

"""simple docstring"""




import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class        __lowercase     (      __lowerCamelCase		,     unittest.TestCase   ):
  snake_case_			    =    RealmTokenizer
  snake_case_			    =    RealmTokenizerFast
  snake_case_			    =    True
  snake_case_			    =    True
  snake_case_			    =    filter_non_english

  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   super().setUp()

   UpperCAmelCase__   :					Optional[int]   				=   [
       """[UNK]""",
       """[CLS]""",
       """[SEP]""",
       """[PAD]""",
       """[MASK]""",
       """want""",
       """##want""",
       """##ed""",
       """wa""",
       """un""",
       """runn""",
       """##ing""",
       """,""",
       """low""",
       """lowest""",
   ]
   UpperCAmelCase__   :					Optional[Any]   				=   os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["""vocab_file"""]      )
   with open(self.vocab_file   ,"""w"""   ,encoding="""utf-8"""      ) as vocab_writer:
    vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )

  def 				__lowercase    (					self						:     int   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   """UNwant\u00E9d,running"""
   UpperCAmelCase__   :					Dict   				=   """unwanted, running"""
   return input_text, output_text

  def 				__lowercase    (					self						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   self.tokenizer_class(self.vocab_file      )

   UpperCAmelCase__   :					Optional[int]   				=   tokenizer.tokenize("""UNwant\u00E9d,running"""      )
   self.assertListEqual(A   ,["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]      )
   self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )   ,[9, 6, 7, 12, 10, 11]      )

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   if not self.test_rust_tokenizer:
    return

   UpperCAmelCase__   :					str   				=   self.get_tokenizer()
   UpperCAmelCase__   :					List[str]   				=   self.get_rust_tokenizer()

   UpperCAmelCase__   :					int   				=   """UNwant\u00E9d,running"""

   UpperCAmelCase__   :					Optional[Any]   				=   tokenizer.tokenize(A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   rust_tokenizer.tokenize(A      )
   self.assertListEqual(A   ,A      )

   UpperCAmelCase__   :					Optional[Any]   				=   tokenizer.encode(A   ,add_special_tokens=A      )
   UpperCAmelCase__   :					Dict   				=   rust_tokenizer.encode(A   ,add_special_tokens=A      )
   self.assertListEqual(A   ,A      )

   UpperCAmelCase__   :					Dict   				=   self.get_rust_tokenizer()
   UpperCAmelCase__   :					int   				=   tokenizer.encode(A      )
   UpperCAmelCase__   :					Any   				=   rust_tokenizer.encode(A      )
   self.assertListEqual(A   ,A      )

   # With lower casing
   UpperCAmelCase__   :					int   				=   self.get_tokenizer(do_lower_case=A      )
   UpperCAmelCase__   :					List[Any]   				=   self.get_rust_tokenizer(do_lower_case=A      )

   UpperCAmelCase__   :					str   				=   """UNwant\u00E9d,running"""

   UpperCAmelCase__   :					Union[str, Any]   				=   tokenizer.tokenize(A      )
   UpperCAmelCase__   :					Tuple   				=   rust_tokenizer.tokenize(A      )
   self.assertListEqual(A   ,A      )

   UpperCAmelCase__   :					Optional[Any]   				=   tokenizer.encode(A   ,add_special_tokens=A      )
   UpperCAmelCase__   :					Optional[int]   				=   rust_tokenizer.encode(A   ,add_special_tokens=A      )
   self.assertListEqual(A   ,A      )

   UpperCAmelCase__   :					Union[str, Any]   				=   self.get_rust_tokenizer()
   UpperCAmelCase__   :					Dict   				=   tokenizer.encode(A      )
   UpperCAmelCase__   :					Tuple   				=   rust_tokenizer.encode(A      )
   self.assertListEqual(A   ,A      )

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   BasicTokenizer()

   self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""      )   ,["""ah""", """\u535A""", """\u63A8""", """zz"""]      )

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   BasicTokenizer(do_lower_case=A      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """      )   ,["""hello""", """!""", """how""", """are""", """you""", """?"""]      )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )   ,["""hello"""]      )

  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   BasicTokenizer(do_lower_case=A   ,strip_accents=A      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )   ,["""hällo""", """!""", """how""", """are""", """you""", """?"""]      )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )   ,["""h\u00E9llo"""]      )

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   BasicTokenizer(do_lower_case=A   ,strip_accents=A      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )   ,["""hallo""", """!""", """how""", """are""", """you""", """?"""]      )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )   ,["""hello"""]      )

  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   BasicTokenizer(do_lower_case=A      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )   ,["""hallo""", """!""", """how""", """are""", """you""", """?"""]      )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )   ,["""hello"""]      )

  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   BasicTokenizer(do_lower_case=A      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """      )   ,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   BasicTokenizer(do_lower_case=A   ,strip_accents=A      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )   ,["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   BasicTokenizer(do_lower_case=A   ,strip_accents=A      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )   ,["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )

  def 				__lowercase    (					self						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   BasicTokenizer(do_lower_case=A   ,never_split=["""[UNK]"""]      )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""      )   ,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]      )

  def 				__lowercase    (					self						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

