


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')

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







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

       """simple docstring"""

       a_      :	Tuple							=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
       a_							,	a_							,	a_      :	Union[str, 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'						,					__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()

       a_      :	int							=						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.
       a_      :	Optional[int]							=						None
       if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
              a_      :	Optional[int]							=						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:
                     a_      :	Dict							=						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:
                     a_      :	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						,					)
              a_      :	Union[str, Any]							=						train_dataset.features['label'].names

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

       if training_args.do_predict:
              a_      :	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						,					)
              a_      :	Optional[int]							=						predict_dataset.features['label'].names

       # Labels
       a_      :	List[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.
       a_      :	Tuple							=						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						,					)
       a_      :	List[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						,					)
       a_      :	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:
              a_      :	int							=						'max_length'
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              a_      :	Any							=						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:
                     a_      :	List[str]							=						min(len(__A       )						,					data_args.max_train_samples       )
                     a_      :	Tuple							=						train_dataset.select(range(__A       )       )
              with training_args.main_process_first(desc='train dataset map pre-processing'       ):
                     a_      :	Optional[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:
                     a_      :	Optional[Any]							=						min(len(__A       )						,					data_args.max_eval_samples       )
                     a_      :	Any							=						eval_dataset.select(range(__A       )       )
              with training_args.main_process_first(desc='validation dataset map pre-processing'       ):
                     a_      :	Optional[int]							=						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:
                     a_      :	Optional[int]							=						min(len(__A       )						,					data_args.max_predict_samples       )
                     a_      :	Any							=						predict_dataset.select(range(__A       )       )
              with training_args.main_process_first(desc='prediction dataset map pre-processing'       ):
                     a_      :	Tuple							=						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
       a_      :	Optional[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(__A				:    EvalPrediction       ):
              a_      :	Tuple							=						p.predictions[0] if isinstance(p.predictions						,					__A       ) else p.predictions
              a_      :	Optional[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:
              a_      :	Optional[int]							=						default_data_collator
       elif training_args.fpaa:
              a_      :	Union[str, Any]							=						DataCollatorWithPadding(__A						,					pad_to_multiple_of=8       )
       else:
              a_      :	Optional[int]							=						None

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

              a_      :	Tuple							=						data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A       )
              a_      :	List[str]							=						min(__A						,					len(__A       )       )

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

       # Prediction
       if training_args.do_predict:
              logger.info('*** Predict ***'       )
              a_							,	a_							,	a_      :	Dict							=						trainer.predict(__A						,					metric_key_prefix='predict'       )

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

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

              a_      :	str							=						np.argmax(__A						,					axis=1       )
              a_      :	Optional[int]							=						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       ):
                                   a_      :	Optional[Any]							=						label_list[item]
                                   writer.write(F"""{index}\t{item}\n"""       )


if __name__ == "__main__":
   main()






def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	bool:

       """simple docstring"""

       a_      :	List[Any]							=						n ** (1 / 3)
       return (val * val * val) == n


if __name__ == "__main__":
   print(perfect_cube(27))
   print(perfect_cube(4))


