=bool(tokenizer.padding_side == "left")				, pad_token=tokenizer.pad_token_id				, pad_token_segment_id=tokenizer.pad_token_type_id				, pad_token_label_id=self.pad_token_label_id				, )
           logger.info(f'''Saving features into cached file {cached_features_file}''')
           torch.save(self.features				, __UpperCAmelCase)






     def __len__(					self				:    int):
       return len(self.features)






     def __getitem__(					self				:    str				, __UpperCAmelCase				:    Tuple):
       return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class 						_A  :







     """simple docstring"""




     UpperCAmelCase       :							List[InputFeatures]
     UpperCAmelCase       :							int	=       -1_0_0






     def __init__(					self				:    str				, __UpperCAmelCase				:    TokenClassificationTask				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    PreTrainedTokenizer				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[int] = None				, __UpperCAmelCase				:    Tuple=False				, __UpperCAmelCase				:    Split = Split.train				, ):
       a						:		Optional[Any]      							=			token_classification_task.read_examples_from_file(__UpperCAmelCase				, __UpperCAmelCase)
       # TODO clean up all this to leverage built-in features of tokenizers
       a						:		Tuple      							=			token_classification_task.convert_examples_to_features(
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, cls_token_at_end=bool(model_type in ["xlnet"])				, cls_token=tokenizer.cls_token				, cls_token_segment_id=2 if model_type in ["xlnet"] else 0				, sep_token=tokenizer.sep_token				, sep_token_extra=__UpperCAmelCase				, pad_on_left=bool(tokenizer.padding_side == "left")				, pad_token=tokenizer.pad_token_id				, pad_token_segment_id=tokenizer.pad_token_type_id				, pad_token_label_id=self.pad_token_label_id				, )

       def gen():
         for ex in self.features:
           if ex.token_type_ids is None:
             yield (
                 {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                 ex.label_ids,
             )
           else:
             yield (
                 {
                     "input_ids": ex.input_ids,
                     "attention_mask": ex.attention_mask,
                     "token_type_ids": ex.token_type_ids,
                 },
                 ex.label_ids,
             )

       if "token_type_ids" not in tokenizer.model_input_names:
         a						:		List[Any]      							=			tf.data.Dataset.from_generator(
             __UpperCAmelCase				, ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)				, (
                 {"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])},
                 tf.TensorShape([None]),
             )				, )
       else:
         a						:		Tuple      							=			tf.data.Dataset.from_generator(
             __UpperCAmelCase				, ({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)				, (
                 {
                     "input_ids": tf.TensorShape([None]),
                     "attention_mask": tf.TensorShape([None]),
                     "token_type_ids": tf.TensorShape([None]),
                 },
                 tf.TensorShape([None]),
             )				, )






     def 	__snake_case							(					self				:    Optional[Any]):
       a						:		str      							=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

       return self.dataset






     def __len__(					self				:    Optional[Any]):
       return len(self.features)






     def __getitem__(					self				:    str				, __UpperCAmelCase				:    Dict):
       return self.features[i]










"""simple docstring"""







def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  if isinstance(A_  ,   A_       ):
    raise TypeError("'float' object cannot be interpreted as an integer"       )
  if isinstance(A_  ,   A_       ):
    raise TypeError("'str' object cannot be interpreted as an integer"       )

  if num == 0:
    return "0b0"

  a						:		Optional[Any]      							=			False

  if num < 0:
    a						:		Tuple      							=			True
    a						:		str      							=			-num

  a						:		list[int]      							=			[]
  while num > 0:
    binary.insert(0  ,   num % 2       )
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_       ) for e in binary       )

  return "0b" + "".join(str(A_       ) for e in binary       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






