"""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       lowerCAmelCase__			(	_UpperCamelCase				: List[Any]    )    -> Optional[int]:





						"""simple docstring"""






						snake_case	    =	fname.split(os.path.sep    )[-1]
						return re.search(r'^(.*)_\d+\.jpg$'       ,	__SCREAMING_SNAKE_CASE    ).groups()[0]



class      lowerCAmelCase_			(	lowercase_    ):



				"""simple docstring"""

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






										"""simple docstring"""

										snake_case	    =	file_names
										snake_case	    =	image_transform
										snake_case	    =	label_to_id

				def __len__( self			):






										"""simple docstring"""

										return len(self.file_names			)

				def __getitem__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	self.file_names[idx]
										snake_case	    =	PIL.Image.open(__UpperCamelCase			)
										snake_case	    =	raw_image.convert('RGB'			)
										if self.image_transform is not None:
																snake_case	    =	self.image_transform(__UpperCamelCase			)
										snake_case	    =	extract_label(__UpperCamelCase			)
										if self.label_to_id is not None:
																snake_case	    =	self.label_to_id[label]
										return {"image": image, "label": label}





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: Union[str, Any]    )    -> Tuple:





						"""simple docstring"""






						if args.with_tracking:
												snake_case	    =	Accelerator(
												    cpu=args.cpu       ,	mixed_precision=args.mixed_precision       ,	log_with='all'       ,	project_dir=args.project_dir    )
						else:
												snake_case	    =	Accelerator(cpu=args.cpu       ,	mixed_precision=args.mixed_precision    )

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case	    =	config['lr']
						snake_case	    =	int(config['num_epochs']    )
						snake_case	    =	int(config['seed']    )
						snake_case	    =	int(config['batch_size']    )
						snake_case	    =	config['image_size']
						if not isinstance(__SCREAMING_SNAKE_CASE       ,	(list, tuple)    ):
												snake_case	    =	(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":
																		snake_case	    =	args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case	    =	int(args.checkpointing_steps    )
												else:
																		raise ValueError(
																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""    )
						else:
												snake_case	    =	None

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

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

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

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

						# Split our filenames between train and validation
						snake_case	    =	np.random.permutation(len(__SCREAMING_SNAKE_CASE    )    )
						snake_case	    =	int(0.8 * len(__SCREAMING_SNAKE_CASE    )    )
						snake_case	    =	random_perm[:cut]
						snake_case	    =	random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						snake_case	    =	Compose([RandomResizedCrop(__SCREAMING_SNAKE_CASE       ,	scale=(0.5, 1.0)    ), ToTensor()]    )
						snake_case	    =	PetsDataset(
						    [file_names[i] for i in train_split]       ,	image_transform=__SCREAMING_SNAKE_CASE       ,	label_to_id=__SCREAMING_SNAKE_CASE    )

						# For evaluation, we use a deterministic Resize
						snake_case	    =	Compose([Resize(__SCREAMING_SNAKE_CASE    ), ToTensor()]    )
						snake_case	    =	PetsDataset([file_names[i] for i in eval_split]       ,	image_transform=__SCREAMING_SNAKE_CASE       ,	label_to_id=__SCREAMING_SNAKE_CASE    )

						# Instantiate dataloaders.
						snake_case	    =	DataLoader(__SCREAMING_SNAKE_CASE       ,	shuffle=__SCREAMING_SNAKE_CASE       ,	batch_size=__SCREAMING_SNAKE_CASE       ,	num_workers=4    )
						snake_case	    =	DataLoader(__SCREAMING_SNAKE_CASE       ,	shuffle=__SCREAMING_SNAKE_CASE       ,	batch_size=__SCREAMING_SNAKE_CASE       ,	num_workers=4    )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case	    =	create_model('resnet50d'       ,	pretrained=__SCREAMING_SNAKE_CASE       ,	num_classes=len(__SCREAMING_SNAKE_CASE    )    )

						# 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).
						snake_case	    =	model.to(accelerator.device    )

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

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

						# Instantiate optimizer
						snake_case	    =	torch.optim.Adam(params=model.parameters()       ,	lr=lr / 2_5    )

						# Instantiate learning rate scheduler
						snake_case	    =	OneCycleLR(optimizer=__SCREAMING_SNAKE_CASE       ,	max_lr=__SCREAMING_SNAKE_CASE       ,	epochs=__SCREAMING_SNAKE_CASE       ,	steps_per_epoch=len(__SCREAMING_SNAKE_CASE    )    )

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

												if "epoch" in training_difference:
																		snake_case	    =	int(training_difference.replace('epoch_'       ,	''    )    ) + 1
																		snake_case	    =	None
												else:
																		snake_case	    =	int(training_difference.replace('step_'       ,	''    )    )
																		snake_case	    =	resume_step // len(__SCREAMING_SNAKE_CASE    )
																		resume_step -= starting_epoch * len(__SCREAMING_SNAKE_CASE    )