   UpperCAmelCase__   :					Union[str, Any]   				=   {}
   for i, token in enumerate(A      ):
    UpperCAmelCase__   :					Optional[Any]   				=   i
   UpperCAmelCase__   :					Dict   				=   WordpieceTokenizer(vocab=A   ,unk_token="""[UNK]"""      )

   self.assertListEqual(tokenizer.tokenize(""""""      )   ,[]      )

   self.assertListEqual(tokenizer.tokenize("""unwanted running"""      )   ,["""un""", """##want""", """##ed""", """runn""", """##ing"""]      )

   self.assertListEqual(tokenizer.tokenize("""unwantedX running"""      )   ,["""[UNK]""", """runn""", """##ing"""]      )

  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   self.assertTrue(_is_whitespace(""" """      )      )
   self.assertTrue(_is_whitespace("""\t"""      )      )
   self.assertTrue(_is_whitespace("""\r"""      )      )
   self.assertTrue(_is_whitespace("""\n"""      )      )
   self.assertTrue(_is_whitespace("""\u00A0"""      )      )

   self.assertFalse(_is_whitespace("""A"""      )      )
   self.assertFalse(_is_whitespace("""-"""      )      )

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   self.assertTrue(_is_control("""\u0005"""      )      )

   self.assertFalse(_is_control("""A"""      )      )
   self.assertFalse(_is_control(""" """      )      )
   self.assertFalse(_is_control("""\t"""      )      )
   self.assertFalse(_is_control("""\r"""      )      )

  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   self.assertTrue(_is_punctuation("""-"""      )      )
   self.assertTrue(_is_punctuation("""$"""      )      )
   self.assertTrue(_is_punctuation("""`"""      )      )
   self.assertTrue(_is_punctuation("""."""      )      )

   self.assertFalse(_is_punctuation("""A"""      )      )
   self.assertFalse(_is_punctuation(""" """      )      )

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   self.get_tokenizer()

   # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
   self.assertListEqual([tokenizer.tokenize(A      ) for t in ["""Test""", """\xad""", """test"""]]   ,[["""[UNK]"""], [], ["""[UNK]"""]]      )

   if self.test_rust_tokenizer:
    UpperCAmelCase__   :					List[Any]   				=   self.get_rust_tokenizer()
    self.assertListEqual(
        [rust_tokenizer.tokenize(A      ) for t in ["""Test""", """\xad""", """test"""]]   ,[["""[UNK]"""], [], ["""[UNK]"""]]      )

  @slow
  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   self.tokenizer_class.from_pretrained("""bert-base-uncased"""      )

   UpperCAmelCase__   :					int   				=   tokenizer.encode("""sequence builders"""   ,add_special_tokens=A      )
   UpperCAmelCase__   :					int   				=   tokenizer.encode("""multi-sequence build"""   ,add_special_tokens=A      )

   UpperCAmelCase__   :					Optional[int]   				=   tokenizer.build_inputs_with_special_tokens(A      )
   UpperCAmelCase__   :					Optional[int]   				=   tokenizer.build_inputs_with_special_tokens(A   ,A      )

   assert encoded_sentence == [101] + text + [102]
   assert encoded_pair == [101] + text + [102] + text_a + [102]

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
     UpperCAmelCase__   :					str   				=   self.rust_tokenizer_class.from_pretrained(A   ,**A      )

     UpperCAmelCase__   :					int   				=   f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
     UpperCAmelCase__   :					Optional[int]   				=   tokenizer_r.encode_plus(
         A   ,return_attention_mask=A   ,return_token_type_ids=A   ,return_offsets_mapping=A   ,add_special_tokens=A   ,)

     UpperCAmelCase__   :					Dict   				=   tokenizer_r.do_lower_case if hasattr(A   ,"""do_lower_case"""      ) else False
     UpperCAmelCase__   :					Any   				=   (
         [
             ((0, 0), tokenizer_r.cls_token),
             ((0, 1), """A"""),
             ((1, 2), ""","""),
             ((3, 5), """na"""),
             ((5, 6), """##ï"""),
             ((6, 8), """##ve"""),
             ((9, 15), tokenizer_r.mask_token),
             ((16, 21), """Allen"""),
             ((21, 23), """##NL"""),
             ((23, 24), """##P"""),
             ((25, 33), """sentence"""),
             ((33, 34), """."""),
             ((0, 0), tokenizer_r.sep_token),
         ]
         if not do_lower_case
         else [
             ((0, 0), tokenizer_r.cls_token),
             ((0, 1), """a"""),
             ((1, 2), ""","""),
             ((3, 8), """naive"""),
             ((9, 15), tokenizer_r.mask_token),
             ((16, 21), """allen"""),
             ((21, 23), """##nl"""),
             ((23, 24), """##p"""),
             ((25, 33), """sentence"""),
             ((33, 34), """."""),
             ((0, 0), tokenizer_r.sep_token),
         ]
     )

     self.assertEqual(
         [e[1] for e in expected_results]   ,tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]      )      )
     self.assertEqual([e[0] for e in expected_results]   ,tokens["""offset_mapping"""]      )




  @slow
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
     UpperCAmelCase__   :					Dict   				=   self.rust_tokenizer_class.from_pretrained(A   ,**A      )
     UpperCAmelCase__   :					Union[str, Any]   				=   self.tokenizer_class.from_pretrained(A   ,**A      )
     UpperCAmelCase__   :					Dict   				=   [["""Hello world!""", """Nice to meet you!"""], ["""The cute cat.""", """The adorable dog."""]]

