


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")

SCREAMING_SNAKE_CASE       :      List[str]												= logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_ :	Optional[int]     =			field(
           default=1_28, metadata={
               """help""": (
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )
           }, )
       lowercase_ :	bool     =			field(
           default=_a, metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}   )
       lowercase_ :	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."""
               )
           }, )
       lowercase_ :	Optional[int]     =			field(
           default=_a, metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of training examples to this """
                   """value if set."""
               )
           }, )
       lowercase_ :	Optional[int]     =			field(
           default=_a, metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                   """value if set."""
               )
           }, )
       lowercase_ :	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:
       lowercase_ :	str     =			field(
           default=_a, metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
       lowercase_ :	str     =			field(
           default=_a, metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}   )
       lowercase_ :	Optional[str]     =			field(
           default=_a, metadata={"""help""": """Train language if it is different from the evaluation language."""}   )
       lowercase_ :	Optional[str]     =			field(
           default=_a, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
       lowercase_ :	Optional[str]     =			field(
           default=_a, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
       lowercase_ :	Optional[str]     =			field(
           default=_a, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}, )
       lowercase_ :	Optional[bool]     =			field(
           default=_a, metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}, )
       lowercase_ :	bool     =			field(
           default=_a, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}, )
       lowercase_ :	str     =			field(
           default="""main""", metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}, )
       lowercase_ :	bool     =			field(
           default=_a, metadata={
               """help""": (
                   """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                   """with private models)."""
               )
           }, )
       lowercase_ :	bool     =			field(
           default=_a, metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}, )



def 				UpperCamelCase_(      )    ->						Dict:
    # 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.

    _lowercase       :							Any      		=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
    _lowercase		,				_lowercase		,				_lowercase       :							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()

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

    if training_args.do_eval:
        _lowercase       :							Optional[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							, )
        _lowercase       :							List[Any]      		=		eval_dataset.features['label'].names

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

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

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

        _lowercase       :							Dict      		=		data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_ )
        _lowercase       :							int      		=		min(lowerCamelCase_							, len(lowerCamelCase_ ) )

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

    # Prediction
    if training_args.do_predict:
        logger.info('*** Predict ***' )
        _lowercase		,				_lowercase		,				_lowercase       :							List[Any]      		=		trainer.predict(lowerCamelCase_							, metric_key_prefix='predict' )

        _lowercase       :							Tuple      		=		(
            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase_ )
        )
        _lowercase       :							Union[str, Any]      		=		min(lowerCamelCase_							, len(lowerCamelCase_ ) )

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

        _lowercase       :							List[str]      		=		np.argmax(lowerCamelCase_							, axis=1 )
        _lowercase       :							Optional[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_ ):
                    _lowercase       :							Union[str, Any]      		=		label_list[item]
                    writer.write(F'''{index}\t{item}\n''' )


if __name__ == "__main__":
 main()




"""simple docstring"""



UpperCAmelCase__          = {
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}



