

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 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







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





class A__     (			_lowercase			):



 """simple docstring"""







 def __init__(		self			:						int  ,    A_			:						Union[str, Any]  ,    A_			:						List[str]=None  ,    A_			:						Optional[int]=None							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     file_names
      _lowerCAmelCase :       Optional[Any]	   =     image_transform
      _lowerCAmelCase :       str	   =     label_to_id

 def __len__(		self			:						List[Any]							):






      '''simple docstring'''




      return len(self.file_names							)


 def __getitem__(		self			:						List[str]  ,    A_			:						Optional[int]							):






      '''simple docstring'''




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







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       Tuple	   =     Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision					)

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

     # Grab all the image filenames
     _lowerCAmelCase :       Optional[Any]	   =     [os.path.join(args.data_dir		,				lowerCamelCase_					) for fname in os.listdir(args.data_dir					) if fname.endswith(".jpg"					)]

     # Build the label correspondences
     _lowerCAmelCase :       Optional[int]	   =     [extract_label(lowerCamelCase_					) for fname in file_names]
     _lowerCAmelCase :       Any	   =     list(set(lowerCamelCase_					)					)
     id_to_label.sort()
     _lowerCAmelCase :       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
     _lowerCAmelCase :       Tuple	   =     np.random.permutation(len(lowerCamelCase_					)					)
     _lowerCAmelCase :       Optional[Any]	   =     int(0.8 * len(lowerCamelCase_					)					)
     _lowerCAmelCase :       Optional[Any]	   =     random_perm[:cut]
     _lowerCAmelCase :       Optional[int]	   =     random_perm[cut:]

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

     # Instantiate dataloaders.
     _lowerCAmelCase :       Union[str, Any]	   =     DataLoader(lowerCamelCase_		,				shuffle=lowerCamelCase_		,				batch_size=lowerCamelCase_		,				num_workers=4					)
     _lowerCAmelCase :       List[str]	   =     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)
     _lowerCAmelCase :       Tuple	   =     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).
     _lowerCAmelCase :       Any	   =     model.to(accelerator.device					)

     # Freezing the base model
     for param in model.parameters():
          _lowerCAmelCase :       List[Any]	   =     False
     for param in model.get_classifier().parameters():
          _lowerCAmelCase :       Dict	   =     True

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

     # Instantiate optimizer
     _lowerCAmelCase :       List[Any]	   =     torch.optim.Adam(params=model.parameters()		,				lr=lr / 25					)

     # Instantiate learning rate scheduler
     _lowerCAmelCase :       Tuple	   =     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.
     _lowerCAmelCase :       Optional[int]	   =     accelerator.prepare(
         lowerCamelCase_		,				lowerCamelCase_		,				lowerCamelCase_		,				lowerCamelCase_		,				lowerCamelCase_					)

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

          model.eval()
          _lowerCAmelCase :       Tuple	   =     0
          _lowerCAmelCase :       Dict	   =     0
          for _, batch in enumerate(lowerCamelCase_					):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               _lowerCAmelCase :       int	   =     {k: v.to(accelerator.device					) for k, v in batch.items()}
               _lowerCAmelCase :       int	   =     (batch["""image"""] - mean) / std
               with torch.no_grad():
                    _lowerCAmelCase :       List[Any]	   =     model(lowerCamelCase_					)
               _lowerCAmelCase :       List[str]	   =     outputs.argmax(dim=-1					)
               _lowerCAmelCase :       Union[str, 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 :       Optional[int]	   =     accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''					)







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     argparse.ArgumentParser(description="Simple example of training script."					)
     parser.add_argument("--data_dir"		,				required=lowerCamelCase_		,				help="The data folder on disk."					)
     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(
         "--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("--cpu"		,				action="store_true"		,				help="If passed, will train on the CPU."					)
     _lowerCAmelCase :       str	   =     parser.parse_args()
     _lowerCAmelCase :       Dict	   =     {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
     training_function(lowerCamelCase_		,				lowerCamelCase_					)


if __name__ == "__main__":
  main()








"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase		:    Tuple			=       {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    int			=       ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			lowercase		:    Optional[Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
