


import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    _lowercase			:				Optional[int] 						=						fname.split(os.path.sep					)[-1]
    return re.search(R'^(.*)_\d+\.jpg$'				,					lowerCamelCase_					).groups()[0]






class _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None)			->  List[str]:




           """simple docstring"""

           _lowercase			:				int 						=						file_names
           _lowercase			:				List[str] 						=						image_transform
           _lowercase			:				List[Any] 						=						label_to_id
       def __len__(   self)			->  str:




           """simple docstring"""

           return len(self.file_names)




       def __getitem__(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.file_names[idx]
           _lowercase			:				Tuple 						=						PIL.Image.open(lowerCamelCase)
           _lowercase			:				Any 						=						raw_image.convert('RGB')
           if self.image_transform is not None:
               _lowercase			:				Dict 						=						self.image_transform(lowerCamelCase)
           _lowercase			:				Dict 						=						extract_label(lowerCamelCase)
           if self.label_to_id is not None:
               _lowercase			:				List[Any] 						=						self.label_to_id[label]
           return {"image": image, "label": label}



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Union[str, Any]:
    # Initialize accelerator
    if args.with_tracking:
        _lowercase			:				int 						=						Accelerator(
            cpu=args.cpu				,					mixed_precision=args.mixed_precision				,					log_with='all'				,					project_dir=args.project_dir					)
    else:
        _lowercase			:				Tuple 						=						Accelerator(cpu=args.cpu				,					mixed_precision=args.mixed_precision					)

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase			:				List[str] 						=						config['lr']
    _lowercase			:				Optional[int] 						=						int(config['num_epochs']					)
    _lowercase			:				Union[str, Any] 						=						int(config['seed']					)
    _lowercase			:				Dict 						=						int(config['batch_size']					)
    _lowercase			:				List[str] 						=						config['image_size']
    if not isinstance(lowerCamelCase_				,					(list, tuple)					):
        _lowercase			:				List[str] 						=						(image_size, image_size)

    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps				,					'isdigit'					):
        if args.checkpointing_steps == "epoch":
            _lowercase			:				Optional[Any] 						=						args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            _lowercase			:				int 						=						int(args.checkpointing_steps					)
        else:
            raise ValueError(
                F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
    else:
        _lowercase			:				Union[str, Any] 						=						None

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        _lowercase			:				Tuple 						=						os.path.split(lowerCamelCase_					)[-1].split('.'					)[0]
        accelerator.init_trackers(lowerCamelCase_				,					lowerCamelCase_					)

    # Grab all the image filenames
    _lowercase			:				str 						=						[os.path.join(args.data_dir				,					lowerCamelCase_					) for fname in os.listdir(args.data_dir					) if fname.endswith('.jpg'					)]

    # Build the label correspondences
    _lowercase			:				List[str] 						=						[extract_label(lowerCamelCase_					) for fname in file_names]
    _lowercase			:				Dict 						=						list(set(lowerCamelCase_					)					)
    id_to_label.sort()
    _lowercase			:				Optional[int] 						=						{lbl: i for i, lbl in enumerate(lowerCamelCase_					)}

    # Set the seed before splitting the data.
    np.random.seed(lowerCamelCase_					)
    torch.manual_seed(lowerCamelCase_					)
    torch.cuda.manual_seed_all(lowerCamelCase_					)

    # Split our filenames between train and validation
    _lowercase			:				Optional[Any] 						=						np.random.permutation(len(lowerCamelCase_					)					)
    _lowercase			:				str 						=						int(0.8 * len(lowerCamelCase_					)					)
    _lowercase			:				Any 						=						random_perm[:cut]
    _lowercase			:				Optional[int] 						=						random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    _lowercase			:				Optional[int] 						=						Compose([RandomResizedCrop(lowerCamelCase_				,					scale=(0.5, 1.0)					), ToTensor()]					)
    _lowercase			:				Optional[Any] 						=						PetsDataset(
        [file_names[i] for i in train_split]				,					image_transform=lowerCamelCase_				,					label_to_id=lowerCamelCase_					)

