




import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

UpperCamelCase								=	logging.getLogger(__name__)





@dataclass
class 					__lowerCamelCase			:




    """simple docstring"""

    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=UpperCamelCase__      ,							metadata={"help": "Overwrite the cached preprocessed datasets or not."}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={
            "help": (
                "Whether to pad all samples to `max_seq_length`. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch."
            )
        }      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                "value if set."
            )
        }      ,							)






@dataclass
class 					__lowerCamelCase			:




    """simple docstring"""

    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Train language if it is different from the evaluation language."}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}      ,							)
    snake_case__   = field(
        default="main"      ,							metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}      ,							)






def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
     lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry("run_xnli"  ,							lowerCAmelCase_     )

     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,							datefmt="%m/%d/%Y %H:%M:%S"  ,							handlers=[logging.StreamHandler(sys.stdout     )]  ,							)

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     lowerCAmelCase__            =					training_args.get_process_log_level()
     logger.setLevel(lowerCAmelCase_     )
     datasets.utils.logging.set_verbosity(lowerCAmelCase_     )
     transformers.utils.logging.set_verbosity(lowerCAmelCase_     )
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + F'distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'     )
     logger.info(F'Training/evaluation parameters {training_args}'     )

     # Detecting last checkpoint.
     lowerCAmelCase__            =					None
     if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
          lowerCAmelCase__            =					get_last_checkpoint(training_args.output_dir     )
          if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
               raise ValueError(
                   F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   "Use --overwrite_output_dir to overcome."     )
          elif last_checkpoint is not None:
               logger.info(
                   F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."     )

    # Set seed before initializing model.
     set_seed(training_args.seed     )

     # In distributed training, the load_dataset function guarantees that only one local process can concurrently
     # download the dataset.
     # Downloading and loading xnli dataset from the hub.
     if training_args.do_train:
          if model_args.train_language is None:
               lowerCAmelCase__            =					load_dataset(
                   "xnli"  ,							model_args.language  ,							split="train"  ,							cache_dir=model_args.cache_dir  ,							use_auth_token=True if model_args.use_auth_token else None  ,							)
          else:
               lowerCAmelCase__            =					load_dataset(
                   "xnli"  ,							model_args.train_language  ,							split="train"  ,							cache_dir=model_args.cache_dir  ,							use_auth_token=True if model_args.use_auth_token else None  ,							)
          lowerCAmelCase__            =					train_dataset.features["label"].names

     if training_args.do_eval:
          lowerCAmelCase__            =					load_dataset(
              "xnli"  ,							model_args.language  ,							split="validation"  ,							cache_dir=model_args.cache_dir  ,							use_auth_token=True if model_args.use_auth_token else None  ,							)
          lowerCAmelCase__            =					eval_dataset.features["label"].names

     if training_args.do_predict:
          lowerCAmelCase__            =					load_dataset(
              "xnli"  ,							model_args.language  ,							split="test"  ,							cache_dir=model_args.cache_dir  ,							use_auth_token=True if model_args.use_auth_token else None  ,							)
          lowerCAmelCase__            =					predict_dataset.features["label"].names

     # Labels
     lowerCAmelCase__            =					len(lowerCAmelCase_     )

     # Load pretrained model and tokenizer
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     lowerCAmelCase__            =					AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,							num_labels=lowerCAmelCase_  ,							idalabel={str(lowerCAmelCase_     ): label for i, label in enumerate(lowerCAmelCase_     )}  ,							labelaid={label: i for i, label in enumerate(lowerCAmelCase_     )}  ,							finetuning_task="xnli"  ,							cache_dir=model_args.cache_dir  ,							revision=model_args.model_revision  ,							use_auth_token=True if model_args.use_auth_token else None  ,							)
     lowerCAmelCase__            =					AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,							do_lower_case=model_args.do_lower_case  ,							cache_dir=model_args.cache_dir  ,							use_fast=model_args.use_fast_tokenizer  ,							revision=model_args.model_revision  ,							use_auth_token=True if model_args.use_auth_token else None  ,							)
     lowerCAmelCase__            =					AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path  ,							from_tf=bool(".ckpt" in model_args.model_name_or_path     )  ,							config=lowerCAmelCase_  ,							cache_dir=model_args.cache_dir  ,							revision=model_args.model_revision  ,							use_auth_token=True if model_args.use_auth_token else None  ,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes  ,							)

