

"""simple docstring"""

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 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Tuple:
 lowerCAmelCase__      :							str    =							fname.split(os.path.sep      )[-1]
 return re.search(r"^(.*)_\d+\.jpg$"				,				lowercase__      ).groups()[0]






class  __a				(	__magic_name__					):





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=None ,				snake_case=None      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							file_names
        lowerCAmelCase__      :							Tuple    =							image_transform
        lowerCAmelCase__      :							Optional[int]    =							label_to_id






       def __len__(      self      ):





        """simple docstring"""

        return len(self.file_names      )






       def __getitem__(      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							self.file_names[idx]
        lowerCAmelCase__      :							List[str]    =							PIL.Image.open(snake_case      )
        lowerCAmelCase__      :							Optional[int]    =							raw_image.convert("RGB"      )
        if self.image_transform is not None:
         lowerCAmelCase__      :							str    =							self.image_transform(snake_case      )
        lowerCAmelCase__      :							Any    =							extract_label(snake_case      )
        if self.label_to_id is not None:
         lowerCAmelCase__      :							Dict    =							self.label_to_id[label]
        return {"image": image, "label": label}




def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	Optional[Any]:
 # Initialize accelerator
 lowerCAmelCase__      :							List[Any]    =							Accelerator(cpu=args.cpu				,				mixed_precision=args.mixed_precision      )

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCAmelCase__      :							Tuple    =							config["lr"]
 lowerCAmelCase__      :							Tuple    =							int(config["num_epochs"]      )
 lowerCAmelCase__      :							Any    =							int(config["seed"]      )
 lowerCAmelCase__      :							Tuple    =							int(config["batch_size"]      )
 lowerCAmelCase__      :							Dict    =							config["image_size"]
 if not isinstance(lowercase__				,				(list, tuple)      ):
  lowerCAmelCase__      :							List[Any]    =							(image_size, image_size)

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

 # Build the label correspondences
 lowerCAmelCase__      :							Any    =							[extract_label(lowercase__      ) for fname in file_names]
 lowerCAmelCase__      :							str    =							list(set(lowercase__      )      )
 id_to_label.sort()
 lowerCAmelCase__      :							str    =							{lbl: i for i, lbl in enumerate(lowercase__      )}

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

 # Split our filenames between train and validation
 lowerCAmelCase__      :							List[str]    =							np.random.permutation(len(lowercase__      )      )
 lowerCAmelCase__      :							str    =							int(0.8 * len(lowercase__      )      )
 lowerCAmelCase__      :							List[str]    =							random_perm[:cut]
 lowerCAmelCase__      :							Tuple    =							random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 lowerCAmelCase__      :							List[str]    =							Compose([RandomResizedCrop(lowercase__				,				scale=(0.5, 1.0)      ), ToTensor()]      )
 lowerCAmelCase__      :							List[str]    =							PetsDataset(
     [file_names[i] for i in train_split]				,				image_transform=lowercase__				,				label_to_id=lowercase__      )

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

 # Instantiate dataloaders.
 lowerCAmelCase__      :							str    =							DataLoader(lowercase__				,				shuffle=lowercase__				,				batch_size=lowercase__				,				num_workers=4      )
 lowerCAmelCase__      :							List[str]    =							DataLoader(lowercase__				,				shuffle=lowercase__				,				batch_size=lowercase__				,				num_workers=4      )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowerCAmelCase__      :							Any    =							create_model("resnet50d"				,				pretrained=lowercase__				,				num_classes=len(lowercase__      )      )

 # 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).
 lowerCAmelCase__      :							Union[str, Any]    =							model.to(accelerator.device      )

 # Freezing the base model
 for param in model.parameters():
  lowerCAmelCase__      :							Optional[Any]    =							False
 for param in model.get_classifier().parameters():
  lowerCAmelCase__      :							int    =							True

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

 # Instantiate optimizer
 lowerCAmelCase__      :							Optional[int]    =							torch.optim.Adam(params=model.parameters()				,				lr=lr / 2_5      )

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

 # 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.
 lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							accelerator.prepare(
     lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      )

 # Now we train the model
 for epoch in range(lowercase__      ):
  model.train()
  for step, batch in enumerate(lowercase__      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   lowerCAmelCase__      :							Union[str, Any]    =							{k: v.to(accelerator.device      ) for k, v in batch.items()}
   lowerCAmelCase__      :							Optional[int]    =							(batch["image"] - mean) / std
   lowerCAmelCase__      :							List[Any]    =							model(lowercase__      )
   lowerCAmelCase__      :							Optional[int]    =							torch.nn.functional.cross_entropy(lowercase__				,				batch["label"]      )
   accelerator.backward(lowercase__      )
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()

  model.eval()
  lowerCAmelCase__      :							Union[str, Any]    =							0
  lowerCAmelCase__      :							int    =							0
  for _, batch in enumerate(lowercase__      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   lowerCAmelCase__      :							Dict    =							{k: v.to(accelerator.device      ) for k, v in batch.items()}
   lowerCAmelCase__      :							Union[str, Any]    =							(batch["image"] - mean) / std
   with torch.no_grad():
    lowerCAmelCase__      :							Dict    =							model(lowercase__      )
   lowerCAmelCase__      :							List[str]    =							outputs.argmax(dim=-1      )
   lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[Any]    =							accelerator.gather_for_metrics((predictions, batch["label"])      )
   lowerCAmelCase__      :							List[Any]    =							predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  lowerCAmelCase__      :							Any    =							accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}: {1_0_0 * eval_metric:.2f}"""      )