     UpperCAmelCase__   :					int   				=   tokenizer_r.batch_encode_candidates(A   ,max_length=10   ,return_tensors="""np"""      )
     UpperCAmelCase__   :					List[str]   				=   tokenizer_p.batch_encode_candidates(A   ,max_length=10   ,return_tensors="""np"""      )

     UpperCAmelCase__   :					Dict   				=   (2, 2, 10)

     self.assertEqual(encoded_sentence_r["""input_ids"""].shape   ,A      )
     self.assertEqual(encoded_sentence_r["""attention_mask"""].shape   ,A      )
     self.assertEqual(encoded_sentence_r["""token_type_ids"""].shape   ,A      )

     self.assertEqual(encoded_sentence_p["""input_ids"""].shape   ,A      )
     self.assertEqual(encoded_sentence_p["""attention_mask"""].shape   ,A      )
     self.assertEqual(encoded_sentence_p["""token_type_ids"""].shape   ,A      )








"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCAmelCase	=			logging.getLogger(__name__)







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,     )







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()   )}   )
  snake_case_			    =    field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
  snake_case_			    =    field(
      default=1_2_8		,     metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )







def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   default_data_collator(__UpperCamelCase )
 UpperCAmelCase__   :					List[str]   				=   batch.pop("""pairID"""							,      __UpperCamelCase )
 return batch
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
 UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					Union[str, Any]   				=   parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir )
     and os.listdir(training_args.output_dir )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
      """ --overwrite_output_dir to overcome.""" )

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,      )
 logger.warning(
     """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,      training_args.local_rank							,      training_args.device							,      training_args.n_gpu							,      bool(training_args.local_rank != -1 )							,      training_args.fpaa							,      )
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("""Training/evaluation parameters %s"""							,      __UpperCamelCase )

 # Set seed
 set_seed(training_args.seed )

 try:
  UpperCAmelCase__   :					Optional[int]   				=   hans_tasks_num_labels[data_args.task_name]
 except KeyError:
  raise ValueError("""Task not found: %s""" % (data_args.task_name) )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCAmelCase__   :					Tuple   				=   AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,      num_labels=__UpperCamelCase							,      finetuning_task=data_args.task_name							,      cache_dir=model_args.cache_dir							,      )
 UpperCAmelCase__   :					int   				=   AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      )
 UpperCAmelCase__   :					List[Any]   				=   AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path							,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path )							,      config=__UpperCamelCase							,      cache_dir=model_args.cache_dir							,      )

 # Get datasets
 UpperCAmelCase__   :					Optional[int]   				=   (
     HansDataset(
         data_dir=data_args.data_dir							,      tokenizer=__UpperCamelCase							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      )
     if training_args.do_train
     else None
 )
 UpperCAmelCase__   :					str   				=   (
     HansDataset(
         data_dir=data_args.data_dir							,      tokenizer=__UpperCamelCase							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      evaluate=__UpperCamelCase							,      )
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 UpperCAmelCase__   :					List[Any]   				=   Trainer(
     model=__UpperCamelCase							,      args=__UpperCamelCase							,      train_dataset=__UpperCamelCase							,      eval_dataset=__UpperCamelCase							,      data_collator=__UpperCamelCase							,      )

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
 if training_args.do_eval:
  logger.info("""*** Evaluate ***""" )

  UpperCAmelCase__   :					Optional[Any]   				=   trainer.predict(__UpperCamelCase )
  UpperCAmelCase__   :					List[str]   				=   output.predictions
  UpperCAmelCase__   :					Dict   				=   np.argmax(__UpperCamelCase							,      axis=1 )

  UpperCAmelCase__   :					Optional[Any]   				=   [ex.pairID for ex in eval_dataset]
  UpperCAmelCase__   :					Dict   				=   os.path.join(training_args.output_dir							,      """hans_predictions.txt""" )
  UpperCAmelCase__   :					Dict   				=   eval_dataset.get_labels()
  if trainer.is_world_master():
   with open(__UpperCamelCase							,      """w""" ) as writer:
    writer.write("""pairID,gold_label\n""" )
    for pid, pred in zip(__UpperCamelCase							,      __UpperCamelCase ):
     writer.write("""ex""" + str(__UpperCamelCase ) + """,""" + label_list[int(__UpperCamelCase )] + """\n""" )

  trainer._log(output.metrics )
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 main()


if __name__ == "__main__":
      main()





