
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	(      lowercase_   )		->	List[str]:


		'''simple docstring'''






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



class       _snake_case   (	a__							):


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


			'''simple docstring'''




			lowercase__  :   List[str]    		=      file_names
			lowercase__  :   Dict    		=      image_transform
			lowercase__  :   Any    		=      label_to_id


	def __len__(					self):


			'''simple docstring'''




			return len(self.file_names)



	def __getitem__(					self					,  SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




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

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	int:


		'''simple docstring'''






		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__  :   int    		=      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__  :   Dict    		=      int(config["""num_epochs"""]   )
		lowercase__  :   Optional[int]    		=      int(config["""seed"""]   )
		lowercase__  :   List[Any]    		=      int(config["""batch_size"""]   )
		lowercase__  :   Optional[int]    		=      config["""image_size"""]
		if not isinstance(lowercase_ , (list, tuple)   ):
				lowercase__  :   Dict    		=      (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[int]    		=      args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						lowercase__  :   List[str]    		=      int(args.checkpointing_steps   )
				else:
						raise ValueError(
						    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'   )
		else:
				lowercase__  :   Any    		=      None

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

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

		# Build the label correspondences
		lowercase__  :   List[str]    		=      [extract_label(lowercase_   ) for fname in file_names]
		lowercase__  :   Optional[int]    		=      list(set(lowercase_   )   )
		id_to_label.sort()
		lowercase__  :   int    		=      {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
		lowercase__  :   int    		=      np.random.permutation(len(lowercase_   )   )
		lowercase__  :   int    		=      int(0.8 * len(lowercase_   )   )
		lowercase__  :   str    		=      random_perm[:cut]
		lowercase__  :   Dict    		=      random_perm[cut:]

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

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

		# Instantiate dataloaders.
		lowercase__  :   Union[str, Any]    		=      DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4   )
		lowercase__  :   Dict    		=      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)
		lowercase__  :   Optional[int]    		=      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).
		lowercase__  :   Tuple    		=      model.to(accelerator.device   )

		# Freezing the base model
		for param in model.parameters():
				lowercase__  :   str    		=      False
		for param in model.get_classifier().parameters():
				lowercase__  :   Optional[int]    		=      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__  :   Any    		=      torch.tensor(model.default_cfg["""std"""]   )[None, :, None, None].to(accelerator.device   )

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

		# Instantiate learning rate scheduler
		lowercase__  :   List[str]    		=      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.
		lowercase__		,    lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   str    		=      accelerator.prepare(
		    lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_   )
		# We need to keep track of how many total steps we have iterated over
		lowercase__  :   Any    		=      0
		# We also need to keep track of the starting epoch so files are named properly
		lowercase__  :   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__  :   Tuple    		=      os.path.basename(args.resume_from_checkpoint   )
				else:
						# Get the most recent checkpoint
						lowercase__  :   Dict    		=      [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
						dirs.sort(key=os.path.getctime   )
						lowercase__  :   Optional[Any]    		=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				lowercase__  :   Optional[int]    		=      os.path.splitext(lowercase_   )[0]

				if "epoch" in training_difference:
						lowercase__  :   str    		=      int(training_difference.replace("""epoch_""" , """"""   )   ) + 1
						lowercase__  :   str    		=      None
				else:
						lowercase__  :   Dict    		=      int(training_difference.replace("""step_""" , """"""   )   )
						lowercase__  :   Tuple    		=      resume_step // len(lowercase_   )
						resume_step -= starting_epoch * len(lowercase_   )

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

				lowercase__  :   int    		=      accurate.item() / num_elems
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'epoch {epoch}: {1_00 * eval_metric:.2f}'   )
				if args.with_tracking:
						accelerator.log(
						    {
						        """accuracy""": 1_00 * eval_metric,
						        """train_loss""": total_loss.item() / len(lowercase_   ),
						        """epoch""": epoch,
						    } , step=lowercase_ , )
				if checkpointing_steps == "epoch":
						lowercase__  :   Union[str, Any]    		=      F'epoch_{epoch}'
						if args.output_dir is not None:
								lowercase__  :   Dict    		=      os.path.join(args.output_dir , lowercase_   )
						accelerator.save_state(lowercase_   )

		if args.with_tracking:
				accelerator.end_training()

def 		UpperCamelCase	(      )		->	int:


		'''simple docstring'''






		lowercase__  :   str    		=      argparse.ArgumentParser(description="""Simple example of training script."""   )
		parser.add_argument("""--data_dir""" , required=lowercase_ , 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=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("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU."""   )
		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(
		    """--output_dir""" , type=lowercase_ , 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=lowercase_ , default=lowercase_ , 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=lowercase_ , default="""logs""" , help="""Location on where to store experiment tracking logs` and relevent project information""" , )
		lowercase__  :   Optional[int]    		=      parser.parse_args()
		lowercase__  :   Optional[int]    		=      {"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
		training_function(lowercase_ , lowercase_   )


if __name__ == "__main__":
					main()








from scipy.stats import spearmanr

import datasets


lowerCamelCase__    :		Union[str, Any]          =   """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

lowerCamelCase__    :		int          =   """
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

lowerCamelCase__    :		str          =   R"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,						_KWARGS_DESCRIPTION							)
class       _snake_case   (	datasets.Metric							):


 def      lowercase__     (					self):


   '''simple docstring'''




   return datasets.MetricInfo(
       description=_DESCRIPTION					,  citation=_CITATION					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
           {
               """predictions""": datasets.Value("""float"""),
               """references""": datasets.Value("""float"""),
           })					,  reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""]					,  )



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=False):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      spearmanr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   if return_pvalue:
     return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
     return {"spearmanr": results[0]}