     # Preprocessing the datasets
     # Padding strategy
     if data_args.pad_to_max_length:
          lowerCAmelCase__            =					"max_length"
     else:
          # We will pad later, dynamically at batch creation, to the max sequence length in each batch
          lowerCAmelCase__            =					False

     def preprocess_function(lowerCAmelCase_	:   int     ):
          # Tokenize the texts
          return tokenizer(
              examples["premise"]  ,							examples["hypothesis"]  ,							padding=lowerCAmelCase_  ,							max_length=data_args.max_seq_length  ,							truncation=lowerCAmelCase_  ,							)

     if training_args.do_train:
          if data_args.max_train_samples is not None:
               lowerCAmelCase__            =					min(len(lowerCAmelCase_     )  ,							data_args.max_train_samples     )
               lowerCAmelCase__            =					train_dataset.select(range(lowerCAmelCase_     )     )
          with training_args.main_process_first(desc="train dataset map pre-processing"     ):
               lowerCAmelCase__            =					train_dataset.map(
                   lowerCAmelCase_  ,							batched=lowerCAmelCase_  ,							load_from_cache_file=not data_args.overwrite_cache  ,							desc="Running tokenizer on train dataset"  ,							)
          # Log a few random samples from the training set:
          for index in random.sample(range(len(lowerCAmelCase_     )     )  ,							3     ):
               logger.info(F'Sample {index} of the training set: {train_dataset[index]}.'     )

     if training_args.do_eval:
          if data_args.max_eval_samples is not None:
               lowerCAmelCase__            =					min(len(lowerCAmelCase_     )  ,							data_args.max_eval_samples     )
               lowerCAmelCase__            =					eval_dataset.select(range(lowerCAmelCase_     )     )
          with training_args.main_process_first(desc="validation dataset map pre-processing"     ):
               lowerCAmelCase__            =					eval_dataset.map(
                   lowerCAmelCase_  ,							batched=lowerCAmelCase_  ,							load_from_cache_file=not data_args.overwrite_cache  ,							desc="Running tokenizer on validation dataset"  ,							)

     if training_args.do_predict:
          if data_args.max_predict_samples is not None:
               lowerCAmelCase__            =					min(len(lowerCAmelCase_     )  ,							data_args.max_predict_samples     )
               lowerCAmelCase__            =					predict_dataset.select(range(lowerCAmelCase_     )     )
          with training_args.main_process_first(desc="prediction dataset map pre-processing"     ):
               lowerCAmelCase__            =					predict_dataset.map(
                   lowerCAmelCase_  ,							batched=lowerCAmelCase_  ,							load_from_cache_file=not data_args.overwrite_cache  ,							desc="Running tokenizer on prediction dataset"  ,							)

    # Get the metric function
     lowerCAmelCase__            =					evaluate.load("xnli"     )

     # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(lowerCAmelCase_	:   EvalPrediction     ):
          lowerCAmelCase__            =					p.predictions[0] if isinstance(p.predictions  ,							lowerCAmelCase_     ) else p.predictions
          lowerCAmelCase__            =					np.argmax(lowerCAmelCase_  ,							axis=1     )
          return metric.compute(predictions=lowerCAmelCase_  ,							references=p.label_ids     )

     # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
     if data_args.pad_to_max_length:
          lowerCAmelCase__            =					default_data_collator
     elif training_args.fpaa:
          lowerCAmelCase__            =					DataCollatorWithPadding(lowerCAmelCase_  ,							pad_to_multiple_of=8     )
     else:
          lowerCAmelCase__            =					None

     # Initialize our Trainer
     lowerCAmelCase__            =					Trainer(
         model=lowerCAmelCase_  ,							args=lowerCAmelCase_  ,							train_dataset=train_dataset if training_args.do_train else None  ,							eval_dataset=eval_dataset if training_args.do_eval else None  ,							compute_metrics=lowerCAmelCase_  ,							tokenizer=lowerCAmelCase_  ,							data_collator=lowerCAmelCase_  ,							)

     # Training
     if training_args.do_train:
          lowerCAmelCase__            =					None
          if training_args.resume_from_checkpoint is not None:
               lowerCAmelCase__            =					training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               lowerCAmelCase__            =					last_checkpoint
          lowerCAmelCase__            =					trainer.train(resume_from_checkpoint=lowerCAmelCase_     )
          lowerCAmelCase__            =					train_result.metrics
          lowerCAmelCase__            =					(
              data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase_     )
          )
          lowerCAmelCase__            =					min(lowerCAmelCase_  ,							len(lowerCAmelCase_     )     )

          trainer.save_model()  # Saves the tokenizer too for easy upload

          trainer.log_metrics("train"  ,							lowerCAmelCase_     )
          trainer.save_metrics("train"  ,							lowerCAmelCase_     )
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          logger.info("*** Evaluate ***"     )
          lowerCAmelCase__            =					trainer.evaluate(eval_dataset=lowerCAmelCase_     )

          lowerCAmelCase__            =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase_     )
          lowerCAmelCase__            =					min(lowerCAmelCase_  ,							len(lowerCAmelCase_     )     )

          trainer.log_metrics("eval"  ,							lowerCAmelCase_     )
          trainer.save_metrics("eval"  ,							lowerCAmelCase_     )

     # Prediction
     if training_args.do_predict:
          logger.info("*** Predict ***"     )
          lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					trainer.predict(lowerCAmelCase_  ,							metric_key_prefix="predict"     )

          lowerCAmelCase__            =					(
              data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCAmelCase_     )
          )
          lowerCAmelCase__            =					min(lowerCAmelCase_  ,							len(lowerCAmelCase_     )     )

          trainer.log_metrics("predict"  ,							lowerCAmelCase_     )
          trainer.save_metrics("predict"  ,							lowerCAmelCase_     )

          lowerCAmelCase__            =					np.argmax(lowerCAmelCase_  ,							axis=1     )
          lowerCAmelCase__            =					os.path.join(training_args.output_dir  ,							"predictions.txt"     )
          if trainer.is_world_process_zero():
               with open(lowerCAmelCase_  ,							"w"     ) as writer:
                    writer.write("index\tprediction\n"     )
                    for index, item in enumerate(lowerCAmelCase_     ):
                         lowerCAmelCase__            =					label_list[item]
                         writer.write(F'{index}\t{item}\n'     )


if __name__ == "__main__":
     main()








import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class     _a							(	datasets.BeamBasedBuilder     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     return datasets.DatasetInfo(
         features=datasets.Features({'content': datasets.Value('string'			)}			)		,		supervised_keys=_UpperCAmelCase		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[Any]:
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,		gen_kwargs={'examples': get_test_dummy_examples()}			)]




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     import apache_beam as beam

     return pipeline | "Load Examples" >> beam.Create(_UpperCAmelCase			)




class     _a							(	datasets.BeamBasedBuilder     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Any:
     return datasets.DatasetInfo(
         features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'			)}			)}			)		,		supervised_keys=_UpperCAmelCase		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     return [
         datasets.SplitGenerator(name=datasets.Split.TRAIN		,		gen_kwargs={'examples': get_test_nested_examples()}			)
     ]