    # For evaluation, we use a deterministic Resize
    _lowercase			:				Union[str, Any] 						=						Compose([Resize(lowerCamelCase_					), ToTensor()]					)
    _lowercase			:				Optional[Any] 						=						PetsDataset([file_names[i] for i in eval_split]				,					image_transform=lowerCamelCase_				,					label_to_id=lowerCamelCase_					)

    # Instantiate dataloaders.
    _lowercase			:				List[str] 						=						DataLoader(lowerCamelCase_				,					shuffle=lowerCamelCase_				,					batch_size=lowerCamelCase_				,					num_workers=4					)
    _lowercase			:				List[Any] 						=						DataLoader(lowerCamelCase_				,					shuffle=lowerCamelCase_				,					batch_size=lowerCamelCase_				,					num_workers=4					)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase			:				List[str] 						=						create_model('resnet50d'				,					pretrained=lowerCamelCase_				,					num_classes=len(lowerCamelCase_					)					)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    _lowercase			:				Union[str, Any] 						=						model.to(accelerator.device					)

    # Freezing the base model
    for param in model.parameters():
        _lowercase			:				Dict 						=						False
    for param in model.get_classifier().parameters():
        _lowercase			:				Any 						=						True

    # We normalize the batches of images to be a bit faster.
    _lowercase			:				int 						=						torch.tensor(model.default_cfg['mean']					)[None, :, None, None].to(accelerator.device					)
    _lowercase			:				Union[str, Any] 						=						torch.tensor(model.default_cfg['std']					)[None, :, None, None].to(accelerator.device					)

    # Instantiate optimizer
    _lowercase			:				int 						=						torch.optim.Adam(params=model.parameters()				,					lr=lr / 25					)

    # Instantiate learning rate scheduler
    _lowercase			:				List[Any] 						=						OneCycleLR(optimizer=lowerCamelCase_				,					max_lr=lowerCamelCase_				,					epochs=lowerCamelCase_				,					steps_per_epoch=len(lowerCamelCase_					)					)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    _lowercase						,							_lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						accelerator.prepare(
        lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    # We need to keep track of how many total steps we have iterated over
    _lowercase			:				Optional[int] 						=						0
    # We also need to keep track of the starting epoch so files are named properly
    _lowercase			:				List[Any] 						=						0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''					)
            accelerator.load_state(args.resume_from_checkpoint					)
            _lowercase			:				Optional[Any] 						=						os.path.basename(args.resume_from_checkpoint					)
        else:
            # Get the most recent checkpoint
            _lowercase			:				List[str] 						=						[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
            dirs.sort(key=os.path.getctime					)
            _lowercase			:				str 						=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        _lowercase			:				Union[str, Any] 						=						os.path.splitext(lowerCamelCase_					)[0]

        if "epoch" in training_difference:
            _lowercase			:				Any 						=						int(training_difference.replace('epoch_'				,					''					)					) + 1
            _lowercase			:				Optional[Any] 						=						None
        else:
            _lowercase			:				Dict 						=						int(training_difference.replace('step_'				,					''					)					)
            _lowercase			:				Any 						=						resume_step // len(lowerCamelCase_					)
            resume_step -= starting_epoch * len(lowerCamelCase_					)

