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')

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






@dataclass
class 		_lowerCamelCase		:
    __a  =      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."
            )
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Overwrite the cached preprocessed datasets or not."}    )
    __a  =      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."
            )
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }    ,     )
    __a  =      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		:
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Train language if it is different from the evaluation language."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     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=UpperCamelCase_    ,     metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}    ,     )







def 			A__      (	):
 # 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.

 SCREAMING_SNAKE_CASE__:   Tuple=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Any=      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'''       ,				snake_case_      )

 # 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()

 SCREAMING_SNAKE_CASE__:   int=      training_args.get_process_log_level()
 logger.setLevel(snake_case_      )
 datasets.utils.logging.set_verbosity(snake_case_      )
 transformers.utils.logging.set_verbosity(snake_case_      )
 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.
 SCREAMING_SNAKE_CASE__:   Any=      None
 if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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:
   SCREAMING_SNAKE_CASE__:   Optional[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:
   SCREAMING_SNAKE_CASE__:   str=      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       ,				)
  SCREAMING_SNAKE_CASE__:   int=      train_dataset.features['''label'''].names

 if training_args.do_eval:
  SCREAMING_SNAKE_CASE__:   List[str]=      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       ,				)
  SCREAMING_SNAKE_CASE__:   Optional[int]=      eval_dataset.features['''label'''].names

 if training_args.do_predict:
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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       ,				)
  SCREAMING_SNAKE_CASE__:   str=      predict_dataset.features['''label'''].names

 # Labels
 SCREAMING_SNAKE_CASE__:   List[Any]=      len(snake_case_      )

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

 def preprocess_function(snake_case_ :		Union[str, Any]      ):
  # Tokenize the texts
  return tokenizer(
      examples['''premise''']       ,				examples['''hypothesis''']       ,				padding=snake_case_       ,				max_length=data_args.max_seq_length       ,				truncation=snake_case_       ,				)

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

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

 # Initialize our Trainer
 SCREAMING_SNAKE_CASE__:   Any=      Trainer(
     model=snake_case_       ,				args=snake_case_       ,				train_dataset=train_dataset if training_args.do_train else None       ,				eval_dataset=eval_dataset if training_args.do_eval else None       ,				compute_metrics=snake_case_       ,				tokenizer=snake_case_       ,				data_collator=snake_case_       ,				)

 # Training
 if training_args.do_train:
  SCREAMING_SNAKE_CASE__:   List[str]=      None
  if training_args.resume_from_checkpoint is not None:
   SCREAMING_SNAKE_CASE__:   List[str]=      training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   SCREAMING_SNAKE_CASE__:   List[str]=      last_checkpoint
  SCREAMING_SNAKE_CASE__:   List[Any]=      trainer.train(resume_from_checkpoint=snake_case_      )
  SCREAMING_SNAKE_CASE__:   int=      train_result.metrics
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (
      data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case_      )
  )
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      min(snake_case_       ,				len(snake_case_      )      )

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

  trainer.log_metrics('''train'''       ,				snake_case_      )
  trainer.save_metrics('''train'''       ,				snake_case_      )
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  logger.info('''*** Evaluate ***'''      )
  SCREAMING_SNAKE_CASE__:   List[Any]=      trainer.evaluate(eval_dataset=snake_case_      )

  SCREAMING_SNAKE_CASE__:   List[Any]=      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case_      )
  SCREAMING_SNAKE_CASE__:   Optional[int]=      min(snake_case_       ,				len(snake_case_      )      )

  trainer.log_metrics('''eval'''       ,				snake_case_      )
  trainer.save_metrics('''eval'''       ,				snake_case_      )

 # Prediction
 if training_args.do_predict:
  logger.info('''*** Predict ***'''      )
  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      trainer.predict(snake_case_       ,				metric_key_prefix='''predict'''      )

  SCREAMING_SNAKE_CASE__:   Optional[int]=      (
      data_args.max_predict_samples if data_args.max_predict_samples is not None else len(snake_case_      )
  )
  SCREAMING_SNAKE_CASE__:   Any=      min(snake_case_       ,				len(snake_case_      )      )

  trainer.log_metrics('''predict'''       ,				snake_case_      )
  trainer.save_metrics('''predict'''       ,				snake_case_      )

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


if __name__ == "__main__":
      main()


def 			A__      (	snake_case_ :		list      ):

 if len(snake_case_      ) < 2:
  return collection

 def circle_sort_util(snake_case_ :		list       ,				snake_case_ :		int       ,				snake_case_ :		int      ) -> bool:
  SCREAMING_SNAKE_CASE__:   Dict=      False

  if low == high:
   return swapped

  SCREAMING_SNAKE_CASE__:   str=      low
  SCREAMING_SNAKE_CASE__:   Optional[int]=      high

  while left < right:
   if collection[left] > collection[right]:
    SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      (
        collection[right],
        collection[left],
    )
    SCREAMING_SNAKE_CASE__:   List[Any]=      True

   left += 1
   right -= 1

  if left == right and collection[left] > collection[right + 1]:
   SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[str]=      (
       collection[right + 1],
       collection[left],
   )

   SCREAMING_SNAKE_CASE__:   List[str]=      True

  SCREAMING_SNAKE_CASE__:   Dict=      low + int((high - low) / 2      )
  SCREAMING_SNAKE_CASE__:   str=      circle_sort_util(snake_case_       ,				snake_case_       ,				snake_case_      )
  SCREAMING_SNAKE_CASE__:   List[Any]=      circle_sort_util(snake_case_       ,				mid + 1       ,				snake_case_      )

  return swapped or left_swap or right_swap

 SCREAMING_SNAKE_CASE__:   Optional[int]=      True

 while is_not_sorted is True:
  SCREAMING_SNAKE_CASE__:   Tuple=      circle_sort_util(snake_case_       ,				0       ,				len(snake_case_      ) - 1      )

 return collection


if __name__ == "__main__":
      lowercase_     :   Tuple 				=				input('Enter numbers separated by a comma:\n').strip()
      lowercase_     :   str 				=				[int(item) for item in user_input.split(',')]
      print(circle_sort(unsorted))

