


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")

A      :    Tuple		=					logging.getLogger(__name__)







@dataclass
class 			_lowercase :


       """simple docstring"""




       A__											=			field(
           default=1_28							,  metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }							,  )
       A__											=			field(
           default=lowercase__							,  metadata={"help": "Overwrite the cached preprocessed datasets or not."})
       A__											=			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."
               )
           }							,  )
       A__											=			field(
           default=lowercase__							,  metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           }							,  )
       A__											=			field(
           default=lowercase__							,  metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                   "value if set."
               )
           }							,  )
       A__											=			field(
           default=lowercase__							,  metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                   "value if set."
               )
           }							,  )







@dataclass
class 			_lowercase :


       """simple docstring"""




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



def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[int]    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				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()

      lowerCamelCase__		:				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.
      lowerCamelCase__		:				str    =  None
      if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
            lowerCamelCase__		:				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:
                  lowerCamelCase__		:				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:
                  lowerCamelCase__		:				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    ,					)
            lowerCamelCase__		:				List[Any]    =  train_dataset.features["label"].names

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

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

      # Labels
      lowerCamelCase__		:				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.
      lowerCamelCase__		:				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    ,					)
      lowerCamelCase__		:				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    ,					)
      lowerCamelCase__		:				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:
            lowerCamelCase__		:				List[str]    =  "max_length"
      else:
            # We will pad later, dynamically at batch creation, to the max sequence length in each batch
            lowerCamelCase__		:				Tuple    =  False

      def preprocess_function(_A			:    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:
                  lowerCamelCase__		:				Optional[Any]    =  min(len(_A						)    ,					data_args.max_train_samples						)
                  lowerCamelCase__		:				List[Any]    =  train_dataset.select(range(_A						)						)
            with training_args.main_process_first(desc="train dataset map pre-processing"						):
                  lowerCamelCase__		:				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:
                  lowerCamelCase__		:				str    =  min(len(_A						)    ,					data_args.max_eval_samples						)
                  lowerCamelCase__		:				int    =  eval_dataset.select(range(_A						)						)
            with training_args.main_process_first(desc="validation dataset map pre-processing"						):
                  lowerCamelCase__		:				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:
                  lowerCamelCase__		:				List[str]    =  min(len(_A						)    ,					data_args.max_predict_samples						)
                  lowerCamelCase__		:				Dict    =  predict_dataset.select(range(_A						)						)
            with training_args.main_process_first(desc="prediction dataset map pre-processing"						):
                  lowerCamelCase__		:				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
      lowerCamelCase__		:				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(_A			:    EvalPrediction						):
            lowerCamelCase__		:				List[Any]    =  p.predictions[0] if isinstance(p.predictions    ,					_A						) else p.predictions
            lowerCamelCase__		:				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:
            lowerCamelCase__		:				str    =  default_data_collator
      elif training_args.fpaa:
            lowerCamelCase__		:				Optional[int]    =  DataCollatorWithPadding(_A    ,					pad_to_multiple_of=8						)
      else:
            lowerCamelCase__		:				str    =  None

      # Initialize our Trainer
      lowerCamelCase__		:				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:
            lowerCamelCase__		:				Dict    =  None
            if training_args.resume_from_checkpoint is not None:
                  lowerCamelCase__		:				Tuple    =  training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowerCamelCase__		:				Tuple    =  last_checkpoint
            lowerCamelCase__		:				int    =  trainer.train(resume_from_checkpoint=_A						)
            lowerCamelCase__		:				int    =  train_result.metrics
            lowerCamelCase__		:				List[Any]    =  (
                data_args.max_train_samples if data_args.max_train_samples is not None else len(_A						)
            )
            lowerCamelCase__		:				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 ***"						)
            lowerCamelCase__		:				Optional[Any]    =  trainer.evaluate(eval_dataset=_A						)

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

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

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

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

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

            lowerCamelCase__		:				Optional[int]    =  np.argmax(_A    ,					axis=1						)
            lowerCamelCase__		:				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						):
                              lowerCamelCase__		:				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


