




"""simple docstring"""

def 						_snake_case					( _snake_case							: int    ,  _snake_case							: int							)   -> bool:






      '''simple docstring'''


      return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()







"""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


a         =      logging.getLogger(__name__)





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					str
 UpperCAmelCase   :					List[str]
 UpperCAmelCase   :					Optional[List[str]]





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					List[int]
 UpperCAmelCase   :					List[int]
 UpperCAmelCase   :					Optional[List[int]]				  =     None
 UpperCAmelCase   :					Optional[List[int]]				  =     None





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     '''train'''
 UpperCAmelCase   :					Tuple				  =     '''dev'''
 UpperCAmelCase   :					int				  =     '''test'''





class     lowercase_	:



 '''simple docstring'''

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Union[Split, str]		):
       raise NotImplementedError

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	str		):
       raise NotImplementedError



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

       _A        =       []
       for ex_index, example in enumerate(_UpperCAmelCase		):
             if ex_index % 10_000 == 0:
                   logger.info('Writing example %d of %d'   ,							_UpperCAmelCase   ,							len(_UpperCAmelCase		)		)

             _A        =       []
             _A        =       []
             for word, label in zip(example.words   ,							example.labels		):
                   _A        =       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        =       tokenizer.num_special_tokens_to_add()
             if len(_UpperCAmelCase		) > max_seq_length - special_tokens_count:
                   _A        =       tokens[: (max_seq_length - special_tokens_count)]
                   _A        =       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        =       [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        =       [cls_token] + tokens
                   _A        =       [pad_token_label_id] + label_ids
                   _A        =       [cls_token_segment_id] + segment_ids

             _A        =       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        =       [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase		)

             # Zero-pad up to the sequence length.
             _A        =       max_seq_length - len(_UpperCAmelCase		)
             if pad_on_left:
                   _A        =       ([pad_token] * padding_length) + input_ids
                   _A        =       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   _A        =       ([pad_token_segment_id] * padding_length) + segment_ids
                   _A        =       ([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        =       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     lowercase_	(					__lowerCAmelCase							):



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

        def __init__(			self	:	int   ,							_UpperCAmelCase	:	TokenClassificationTask   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	Split = Split.train   ,							):
              # Load data features from cache or dataset file
              _A        =       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        =       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        =       torch.load(_UpperCAmelCase		)
                    else:
                          logger.info(F'''Creating features from dataset file at {data_dir}'''		)
                          _A        =       token_classification_task.read_examples_from_file(_UpperCAmelCase   ,							_UpperCAmelCase		)
                          # TODO clean up all this to leverage built-in features of tokenizers
                          _A        =       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	:	Dict		):
              return len(self.features		)



        def __getitem__(			self	:	int   ,							_UpperCAmelCase	:	Union[str, Any]		):
              return self.features[i]


if is_tf_available():
       import tensorflow as tf





       class     lowercase_	:



        '''simple docstring'''
        UpperCAmelCase   :					List[InputFeatures]
        UpperCAmelCase   :					int				  =     -100

        def __init__(			self	:	int   ,							_UpperCAmelCase	:	TokenClassificationTask   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	Split = Split.train   ,							):
              _A        =       token_classification_task.read_examples_from_file(_UpperCAmelCase   ,							_UpperCAmelCase		)
              # TODO clean up all this to leverage built-in features of tokenizers
              _A        =       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        =       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        =       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       lowerCAmelCase_							(			self	:	Dict		):
              _A        =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

              return self.dataset

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



        def __getitem__(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]		):
              return self.features[i]