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     import apache_beam as beam

     return pipeline | "Load Examples" >> beam.Create(_UpperCAmelCase			)






def      _snake_case     ():
   return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar'])]






def      _snake_case     ():
   return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar'])]


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  len(get_test_dummy_examples()			)
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  DummyBeamDataset(cache_dir=_UpperCAmelCase		,		beam_runner='DirectRunner'			)
        builder.download_and_prepare()
        self.assertTrue(
            os.path.exists(
                os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train.arrow"""			)			)			)
        self.assertDictEqual(builder.info.features		,		datasets.Features({'content': datasets.Value('string'			)}			)			)
        UpperCamelCase_						=  builder.as_dataset()
        self.assertEqual(dset['train'].num_rows		,		_UpperCAmelCase			)
        self.assertEqual(dset['train'].info.splits['train'].num_examples		,		_UpperCAmelCase			)
        self.assertDictEqual(dset['train'][0]		,		get_test_dummy_examples()[0][1]			)
        self.assertDictEqual(
            dset['train'][expected_num_examples - 1]		,		get_test_dummy_examples()[expected_num_examples - 1][1]			)
        self.assertTrue(
            os.path.exists(os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		'dataset_info.json'			)			)			)
        del dset




  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     import apache_beam as beam

     UpperCamelCase_						=  beam.io.parquetio.WriteToParquet

     UpperCamelCase_						=  len(get_test_dummy_examples()			)
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  DummyBeamDataset(cache_dir=_UpperCAmelCase		,		beam_runner='DirectRunner'			)
        with patch('apache_beam.io.parquetio.WriteToParquet'			) as write_parquet_mock:
           UpperCamelCase_						=  partial(_UpperCAmelCase		,		num_shards=2			)
           builder.download_and_prepare()
        self.assertTrue(
            os.path.exists(
                os.path.join(
                    _UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train-00000-of-00002.arrow"""			)			)			)
        self.assertTrue(
            os.path.exists(
                os.path.join(
                    _UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train-00000-of-00002.arrow"""			)			)			)
        self.assertDictEqual(builder.info.features		,		datasets.Features({'content': datasets.Value('string'			)}			)			)
        UpperCamelCase_						=  builder.as_dataset()
        self.assertEqual(dset['train'].num_rows		,		_UpperCAmelCase			)
        self.assertEqual(dset['train'].info.splits['train'].num_examples		,		_UpperCAmelCase			)
        # Order is not preserved when sharding, so we just check that all the elements are there
        self.assertListEqual(sorted(dset['train']['content']			)		,		sorted(['foo', 'bar', 'foobar']			)			)
        self.assertTrue(
            os.path.exists(os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		'dataset_info.json'			)			)			)
        del dset




  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			Any:
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  DummyBeamDataset(cache_dir=_UpperCAmelCase			)
        self.assertRaises(datasets.builder.MissingBeamOptions		,		builder.download_and_prepare			)




  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  len(get_test_nested_examples()			)
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  NestedBeamDataset(cache_dir=_UpperCAmelCase		,		beam_runner='DirectRunner'			)
        builder.download_and_prepare()
        self.assertTrue(
            os.path.exists(
                os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train.arrow"""			)			)			)
        self.assertDictEqual(
            builder.info.features		,		datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'			)}			)}			)			)
        UpperCamelCase_						=  builder.as_dataset()
        self.assertEqual(dset['train'].num_rows		,		_UpperCAmelCase			)
        self.assertEqual(dset['train'].info.splits['train'].num_examples		,		_UpperCAmelCase			)
        self.assertDictEqual(dset['train'][0]		,		get_test_nested_examples()[0][1]			)
        self.assertDictEqual(
            dset['train'][expected_num_examples - 1]		,		get_test_nested_examples()[expected_num_examples - 1][1]			)
        self.assertTrue(
            os.path.exists(os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		'dataset_info.json'			)			)			)
        del dset