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

												snake_case	    =	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}"""    )
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': 1_0_0 * eval_metric,
																		        'train_loss': total_loss.item() / len(__SCREAMING_SNAKE_CASE    ),
																		        'epoch': epoch,
																		    }       ,	step=__SCREAMING_SNAKE_CASE       ,	)
												if checkpointing_steps == "epoch":
																		snake_case	    =	f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								snake_case	    =	os.path.join(args.output_dir       ,	__SCREAMING_SNAKE_CASE    )
																		accelerator.save_state(__SCREAMING_SNAKE_CASE    )

						if args.with_tracking:
												accelerator.end_training()





def       lowerCAmelCase__			(	)    -> Any:





						"""simple docstring"""






						snake_case	    =	argparse.ArgumentParser(description='Simple example of training script.'    )
						parser.add_argument('--data_dir'       ,	required=__SCREAMING_SNAKE_CASE       ,	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=__SCREAMING_SNAKE_CASE       ,	default=__SCREAMING_SNAKE_CASE       ,	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=__SCREAMING_SNAKE_CASE       ,	default=__SCREAMING_SNAKE_CASE       ,	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=__SCREAMING_SNAKE_CASE       ,	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=__SCREAMING_SNAKE_CASE       ,	default=__SCREAMING_SNAKE_CASE       ,	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=__SCREAMING_SNAKE_CASE       ,	default='logs'       ,	help='Location on where to store experiment tracking logs` and relevent project information'       ,	)
						snake_case	    =	parser.parse_args()
						snake_case	    =	{'lr': 3e-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
						training_function(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
					main()



"""simple docstring"""







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml







class        UpperCamelCase		(			yaml.SafeLoader  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				str													=	[self.constructed_objects[key_node] for key_node, _ in node.value]
        lowercase_	:				str													=	[tuple(__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else key for key in keys]
        lowercase_	:				List[Any]													=	Counter(__UpperCamelCase			)
        lowercase_	:				str													=	[key for key in counter if counter[key] > 1]
        if duplicate_keys:
             raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().construct_mapping(__UpperCamelCase     ,deep=__UpperCamelCase			)
        self._check_no_duplicates_on_constructed_node(__UpperCamelCase			)
        return mapping






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Tuple													=	list(readme_content.splitlines()      )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          lowercase_	:				Dict													=	full_content[1:].index('---'      ) + 1
          lowercase_	:				Optional[int]													=	'\n'.join(full_content[1:sep_idx]      )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

     return None, "\n".join(__SCREAMING_SNAKE_CASE      )







class        UpperCamelCase		(			lowercase_  ):
   # class attributes
   lowercase							=  {'train_eval_index'}  # train-eval-index in the YAML metadata
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
             lowercase_							,  lowercase_	:				Optional[int]													=	_split_yaml_from_readme(readme_file.read()			)
        if yaml_string is not None:
             return cls.from_yaml_string(__UpperCamelCase			)
        else:
             return cls()
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        if path.exists():
             with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
                  lowercase_	:				Dict													=	readme_file.read()
        else:
             lowercase_	:				int													=	None
        lowercase_	:				Any													=	self._to_readme(__UpperCamelCase			)
        with open(__UpperCamelCase     ,'w'     ,encoding='utf-8'			) as readme_file:
             readme_file.write(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = None			)	->      str:


        '''simple docstring'''


        if readme_content is not None:
             lowercase_							,  lowercase_	:				Optional[Any]													=	_split_yaml_from_readme(__UpperCamelCase			)
             lowercase_	:				Optional[Any]													=	'---\n' + self.to_yaml_string() + '---\n' + content
        else:
             lowercase_	:				Tuple													=	'---\n' + self.to_yaml_string() + '---\n'
        return full_content
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        lowercase_	:				List[str]													=	yaml.load(__UpperCamelCase     ,Loader=_NoDuplicateSafeLoader			) or {}

        # Convert the YAML keys to DatasetMetadata fields
        lowercase_	:				Dict													=	{
            (key.replace('-'     ,'_'			) if key.replace('-'     ,'_'			) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return yaml.safe_dump(
            {
                (key.replace('_'     ,'-'			) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }     ,sort_keys=__UpperCamelCase     ,allow_unicode=__UpperCamelCase     ,encoding='utf-8'     ,).decode('utf-8'			)






__SCREAMING_SNAKE_CASE    	={
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
       from argparse import ArgumentParser

       __SCREAMING_SNAKE_CASE    	=ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
       ap.add_argument("readme_filepath")
       __SCREAMING_SNAKE_CASE    	=ap.parse_args()

       __SCREAMING_SNAKE_CASE    	=Path(args.readme_filepath)
       __SCREAMING_SNAKE_CASE    	=DatasetMetadata.from_readme(readme_filepath)
       print(dataset_metadata)
       dataset_metadata.to_readme(readme_filepath)