def 			SCREAMING_SNAKE_CASE       (   ) ->	Optional[int]:
 lowerCAmelCase__      :							Tuple    =							argparse.ArgumentParser(description="Simple example of training script."      )
 parser.add_argument("--data_dir"				,				required=lowercase__				,				help="The data folder on disk."      )
 parser.add_argument(
     "--mixed_precision"				,				type=lowercase__				,				default=lowercase__				,				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(
     "--checkpointing_steps"				,				type=lowercase__				,				default=lowercase__				,				help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."				,				)
 parser.add_argument("--cpu"				,				action="store_true"				,				help="If passed, will train on the CPU."      )
 lowerCAmelCase__      :							List[str]    =							parser.parse_args()
 lowerCAmelCase__      :							Tuple    =							{"lr": 3E-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
 training_function(lowercase__				,				lowercase__      )


if __name__ == "__main__":
    main()









"""simple docstring"""

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_UpperCamelCase		    =						logging.getLogger(__name__)






@dataclass
class  __a				:





       """simple docstring"""


       __UpperCamelCase				:			str          =	field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
       __UpperCamelCase				:			Optional[str]          =	field(
           default=__magic_name__					,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
       __UpperCamelCase				:			Optional[str]          =	field(
           default=__magic_name__					,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
       __UpperCamelCase				:			Optional[str]          =	field(
           default=__magic_name__					,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,  )






@dataclass
class  __a				:





       """simple docstring"""


       __UpperCamelCase				:			str          =	field(
           metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()					)}					)
       __UpperCamelCase				:			str          =	field(
           metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
       __UpperCamelCase				:			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.'
               )
           }					,  )
       __UpperCamelCase				:			bool          =	field(
           default=__magic_name__					,  metadata={'help': 'Overwrite the cached training and evaluation sets'}					)




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Dict[str, torch.Tensor]:
 lowerCAmelCase__      :							List[Any]    =							default_data_collator(lowercase__      )
 lowerCAmelCase__      :							Union[str, Any]    =							batch.pop("pairID"				,				lowercase__      )
 return batch



def 			SCREAMING_SNAKE_CASE       (   ) ->	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.

 lowerCAmelCase__      :							str    =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
 lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir      )
     and os.listdir(training_args.output_dir      )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      " --overwrite_output_dir to overcome."      )

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,				datefmt="%m/%d/%Y %H:%M:%S"				,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,				)
 logger.warning(
     "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"				,				training_args.local_rank				,				training_args.device				,				training_args.n_gpu				,				bool(training_args.local_rank != -1      )				,				training_args.fpaa				,				)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank      ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("Training/evaluation parameters %s"				,				lowercase__      )

 # Set seed
 set_seed(training_args.seed      )

 try:
  lowerCAmelCase__      :							Tuple    =							hans_tasks_num_labels[data_args.task_name]
 except KeyError:
  raise ValueError("Task not found: %s" % (data_args.task_name)      )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 lowerCAmelCase__      :							int    =							AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path				,				num_labels=lowercase__				,				finetuning_task=data_args.task_name				,				cache_dir=model_args.cache_dir				,				)
 lowerCAmelCase__      :							Optional[int]    =							AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,				cache_dir=model_args.cache_dir				,				)
 lowerCAmelCase__      :							Union[str, Any]    =							AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path				,				from_tf=bool(".ckpt" in model_args.model_name_or_path      )				,				config=lowercase__				,				cache_dir=model_args.cache_dir				,				)

 # Get datasets
 lowerCAmelCase__      :							Dict    =							(
     HansDataset(
         data_dir=data_args.data_dir				,				tokenizer=lowercase__				,				task=data_args.task_name				,				max_seq_length=data_args.max_seq_length				,				overwrite_cache=data_args.overwrite_cache				,				)
     if training_args.do_train
     else None
 )
 lowerCAmelCase__      :							Union[str, Any]    =							(
     HansDataset(
         data_dir=data_args.data_dir				,				tokenizer=lowercase__				,				task=data_args.task_name				,				max_seq_length=data_args.max_seq_length				,				overwrite_cache=data_args.overwrite_cache				,				evaluate=lowercase__				,				)
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 lowerCAmelCase__      :							Any    =							Trainer(
     model=lowercase__				,				args=lowercase__				,				train_dataset=lowercase__				,				eval_dataset=lowercase__				,				data_collator=lowercase__				,				)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
 if training_args.do_eval:
  logger.info("*** Evaluate ***"      )

  lowerCAmelCase__      :							Tuple    =							trainer.predict(lowercase__      )
  lowerCAmelCase__      :							Optional[Any]    =							output.predictions
  lowerCAmelCase__      :							Dict    =							np.argmax(lowercase__				,				axis=1      )

  lowerCAmelCase__      :							int    =							[ex.pairID for ex in eval_dataset]
  lowerCAmelCase__      :							Tuple    =							os.path.join(training_args.output_dir				,				"hans_predictions.txt"      )
  lowerCAmelCase__      :							Tuple    =							eval_dataset.get_labels()
  if trainer.is_world_master():
   with open(lowercase__				,				"w"      ) as writer:
    writer.write("pairID,gold_label\n"      )
    for pid, pred in zip(lowercase__				,				lowercase__      ):
     writer.write("ex" + str(lowercase__      ) + "," + label_list[int(lowercase__      )] + "\n"      )

  trainer._log(output.metrics      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Optional[Any]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
    main()






