

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__	:   str								=     logging.getLogger(__name__)



@dataclass
class    __snake_case	:
  __lowerCAmelCase  :						str    			=	field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}							)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}							)
  __lowerCAmelCase  :						Optional[str]    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,			)






@dataclass
class    __snake_case	:
  __lowerCAmelCase  :						str    			=	field(
      metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()							)}							)
  __lowerCAmelCase  :						str    			=	field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}							)
  __lowerCAmelCase  :						int    			=	field(
      default=128  ,			metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }  ,			)
  __lowerCAmelCase  :						bool    			=	field(
      default=lowerCAmelCase__  ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}							)






def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[InputFeatures]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    default_data_collator(_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    batch.pop('pairID'			, _SCREAMING_SNAKE_CASE							)
    return batch


def        _UpperCAmelCase							(	):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
    SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    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'			, _SCREAMING_SNAKE_CASE							)

    # Set seed
    set_seed(training_args.seed							)

    try:
        SCREAMING_SNAKE_CASE_						=    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.

    SCREAMING_SNAKE_CASE_						=    AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path			, num_labels=_SCREAMING_SNAKE_CASE			, finetuning_task=data_args.task_name			, cache_dir=model_args.cache_dir			, )
    SCREAMING_SNAKE_CASE_						=    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			, )
    SCREAMING_SNAKE_CASE_						=    AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path			, from_tf=bool('.ckpt' in model_args.model_name_or_path							)			, config=_SCREAMING_SNAKE_CASE			, cache_dir=model_args.cache_dir			, )

    # Get datasets
    SCREAMING_SNAKE_CASE_						=    (
        HansDataset(
            data_dir=data_args.data_dir			, tokenizer=_SCREAMING_SNAKE_CASE			, 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
    )
    SCREAMING_SNAKE_CASE_						=    (
        HansDataset(
            data_dir=data_args.data_dir			, tokenizer=_SCREAMING_SNAKE_CASE			, task=data_args.task_name			, max_seq_length=data_args.max_seq_length			, overwrite_cache=data_args.overwrite_cache			, evaluate=_SCREAMING_SNAKE_CASE			, )
        if training_args.do_eval
        else None
    )

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE_						=    Trainer(
        model=_SCREAMING_SNAKE_CASE			, args=_SCREAMING_SNAKE_CASE			, train_dataset=_SCREAMING_SNAKE_CASE			, eval_dataset=_SCREAMING_SNAKE_CASE			, data_collator=_SCREAMING_SNAKE_CASE			, )

    # 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 ***'							)

        SCREAMING_SNAKE_CASE_						=    trainer.predict(_SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    output.predictions
        SCREAMING_SNAKE_CASE_						=    np.argmax(_SCREAMING_SNAKE_CASE			, axis=1							)

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

        trainer._log(output.metrics							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    main()


if __name__ == "__main__":
       main()





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__	:   List[str]								=     get_tests_dir("fixtures/spiece.model")



@require_sentencepiece
@require_tokenizers
class    __snake_case	(     lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						Union[str, Any]    			=	ErnieMTokenizer
  __lowerCAmelCase  :						Union[str, Any]    			=	False
  __lowerCAmelCase  :						Optional[int]    			=	True
  __lowerCAmelCase  :						List[str]    			=	False
  __lowerCAmelCase  :						List[str]    			=	False

  def     lowerCAmelCase__  (       self):
      super().setUp()

      # We have a SentencePiece fixture for testing
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')
      tokenizer.save_pretrained(self.tmpdirname)

  def     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    'this is a test'
      SCREAMING_SNAKE_CASE_						=    'this is a test'
      return input_text, output_text

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    '<pad>'
      SCREAMING_SNAKE_CASE_						=    0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A)    ,		_A)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A)    ,		_A)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    list(self.get_tokenizer().get_vocab().keys())

      self.assertEqual(vocab_keys[0]    ,		'<pad>')
      self.assertEqual(vocab_keys[1]    ,		'<unk>')
      self.assertEqual(vocab_keys[-1]    ,		'▁eloquent')
      self.assertEqual(len(_A)    ,		30000)

  def     lowerCAmelCase__  (       self):
      self.assertEqual(self.get_tokenizer().vocab_size    ,		30000)

  def     lowerCAmelCase__  (       self):
      if not self.test_rust_tokenizer:
          return

      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()
      SCREAMING_SNAKE_CASE_						=    self.get_rust_tokenizer()

      SCREAMING_SNAKE_CASE_						=    'I was born in 92000, and this is falsé.'

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize(_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.tokenize(_A)
      self.assertListEqual(_A    ,		_A)

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode(_A    ,		add_special_tokens=_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.encode(_A    ,		add_special_tokens=_A)
      self.assertListEqual(_A    ,		_A)

      SCREAMING_SNAKE_CASE_						=    self.get_rust_tokenizer()
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode(_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.encode(_A)
      self.assertListEqual(_A    ,		_A)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		do_lower_case=_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('This is a test')
      self.assertListEqual(_A    ,		['▁this', '▁is', '▁a', '▁test'])

      self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)    ,		[48, 25, 21, 1289])

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('I was born in 92000, and this is falsé.')
      # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
      self.assertListEqual(
          _A    ,		['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'])
      SCREAMING_SNAKE_CASE_						=    tokenizer.convert_tokens_to_ids(_A)
      self.assertListEqual(_A    ,		[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])

      SCREAMING_SNAKE_CASE_						=    tokenizer.convert_ids_to_tokens(_A)
      self.assertListEqual(
          _A    ,		['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']    ,		)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('sequence builders')
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('multi-sequence build')

      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A)
      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A    ,		_A)

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
          tokenizer.sep_token_id
      ] + text_a + [tokenizer.sep_token_id]


  @slow
  def     lowerCAmelCase__  (       self):
      # fmt: off
      SCREAMING_SNAKE_CASE_						=    {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_A    ,		model_name='susnato/ernie-m-base_pytorch'    ,		sequences=[
              'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
              'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
              'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
              'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
              'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
              'conditioning on both left and right context in all layers.',
              'The quick brown fox jumps over the lazy dog.',
          ]    ,		)