    # Now we train the model
    for epoch in range(lowerCamelCase_				,					lowerCamelCase_					):
        model.train()
        if args.with_tracking:
            _lowercase			:				Tuple 						=						0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            _lowercase			:				Union[str, Any] 						=						accelerator.skip_first_batches(lowerCamelCase_				,					lowerCamelCase_					)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            _lowercase			:				Any 						=						train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase			:				Dict 						=						{k: v.to(accelerator.device					) for k, v in batch.items()}
            _lowercase			:				List[Any] 						=						(batch['image'] - mean) / std
            _lowercase			:				Optional[int] 						=						model(lowerCamelCase_					)
            _lowercase			:				Union[str, Any] 						=						torch.nn.functional.cross_entropy(lowerCamelCase_				,					batch['label']					)
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(lowerCamelCase_					)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(lowerCamelCase_				,					lowerCamelCase_					):
                _lowercase			:				Tuple 						=						F'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        _lowercase			:				Tuple 						=						os.path.join(args.output_dir				,					lowerCamelCase_					)
                    accelerator.save_state(lowerCamelCase_					)
        model.eval()
        _lowercase			:				List[str] 						=						0
        _lowercase			:				List[Any] 						=						0
        for step, batch in enumerate(lowerCamelCase_					):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase			:				Union[str, Any] 						=						{k: v.to(accelerator.device					) for k, v in batch.items()}
            _lowercase			:				List[Any] 						=						(batch['image'] - mean) / std
            with torch.no_grad():
                _lowercase			:				Optional[Any] 						=						model(lowerCamelCase_					)
            _lowercase			:				Any 						=						outputs.argmax(dim=-1					)
            _lowercase						,							_lowercase			:				Optional[int] 						=						accelerator.gather_for_metrics((predictions, batch['label'])					)
            _lowercase			:				int 						=						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        _lowercase			:				Union[str, Any] 						=						accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''					)
        if args.with_tracking:
            accelerator.log(
                {
                    'accuracy': 100 * eval_metric,
                    'train_loss': total_loss.item() / len(lowerCamelCase_					),
                    'epoch': epoch,
                }				,					step=lowerCamelCase_				,					)
        if checkpointing_steps == "epoch":
            _lowercase			:				Any 						=						F'''epoch_{epoch}'''
            if args.output_dir is not None:
                _lowercase			:				Dict 						=						os.path.join(args.output_dir				,					lowerCamelCase_					)
            accelerator.save_state(lowerCamelCase_					)

    if args.with_tracking:
        accelerator.end_training()



def 							UpperCamelCase_( )       ->		Optional[Any]:
    _lowercase			:				Tuple 						=						argparse.ArgumentParser(description='Simple example of training script.'					)
    parser.add_argument('--data_dir'				,					required=lowerCamelCase_				,					help='The data folder on disk.'					)
    parser.add_argument('--fp16'				,					action='store_true'				,					help='If passed, will use FP16 training.'					)
    parser.add_argument(
        '--mixed_precision'				,					type=lowerCamelCase_				,					default=lowerCamelCase_				,					choices=['no', 'fp16', 'bf16', 'fp8']				,					help='Whether to use mixed precision. Choose'
        'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
        'and an Nvidia Ampere GPU.'				,					)
    parser.add_argument('--cpu'				,					action='store_true'				,					help='If passed, will train on the CPU.'					)
    parser.add_argument(
        '--checkpointing_steps'				,					type=lowerCamelCase_				,					default=lowerCamelCase_				,					help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'				,					)
    parser.add_argument(
        '--output_dir'				,					type=lowerCamelCase_				,					default='.'				,					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'				,					)
    parser.add_argument(
        '--resume_from_checkpoint'				,					type=lowerCamelCase_				,					default=lowerCamelCase_				,					help='If the training should continue from a checkpoint folder.'				,					)
    parser.add_argument(
        '--with_tracking'				,					action='store_true'				,					help='Whether to load in all available experiment trackers from the environment and use them for logging.'				,					)
    parser.add_argument(
        '--project_dir'				,					type=lowerCamelCase_				,					default='logs'				,					help='Location on where to store experiment tracking logs` and relevent project information'				,					)
    _lowercase			:				str 						=						parser.parse_args()
    _lowercase			:				List[str] 						=						{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
    training_function(lowerCamelCase_				,					lowerCamelCase_					)


if __name__ == "__main__":
 main()






import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
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("4.17.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

SCREAMING_SNAKE_CASE			: Dict				   =					logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_					:      Optional[str]       					=     field(
           default="""tab_fact""",					metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
       lowercase_					:      Optional[str]       					=     field(
           default="""tab_fact""",					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""},					)
       lowercase_					:      int       					=     field(
           default=10_24,					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."""
               )
           },					)
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """A csv or a json file containing the training data."""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """A csv or a json file containing the validation data."""}  )
       lowercase_					:      Optional[str]       					=     field(default=_a,					metadata={"""help""": """A csv or a json file containing the test data."""}  )
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           if self.dataset_name is not None:
               pass
           elif self.train_file is None or self.validation_file is None:
               raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
           else:
               _lowercase			:				int 						=						self.train_file.split('.')[-1]
               assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
               _lowercase			:				Tuple 						=						self.validation_file.split('.')[-1]
               assert (
                   validation_extension == train_extension
               ), "`validation_file` should have the same extension (csv or json) as `train_file`."






