


'''simple docstring'''






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_					:    str          =    logging.getLogger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__=-1						)					->							Optional[Any]:
      # in NER datasets, the last column is usually reserved for NER label
      _a			      =							label_idx
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							List[InputExample]:
      if isinstance(__magic_name__      ,				__magic_name__						):
           _a			      =							mode.value
      _a			      =							os.path.join(__magic_name__      ,				f'{mode}.txt'						)
      _a			      =							1
      _a			      =							[]
      with open(__magic_name__      ,				encoding='utf-8'						) as f:
           _a			      =							[]
           _a			      =							[]
           for line in f:
                if line.startswith('-DOCSTART-'						) or line == "" or line == "\n":
                     if words:
                          examples.append(InputExample(guid=f'{mode}-{guid_index}'      ,				words=__magic_name__      ,				labels=__magic_name__						)						)
                          guid_index += 1
                          _a			      =							[]
                          _a			      =							[]
                else:
                     _a			      =							line.split(' '						)
                     words.append(splits[0]						)
                     if len(__magic_name__						) > 1:
                          labels.append(splits[self.label_idx].replace('\n'      ,				''						)						)
                     else:
                          # Examples could have no label for mode = "test"
                          labels.append('O'						)
           if words:
                examples.append(InputExample(guid=f'{mode}-{guid_index}'      ,				words=__magic_name__      ,				labels=__magic_name__						)						)
      return examples
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							0
      for line in test_input_reader:
           if line.startswith('-DOCSTART-'						) or line == "" or line == "\n":
                writer.write(__magic_name__						)
                if not preds_list[example_id]:
                     example_id += 1
           elif preds_list[example_id]:
                _a			      =							line.split()[0] + ' ' + preds_list[example_id].pop(0						) + '\n'
                writer.write(__magic_name__						)
           else:
                logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'      ,				line.split()[0]						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      if path:
           with open(__magic_name__      ,				'r'						) as f:
                _a			      =							f.read().splitlines()
           if "O" not in labels:
                _a			      =							['O'] + labels
           return labels
      else:
           return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self						)					->							Any:
      # in CONLL2003 dataset chunk column is second-to-last
      super().__init__(label_idx=-2						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      if path:
           with open(__magic_name__      ,				'r'						) as f:
                _a			      =							f.read().splitlines()
           if "O" not in labels:
                _a			      =							['O'] + labels
           return labels
      else:
           return [
               "O",
               "B-ADVP",
               "B-INTJ",
               "B-LST",
               "B-PRT",
               "B-NP",
               "B-SBAR",
               "B-VP",
               "B-ADJP",
               "B-CONJP",
               "B-PP",
               "I-ADVP",
               "I-INTJ",
               "I-LST",
               "I-PRT",
               "I-NP",
               "I-SBAR",
               "I-VP",
               "I-ADJP",
               "I-CONJP",
               "I-PP",
           ]





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							List[InputExample]:
      if isinstance(__magic_name__      ,				__magic_name__						):
           _a			      =							mode.value
      _a			      =							os.path.join(__magic_name__      ,				f'{mode}.txt'						)
      _a			      =							1
      _a			      =							[]

      with open(__magic_name__      ,				encoding='utf-8'						) as f:
           for sentence in parse_incr(__magic_name__						):
                _a			      =							[]
                _a			      =							[]
                for token in sentence:
                     words.append(token['form']						)
                     labels.append(token['upos']						)
                assert len(__magic_name__						) == len(__magic_name__						)
                if words:
                     examples.append(InputExample(guid=f'{mode}-{guid_index}'      ,				words=__magic_name__      ,				labels=__magic_name__						)						)
                     guid_index += 1
      return examples
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							0
      for sentence in parse_incr(__magic_name__						):
           _a			      =							preds_list[example_id]
           _a			      =							''
           for token in sentence:
                out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0						)}) '
           out += "\n"
           writer.write(__magic_name__						)
           example_id += 1




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      if path:
           with open(__magic_name__      ,				'r'						) as f:
                return f.read().splitlines()
      else:
           return [
               "ADJ",
               "ADP",
               "ADV",
               "AUX",
               "CCONJ",
               "DET",
               "INTJ",
               "NOUN",
               "NUM",
               "PART",
               "PRON",
               "PROPN",
               "PUNCT",
               "SCONJ",
               "SYM",
               "VERB",
               "X",
           ]





'''simple docstring'''






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

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a_					:    Optional[Any]          =    logging.getLogger(__name__)




@dataclass(frozen=_SCREAMING_SNAKE_CASE       )
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None





@dataclass(frozen=_SCREAMING_SNAKE_CASE       )
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None


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




 class  a     (     _SCREAMING_SNAKE_CASE       ):
  _lowerCAmelCase 					=	42
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__=False      ,				__magic_name__ = False      ,				)					->							Optional[int]:
       _a			      =							hans_processors[task]()

       _a			      =							os.path.join(
           __magic_name__      ,				'cached_{}_{}_{}_{}'.format(
               'dev' if evaluate else 'train'      ,				tokenizer.__class__.__name__      ,				str(__magic_name__						)      ,				__magic_name__      ,				)      ,				)
       _a			      =							processor.get_labels()
       if tokenizer.__class__ in (
           RobertaTokenizer,
           RobertaTokenizerFast,
           XLMRobertaTokenizer,
           BartTokenizer,
           BartTokenizerFast,
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            _a      ,   _a			      =							label_list[2], label_list[1]
       _a			      =							label_list

