





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"""






      UpperCAmelCase_  :					Dict      			=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."
              )
          }       ,      )
      UpperCAmelCase_  :					str      			=field(
          default=lowercase__       ,      metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
      UpperCAmelCase_  :					Any      			=field(
          default=lowercase__       ,      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."
              )
          }       ,      )
      UpperCAmelCase_  :					List[Any]      			=field(
          default=lowercase__       ,      metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of training examples to this "
                  "value if set."
              )
          }       ,      )
      UpperCAmelCase_  :					Dict      			=field(
          default=lowercase__       ,      metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                  "value if set."
              )
          }       ,      )
      UpperCAmelCase_  :					Tuple      			=field(
          default=lowercase__       ,      metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                  "value if set."
              )
          }       ,      )




@dataclass
class  _lowerCamelCase       :



      """simple docstring"""






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







def 					lowerCAmelCase__(		)       ->			Union[str, Any]:
  __snake_case      : Optional[int]						=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
  __snake_case      : int						=				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"      ,   _A )

  # 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()

  __snake_case      : Dict						=				training_args.get_process_log_level()
  logger.setLevel(_A )
  datasets.utils.logging.set_verbosity(_A )
  transformers.utils.logging.set_verbosity(_A )
  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.
  __snake_case      : str						=				None
  if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
    __snake_case      : Dict						=				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:
      __snake_case      : int						=				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:
      __snake_case      : int						=				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      ,   )
    __snake_case      : List[Any]						=				train_dataset.features["label"].names

  if training_args.do_eval:
    __snake_case      : Any						=				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      ,   )
    __snake_case      : List[str]						=				eval_dataset.features["label"].names

  if training_args.do_predict:
    __snake_case      : Tuple						=				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      ,   )
    __snake_case      : Any						=				predict_dataset.features["label"].names

  # Labels
  __snake_case      : Any						=				len(_A )

  # Load pretrained model and tokenizer
  # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  __snake_case      : Union[str, Any]						=				AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,   num_labels=_A      ,   idalabel={str(_A ): label for i, label in enumerate(_A )}      ,   labelaid={label: i for i, label in enumerate(_A )}      ,   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      ,   )
  __snake_case      : Union[str, Any]						=				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      ,   )
  __snake_case      : Union[str, Any]						=				AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path      ,   from_tf=bool(".ckpt" in model_args.model_name_or_path )      ,   config=_A      ,   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:
    __snake_case      : List[str]						=				"max_length"
  else:
    # We will pad later, dynamically at batch creation, to the max sequence length in each batch
    __snake_case      : Tuple						=				False

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

  if training_args.do_train:
    if data_args.max_train_samples is not None:
      __snake_case      : Optional[Any]						=				min(len(_A )      ,   data_args.max_train_samples )
      __snake_case      : List[Any]						=				train_dataset.select(range(_A ) )
    with training_args.main_process_first(desc="train dataset map pre-processing" ):
      __snake_case      : int						=				train_dataset.map(
          _A      ,   batched=_A      ,   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(_A ) )      ,   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:
      __snake_case      : str						=				min(len(_A )      ,   data_args.max_eval_samples )
      __snake_case      : int						=				eval_dataset.select(range(_A ) )
    with training_args.main_process_first(desc="validation dataset map pre-processing" ):
      __snake_case      : Union[str, Any]						=				eval_dataset.map(
          _A      ,   batched=_A      ,   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:
      __snake_case      : List[str]						=				min(len(_A )      ,   data_args.max_predict_samples )
      __snake_case      : Dict						=				predict_dataset.select(range(_A ) )
    with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
      __snake_case      : Any						=				predict_dataset.map(
          _A      ,   batched=_A      ,   load_from_cache_file=not data_args.overwrite_cache      ,   desc="Running tokenizer on prediction dataset"      ,   )

    # Get the metric function
  __snake_case      : Optional[Any]						=				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(lowercase				:			EvalPrediction ):
    __snake_case      : List[Any]						=				p.predictions[0] if isinstance(p.predictions      ,   _A ) else p.predictions
    __snake_case      : int						=				np.argmax(_A      ,   axis=1 )
    return metric.compute(predictions=_A      ,   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:
    __snake_case      : str						=				default_data_collator
  elif training_args.fpaa:
    __snake_case      : Optional[int]						=				DataCollatorWithPadding(_A      ,   pad_to_multiple_of=8 )
  else:
    __snake_case      : str						=				None

  # Initialize our Trainer
  __snake_case      : int						=				Trainer(
      model=_A      ,   args=_A      ,   train_dataset=train_dataset if training_args.do_train else None      ,   eval_dataset=eval_dataset if training_args.do_eval else None      ,   compute_metrics=_A      ,   tokenizer=_A      ,   data_collator=_A      ,   )

  # Training
  if training_args.do_train:
    __snake_case      : Dict						=				None
    if training_args.resume_from_checkpoint is not None:
      __snake_case      : Tuple						=				training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      __snake_case      : Tuple						=				last_checkpoint
    __snake_case      : int						=				trainer.train(resume_from_checkpoint=_A )
    __snake_case      : int						=				train_result.metrics
    __snake_case      : List[Any]						=				(
        data_args.max_train_samples if data_args.max_train_samples is not None else len(_A )
    )
    __snake_case      : Tuple						=				min(_A      ,   len(_A ) )

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

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

  # Evaluation
  if training_args.do_eval:
    logger.info("*** Evaluate ***" )
    __snake_case      : Optional[Any]						=				trainer.evaluate(eval_dataset=_A )

    __snake_case      : Union[str, Any]						=				data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_A )
    __snake_case      : Dict						=				min(_A      ,   len(_A ) )

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

  # Prediction
  if training_args.do_predict:
    logger.info("*** Predict ***" )
    __snake_case      : str						=				trainer.predict(_A      ,   metric_key_prefix="predict" )

    __snake_case      : Tuple						=				(
        data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_A )
    )
    __snake_case      : str						=				min(_A      ,   len(_A ) )

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

    __snake_case      : Optional[int]						=				np.argmax(_A      ,   axis=1 )
    __snake_case      : str						=				os.path.join(training_args.output_dir      ,   "predictions.txt" )
    if trainer.is_world_process_zero():
      with open(_A      ,   "w" ) as writer:
        writer.write("index\tprediction\n" )
        for index, item in enumerate(_A ):
          __snake_case      : List[str]						=				label_list[item]
          writer.write(f"""{index}\t{item}\n""" )


if __name__ == "__main__":
     main()





def       lowercase_		(		_A			:    int    ,					_A			:    int						):

      """simple docstring"""




      while a != 0:
            lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  b % a, a
      return b


def       lowercase_		(		_A			:    int    ,					_A			:    int						):

      """simple docstring"""




      if gcd(_A    ,					_A						) != 1:
            lowerCamelCase__		:				List[str]    =  F"mod inverse of {a!r} and {m!r} does not exist"
            raise ValueError(_A						)
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  1, 0, a
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  0, 1, m
      while va != 0:
            lowerCamelCase__		:				Tuple    =  ua // va
            lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				str    =  (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
      return ua % m