@dataclass
class _lowerCamelCase:
       lowercase_					:      str       					=     field(
           default=_a,					metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
       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_					:      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)."""
               )
           },					)



def 							UpperCamelCase_( )       ->		Optional[int]:
    # 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			:				Optional[int] 						=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
    if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        _lowercase						,							_lowercase						,							_lowercase			:				Tuple 						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
    else:
        _lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						parser.parse_args_into_dataclasses()

    # 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					)]				,					)

    _lowercase			:				Union[str, 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			:				Optional[int] 						=						None
    if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
        _lowercase			:				Dict 						=						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 and training_args.resume_from_checkpoint is 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					)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        _lowercase			:				Dict 						=						load_dataset(
            data_args.dataset_name				,					data_args.dataset_config_name				,					cache_dir=model_args.cache_dir					)
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        _lowercase			:				Optional[Any] 						=						{'train': data_args.train_file, 'validation': data_args.validation_file}

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                _lowercase			:				Tuple 						=						data_args.train_file.split('.'					)[-1]
                _lowercase			:				int 						=						data_args.test_file.split('.'					)[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                _lowercase			:				Any 						=						data_args.test_file
            else:
                raise ValueError('Need either a GLUE task or a test file for `do_predict`.'					)

        for key in data_files.keys():
            logger.info(F'''load a local file for {key}: {data_files[key]}'''					)

        if data_args.train_file.endswith('.csv'					):
            # Loading a dataset from local csv files
            _lowercase			:				str 						=						load_dataset('csv'				,					data_files=lowerCamelCase_				,					cache_dir=model_args.cache_dir					)
        else:
            # Loading a dataset from local json files
            _lowercase			:				Optional[int] 						=						load_dataset('json'				,					data_files=lowerCamelCase_				,					cache_dir=model_args.cache_dir					)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    _lowercase			:				Optional[Any] 						=						raw_datasets['train'].features['label'].names
    _lowercase			:				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.
    _lowercase			:				List[Any] 						=						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					num_labels=lowerCamelCase_				,					cache_dir=model_args.cache_dir				,					revision=model_args.model_revision				,					use_auth_token=True if model_args.use_auth_token else None				,					)
    # load tapex tokenizer
    _lowercase			:				str 						=						TapexTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,					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				,					add_prefix_space=lowerCamelCase_				,					)
    _lowercase			:				Tuple 						=						BartForSequenceClassification.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				,					)

    # Padding strategy
    if data_args.pad_to_max_length:
        _lowercase			:				int 						=						'max_length'
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        _lowercase			:				str 						=						False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    _lowercase			:				List[Any] 						=						{'Refused': 0, 'Entailed': 1}
    _lowercase			:				Union[str, Any] 						=						{0: 'Refused', 1: 'Entailed'}

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
            F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''					)
    _lowercase			:				List[str] 						=						min(data_args.max_seq_length				,					tokenizer.model_max_length					)

    def preprocess_tabfact_function(lowerCamelCase_					):
        # Tokenize the texts
        def _convert_table_text_to_pandas(lowerCamelCase_					):
            _lowercase			:				int 						=						[_table_row.split('#'					) for _table_row in _table_text.strip('\n'					).split('\n'					)]
            _lowercase			:				Any 						=						pd.DataFrame.from_records(_table_content[1:]				,					columns=_table_content[0]					)
            return _table_pd

        _lowercase			:				List[Any] 						=						examples['statement']
        _lowercase			:				Optional[Any] 						=						list(map(_convert_table_text_to_pandas				,					examples['table_text']					)					)
        _lowercase			:				Union[str, Any] 						=						tokenizer(lowerCamelCase_				,					lowerCamelCase_				,					padding=lowerCamelCase_				,					max_length=lowerCamelCase_				,					truncation=lowerCamelCase_					)

