



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")

_snake_case		=  logging.getLogger(__name__)



@dataclass
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		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__   			=		field(
							    default=a   ,					metadata={'help': 'Overwrite the cached preprocessed datasets or not.'})
							lowerCamelCase__   			=		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__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
							            'value if set.'
							        )
							    }   ,					)



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






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							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"				, _lowerCamelCase  )

							# 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     :							str    =    training_args.get_process_log_level()
							logger.setLevel(_lowerCamelCase  )
							datasets.utils.logging.set_verbosity(_lowerCamelCase  )
							transformers.utils.logging.set_verbosity(_lowerCamelCase  )
							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     :							Optional[Any]    =    None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														_lowerCAmelCase     :							Tuple    =    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     :							List[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:
																					_lowerCAmelCase     :							Union[str, Any]    =    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     :							Optional[Any]    =    train_dataset.features["label"].names

							if training_args.do_eval:
														_lowerCAmelCase     :							int    =    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     :							Dict    =    eval_dataset.features["label"].names

							if training_args.do_predict:
														_lowerCAmelCase     :							Optional[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				, )
														_lowerCAmelCase     :							List[str]    =    predict_dataset.features["label"].names

							# Labels
							_lowerCAmelCase     :							List[Any]    =    len(_lowerCamelCase  )

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

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

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

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

							# Initialize our Trainer
							_lowerCAmelCase     :							Dict    =    Trainer(
							    model=_lowerCamelCase				, args=_lowerCamelCase				, train_dataset=train_dataset if training_args.do_train else None				, eval_dataset=eval_dataset if training_args.do_eval else None				, compute_metrics=_lowerCamelCase				, tokenizer=_lowerCamelCase				, data_collator=_lowerCamelCase				, )

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

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

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

							# Evaluation
							if training_args.do_eval:
														logger.info("*** Evaluate ***"  )
														_lowerCAmelCase     :							Optional[int]    =    trainer.evaluate(eval_dataset=_lowerCamelCase  )

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

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

							# Prediction
							if training_args.do_predict:
														logger.info("*** Predict ***"  )
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    trainer.predict(_lowerCamelCase				, metric_key_prefix="predict"  )

														_lowerCAmelCase     :							str    =    (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_lowerCamelCase  )
														)
														_lowerCAmelCase     :							int    =    min(_lowerCamelCase				, len(_lowerCamelCase  )  )

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

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


if __name__ == "__main__":
						main()




'''simple docstring'''




class 		UpperCAmelCase_      :





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      list[int]    )				->  None:
        lowerCAmelCase     =			len(UpperCAmelCase__    )
        lowerCAmelCase     =			[0] * len_array

        if len_array > 0:
           lowerCAmelCase     =			array[0]

        for i in range(1    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			self.prefix_sum[i - 1] + array[i]





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  int:
        if start == 0:
           return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      int    )				->  bool:
        lowerCAmelCase     =			{0}
        for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
              return True

           sums.add(UpperCAmelCase__    )

        return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()





