





import operator






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase = False			,    _UpperCamelCase = None			)		->						list:






				"""simple docstring"""


				snake_case_						:   Any           =  operator.lt if reverse else operator.gt
				snake_case_						:   List[Any]           =  solution or []

				if not arr:
								return solution

				snake_case_						:   Optional[int]           =  [arr.pop(0			)]
				for i, item in enumerate(_UpperCamelCase			):
								if _operator(_UpperCamelCase			,    sublist[-1]			):
												sublist.append(_UpperCamelCase			)
												arr.pop(_UpperCamelCase			)

    #  merging sublist into solution list
				if not solution:
								solution.extend(_UpperCamelCase			)
				else:
								while sublist:
												snake_case_						:   List[Any]           =  sublist.pop(0			)
												for i, xx in enumerate(_UpperCamelCase			):
																if not _operator(_UpperCamelCase			,    _UpperCamelCase			):
																				solution.insert(_UpperCamelCase			,    _UpperCamelCase			)
																				break
												else:
																solution.append(_UpperCamelCase			)

				strand_sort(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
				return solution


if __name__ == "__main__":
	assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
	assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]







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_    =    logging.getLogger(__name__)



@dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					Optional[int]	      =				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.'''
			        )
			    },					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					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.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    },					)




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






def 					lowerCamelCase_						(    )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
				snake_case_      ,							snake_case_      ,							snake_case_						:   Tuple           =  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()

				snake_case_						:   List[Any]           =  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.
				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_						:   Optional[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:
												snake_case_						:   Union[str, Any]           =  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_						:   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			,    )
								snake_case_						:   Optional[int]           =  train_dataset.features['''label'''].names

				if training_args.do_eval:
								snake_case_						:   Dict           =  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_						:   Tuple           =  eval_dataset.features['''label'''].names

				if training_args.do_predict:
								snake_case_						:   int           =  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_						:   Optional[int]           =  predict_dataset.features['''label'''].names

				# Labels
				snake_case_						:   int           =  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.
				snake_case_						:   Any           =  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			,    )
				snake_case_						:   Dict           =  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=_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:
								snake_case_						:   Dict           =  '''max_length'''
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								snake_case_						:   str           =  False

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

				# Initialize our Trainer
				snake_case_						:   Any           =  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:
								snake_case_						:   int           =  None
								if training_args.resume_from_checkpoint is not None:
												snake_case_						:   Dict           =  training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case_						:   Dict           =  last_checkpoint
								snake_case_						:   int           =  trainer.train(resume_from_checkpoint=_UpperCamelCase			)
								snake_case_						:   Union[str, Any]           =  train_result.metrics
								snake_case_						:   Union[str, Any]           =  (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCamelCase			)
								)
								snake_case_						:   Dict           =  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 ***'''			)
								snake_case_						:   Any           =  trainer.evaluate(eval_dataset=_UpperCamelCase			)

								snake_case_						:   int           =  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCamelCase			)
								snake_case_						:   str           =  min(_UpperCamelCase			,    len(_UpperCamelCase			)			)

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

				# Prediction
				if training_args.do_predict:
								logger.info('''*** Predict ***'''			)
								snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  trainer.predict(_UpperCamelCase			,    metric_key_prefix='''predict'''			)

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

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

								snake_case_						:   List[Any]           =  np.argmax(_UpperCamelCase			,    axis=1			)
								snake_case_						:   Optional[Any]           =  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			):
																				snake_case_						:   List[str]           =  label_list[item]
																				writer.write(f'''{index}\t{item}\n'''			)


if __name__ == "__main__":
	main()