        _lowercase			:				Any 						=						examples['label']
        return result

    with training_args.main_process_first(desc='dataset map pre-processing'					):
        _lowercase			:				str 						=						raw_datasets.map(
            lowerCamelCase_				,					batched=lowerCamelCase_				,					load_from_cache_file=not data_args.overwrite_cache				,					desc='Running tokenizer on dataset'				,					)
    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError('--do_train requires a train dataset'					)
        _lowercase			:				Any 						=						raw_datasets['train']
        if data_args.max_train_samples is not None:
            _lowercase			:				str 						=						train_dataset.select(range(data_args.max_train_samples					)					)

    if training_args.do_eval:
        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
            raise ValueError('--do_eval requires a validation dataset'					)
        _lowercase			:				str 						=						raw_datasets['validation']
        if data_args.max_eval_samples is not None:
            _lowercase			:				List[Any] 						=						eval_dataset.select(range(data_args.max_eval_samples					)					)

    if training_args.do_predict or data_args.test_file is not None:
        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
            raise ValueError('--do_predict requires a test dataset'					)
        _lowercase			:				Optional[int] 						=						raw_datasets['test']
        if data_args.max_predict_samples is not None:
            _lowercase			:				List[str] 						=						predict_dataset.select(range(data_args.max_predict_samples					)					)

    # Log a few random samples from the training set:
    if training_args.do_train:
        for index in random.sample(range(len(lowerCamelCase_					)					)				,					3					):
            logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''					)

    # 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			:				Dict 						=						p.predictions[0] if isinstance(p.predictions				,					lowerCamelCase_					) else p.predictions
        _lowercase			:				Tuple 						=						np.argmax(lowerCamelCase_				,					axis=1					)
        return {"accuracy": (preds == p.label_ids).astype(np.floataa					).mean().item()}

    # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
    if data_args.pad_to_max_length:
        _lowercase			:				Any 						=						default_data_collator
    elif training_args.fpaa:
        _lowercase			:				str 						=						DataCollatorWithPadding(lowerCamelCase_				,					pad_to_multiple_of=8					)
    else:
        _lowercase			:				Optional[Any] 						=						None

    # Initialize our Trainer
    _lowercase			:				List[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			:				Optional[int] 						=						None
        if training_args.resume_from_checkpoint is not None:
            _lowercase			:				List[Any] 						=						training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            _lowercase			:				Optional[Any] 						=						last_checkpoint
        _lowercase			:				Optional[Any] 						=						trainer.train(resume_from_checkpoint=lowerCamelCase_					)
        _lowercase			:				List[Any] 						=						train_result.metrics
        _lowercase			:				Dict 						=						(
            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			:				Tuple 						=						trainer.evaluate(eval_dataset=lowerCamelCase_					)
        _lowercase			:				Any 						=						data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						min(lowerCamelCase_				,					len(lowerCamelCase_					)					)

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

    if training_args.do_predict:
        logger.info('*** Predict ***'					)

        # Removing the `label` columns because it contains -1 and Trainer won't like that.
        _lowercase			:				Any 						=						predict_dataset.remove_columns('label'					)
        _lowercase			:				Optional[Any] 						=						trainer.predict(lowerCamelCase_				,					metric_key_prefix='predict'					).predictions
        _lowercase			:				Union[str, Any] 						=						np.argmax(lowerCamelCase_				,					axis=1					)

        _lowercase			:				Dict 						=						os.path.join(training_args.output_dir				,					'predict_results_tabfact.txt'					)
        if trainer.is_world_process_zero():
            with open(lowerCamelCase_				,					'w'					) as writer:
                logger.info('***** Predict Results *****'					)
                writer.write('index\tprediction\n'					)
                for index, item in enumerate(lowerCamelCase_					):
                    _lowercase			:				List[str] 						=						label_list[item]
                    writer.write(F'''{index}\t{item}\n'''					)

    _lowercase			:				str 						=						{'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}

    if training_args.push_to_hub:
        trainer.push_to_hub(**lowerCamelCase_					)
    else:
        trainer.create_model_card(**lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
 main()