       # 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(__magic_name__						):
            if os.path.exists(__magic_name__						) and not overwrite_cache:
                 logger.info(f'Loading features from cached file {cached_features_file}'						)
                 _a			      =							torch.load(__magic_name__						)
            else:
                 logger.info(f'Creating features from dataset file at {data_dir}'						)

                 _a			      =							(
                     processor.get_dev_examples(__magic_name__						) if evaluate else processor.get_train_examples(__magic_name__						)
                 )

                 logger.info('Training examples: %s'      ,				len(__magic_name__						)						)
                 _a			      =							hans_convert_examples_to_features(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)
                 logger.info('Saving features into cached file %s'      ,				__magic_name__						)
                 torch.save(self.features      ,				__magic_name__						)
  def __len__( self						)					->							List[Any]:
       return len(self.features						)
  def __getitem__( self      ,				__magic_name__						)					->							InputFeatures:
       return self.features[i]




  def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
       return self.label_list


if is_tf_available():
 import tensorflow as tf




 class  a     :
  _lowerCAmelCase 					=	42
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 1_28      ,				__magic_name__=False      ,				__magic_name__ = False      ,				)					->							str:
       _a			      =							hans_processors[task]()
       _a			      =							processor.get_labels()
       if tokenizer.__class__ in (
           RobertaTokenizer,
           RobertaTokenizerFast,
           XLMRobertaTokenizer,
           BartTokenizer,
           BartTokenizerFast,
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            _a      ,   _a			      =							label_list[2], label_list[1]
       _a			      =							label_list

       _a			      =							processor.get_dev_examples(__magic_name__						) if evaluate else processor.get_train_examples(__magic_name__						)
       _a			      =							hans_convert_examples_to_features(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)

       def gen():
            for ex_index, ex in tqdm.tqdm(enumerate(self.features						)      ,				desc='convert examples to features'						):
                 if ex_index % 1_00_00 == 0:
                      logger.info('Writing example %d of %d' % (ex_index, len(__magic_name__						))						)

                 yield (
                     {
                         "example_id": 0,
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
                     },
                     ex.label,
                 )

       _a			      =							tf.data.Dataset.from_generator(
           __magic_name__      ,				(
               {
                   'example_id': tf.intaa,
                   'input_ids': tf.intaa,
                   'attention_mask': tf.intaa,
                   'token_type_ids': tf.intaa,
               },
               tf.intaa,
           )      ,				(
               {
                   'example_id': tf.TensorShape([]						),
                   'input_ids': tf.TensorShape([None, None]						),
                   'attention_mask': tf.TensorShape([None, None]						),
                   'token_type_ids': tf.TensorShape([None, None]						),
               },
               tf.TensorShape([]						),
           )      ,				)
  def 							__UpperCAmelCase		( self						)					->							Tuple:
       return self.dataset
  def __len__( self						)					->							Optional[Any]:
       return len(self.features						)
  def __getitem__( self      ,				__magic_name__						)					->							InputFeatures:
       return self.features[i]




  def 							__UpperCAmelCase		( self						)					->							List[Any]:
       return self.label_list




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Tuple:
      return self._create_examples(self._read_tsv(os.path.join(__magic_name__      ,				'heuristics_train_set.txt'						)						)      ,				'train'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[Any]:
      return self._create_examples(self._read_tsv(os.path.join(__magic_name__      ,				'heuristics_evaluation_set.txt'						)						)      ,				'dev'						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      return ["contradiction", "entailment", "neutral"]




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							[]
      for i, line in enumerate(__magic_name__						):
           if i == 0:
                continue
           _a			      =							'%s-%s' % (set_type, line[0])
           _a			      =							line[5]
           _a			      =							line[6]
           _a			      =							line[7][2:] if line[7].startswith('ex'						) else line[7]
           _a			      =							line[0]
           examples.append(InputExample(guid=__magic_name__      ,				text_a=__magic_name__      ,				text_b=__magic_name__      ,				label=__magic_name__      ,				pairID=__magic_name__						)						)
      return examples






def 	_A				(lowerCAmelCase__  :List[InputExample]				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :PreTrainedTokenizer				,						)   ->  Tuple:




     '''simple docstring'''



     _a			      =							{label: i for i, label in enumerate(lowerCAmelCase__		)}

     _a			      =							[]
     for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__		)				,						desc='convert examples to features'		):
          if ex_index % 1_00_00 == 0:
               logger.info('Writing example %d' % (ex_index)		)

          _a			      =							tokenizer(
              example.text_a				,						example.text_b				,						add_special_tokens=lowerCAmelCase__				,						max_length=lowerCAmelCase__				,						padding='max_length'				,						truncation=lowerCAmelCase__				,						return_overflowing_tokens=lowerCAmelCase__				,						)

          _a			      =							label_map[example.label] if example.label in label_map else 0

          _a			      =							int(example.pairID		)

          features.append(InputFeatures(**lowerCAmelCase__				,						label=lowerCAmelCase__				,						pairID=lowerCAmelCase__		)		)

     for i, example in enumerate(examples[:5]		):
          logger.info('*** Example ***'		)
          logger.info(f'guid: {example}'		)
          logger.info(f'features: {features[i]}'		)

     return features


a_					:    Optional[int]          =    {
    "hans": 3,
}






a_					:    Optional[Any]          =    {
    "hans": HansProcessor,
}

