






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''')

lowerCamelCase      :       Optional[Any]    =						logging.getLogger(__name__)



@dataclass
class 				_UpperCamelCase      :
      snake_case_	=				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."""
              )
          }				,		)
      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 				_UpperCamelCase      :
      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    __lowerCAmelCase   (    ):
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     __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"       ,			UpperCamelCase__							)

     # 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(UpperCamelCase__							)
     datasets.utils.logging.set_verbosity(UpperCamelCase__							)
     transformers.utils.logging.set_verbosity(UpperCamelCase__							)
     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(UpperCamelCase__							)

     # 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=UpperCamelCase__       ,			idalabel={str(UpperCamelCase__							): label for i, label in enumerate(UpperCamelCase__							)}       ,			labelaid={label: i for i, label in enumerate(UpperCamelCase__							)}       ,			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=UpperCamelCase__       ,			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(__snake_case							):
          # Tokenize the texts
          return tokenizer(
              examples["premise"]       ,			examples["hypothesis"]       ,			padding=UpperCamelCase__       ,			max_length=data_args.max_seq_length       ,			truncation=UpperCamelCase__       ,			)

     if training_args.do_train:
          if data_args.max_train_samples is not None:
               __lowerCAmelCase             =       min(len(UpperCamelCase__							)       ,			data_args.max_train_samples							)
               __lowerCAmelCase             =       train_dataset.select(range(UpperCamelCase__							)							)
          with training_args.main_process_first(desc="train dataset map pre-processing"							):
               __lowerCAmelCase             =       train_dataset.map(
                   UpperCamelCase__       ,			batched=UpperCamelCase__       ,			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(UpperCamelCase__							)							)       ,			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(UpperCamelCase__							)       ,			data_args.max_eval_samples							)
               __lowerCAmelCase             =       eval_dataset.select(range(UpperCamelCase__							)							)
          with training_args.main_process_first(desc="validation dataset map pre-processing"							):
               __lowerCAmelCase             =       eval_dataset.map(
                   UpperCamelCase__       ,			batched=UpperCamelCase__       ,			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(UpperCamelCase__							)       ,			data_args.max_predict_samples							)
               __lowerCAmelCase             =       predict_dataset.select(range(UpperCamelCase__							)							)
          with training_args.main_process_first(desc="prediction dataset map pre-processing"							):
               __lowerCAmelCase             =       predict_dataset.map(
                   UpperCamelCase__       ,			batched=UpperCamelCase__       ,			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(__snake_case							):
          __lowerCAmelCase             =       p.predictions[0] if isinstance(p.predictions       ,			UpperCamelCase__							) else p.predictions
          __lowerCAmelCase             =       np.argmax(UpperCamelCase__       ,			axis=1							)
          return metric.compute(predictions=UpperCamelCase__       ,			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(UpperCamelCase__       ,			pad_to_multiple_of=8							)
     else:
          __lowerCAmelCase             =       None

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

     # 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=UpperCamelCase__							)
          __lowerCAmelCase             =       train_result.metrics
          __lowerCAmelCase             =       (
              data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase__							)
          )
          __lowerCAmelCase             =       min(UpperCamelCase__       ,			len(UpperCamelCase__							)							)

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

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

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

          __lowerCAmelCase             =       data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__							)
          __lowerCAmelCase             =       min(UpperCamelCase__       ,			len(UpperCamelCase__							)							)

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

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

          __lowerCAmelCase             =       (
              data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCamelCase__							)
          )
          __lowerCAmelCase             =       min(UpperCamelCase__       ,			len(UpperCamelCase__							)							)

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

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


if __name__ == "__main__":
 main()













def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int = 600_851_475_143     ):

		try:
				SCREAMING_SNAKE_CASE__								=   int(UpperCamelCase__     )
		except (TypeError, ValueError):
				raise TypeError("""Parameter n must be int or castable to int."""     )
		if n <= 0:
				raise ValueError("""Parameter n must be greater than or equal to one."""     )
		SCREAMING_SNAKE_CASE__								=   1
		SCREAMING_SNAKE_CASE__								=   2
		while i * i <= n:
				while n % i == 0:
						SCREAMING_SNAKE_CASE__								=   i
						n //= i
				i += 1
		if n > 1:
				SCREAMING_SNAKE_CASE__								=   n
		return int(UpperCamelCase__     )


if __name__ == "__main__":
	print(F'''{solution() = }''')