





'''simple docstring'''






import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__UpperCamelCase      :   Optional[int]								=     get_logger(__name__)





def      lowercase  (  lowerCAmelCase :						List[str]):



				"""simple docstring"""


				return TestCommand(
				    args.dataset   , args.name   , args.cache_dir   , args.data_dir   , args.all_configs   , args.save_info or args.save_infos   , args.ignore_verifications   , args.force_redownload   , args.clear_cache   , )




class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	__magic_name__							   =		False  # to tell pytest it's not a test class




	@staticmethod
	def 				_lowerCamelCase				(					UpperCAmelCase__			)       ->				str:
					_A :				Union[str, Any]				=  parser.add_parser('''test'''   ,     help='''Test dataset implementation.'''			)
					test_parser.add_argument('''--name'''   ,     type=UpperCAmelCase__   ,     default=UpperCAmelCase__   ,     help='''Dataset processing name'''			)
					test_parser.add_argument(
					    '''--cache_dir'''   ,     type=UpperCAmelCase__   ,     default=UpperCAmelCase__   ,     help='''Cache directory where the datasets are stored.'''   ,     )
					test_parser.add_argument(
					    '''--data_dir'''   ,     type=UpperCAmelCase__   ,     default=UpperCAmelCase__   ,     help='''Can be used to specify a manual directory to get the files from.'''   ,     )
					test_parser.add_argument('''--all_configs'''   ,     action='''store_true'''   ,     help='''Test all dataset configurations'''			)
					test_parser.add_argument(
					    '''--save_info'''   ,     action='''store_true'''   ,     help='''Save the dataset infos in the dataset card (README.md)'''			)
					test_parser.add_argument(
					    '''--ignore_verifications'''   ,     action='''store_true'''   ,     help='''Run the test without checksums and splits checks.'''   ,     )
					test_parser.add_argument('''--force_redownload'''   ,     action='''store_true'''   ,     help='''Force dataset redownload'''			)
					test_parser.add_argument(
					    '''--clear_cache'''   ,     action='''store_true'''   ,     help='''Remove downloaded files and cached datasets after each config test'''   ,     )
					# aliases
					test_parser.add_argument('''--save_infos'''   ,     action='''store_true'''   ,     help='''alias to save_info'''			)
					test_parser.add_argument('''dataset'''   ,     type=UpperCAmelCase__   ,     help='''Name of the dataset to download'''			)
					test_parser.set_defaults(func=_test_command_factory			)




	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     )       ->				List[str]:
					_A :				Optional[int]				=  dataset
					_A :				str				=  name
					_A :				List[Any]				=  cache_dir
					_A :				str				=  data_dir
					_A :				Optional[Any]				=  all_configs
					_A :				Dict				=  save_infos
					_A :				int				=  ignore_verifications
					_A :				Optional[int]				=  force_redownload
					_A :				Any				=  clear_cache
					if clear_cache and not cache_dir:
									print(
									    '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
									    '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
									    '''Please provide a --cache_dir that will be used to test the dataset script.'''			)
									exit(1			)
					if save_infos:
									_A :				str				=  True




	def 				_lowerCamelCase				(					self			)       ->				Optional[int]:
					fl_logger().setLevel(UpperCAmelCase__			)
					if self._name is not None and self._all_configs:
									print('''Both parameters `config` and `all_configs` can\'t be used at once.'''			)
									exit(1			)
					_A	,       _A :				int				=  self._dataset, self._name
					_A :				Any				=  dataset_module_factory(UpperCAmelCase__			)
					_A :				List[str]				=  import_main_class(module.module_path			)
					_A :				List[Any]				=  len(builder_cls.BUILDER_CONFIGS			) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

					def get_builders() -> Generator[DatasetBuilder, None, None]:
									if self._all_configs and builder_cls.BUILDER_CONFIGS:
													for i, config in enumerate(builder_cls.BUILDER_CONFIGS			):
																	if "config_name" in module.builder_kwargs:
																					yield builder_cls(
																					    cache_dir=self._cache_dir   ,     data_dir=self._data_dir   ,     **module.builder_kwargs   ,     )
																	else:
																					yield builder_cls(
																					    config_name=config.name   ,     cache_dir=self._cache_dir   ,     data_dir=self._data_dir   ,     **module.builder_kwargs   ,     )
									else:
													if "config_name" in module.builder_kwargs:
																	yield builder_cls(cache_dir=self._cache_dir   ,     data_dir=self._data_dir   ,     **module.builder_kwargs			)
													else:
																	yield builder_cls(
																	    config_name=UpperCAmelCase__   ,     cache_dir=self._cache_dir   ,     data_dir=self._data_dir   ,     **module.builder_kwargs   ,     )

					for j, builder in enumerate(get_builders()			):
									print(F"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""			)
									_A :				Any				=  os.path.exists(
									    os.path.join(builder.get_imported_module_dir()   ,     datasets.config.DATASETDICT_INFOS_FILENAME			)			)  # record checksums only if we need to update a (deprecated) dataset_infos.json
									builder.download_and_prepare(
									    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
									    if not self._force_redownload
									    else DownloadMode.FORCE_REDOWNLOAD   ,     verification_mode=VerificationMode.NO_CHECKS
									    if self._ignore_verifications
									    else VerificationMode.ALL_CHECKS   ,     try_from_hf_gcs=UpperCAmelCase__   ,     )
									builder.as_dataset()
									if self._save_infos:
													builder._save_infos()

									# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
									# The dataset_infos are saved in the YAML part of the README.md

									# Let's move it to the original directory of the dataset script, to allow the user to
									# upload them on S3 at the same time afterwards.
									if self._save_infos:
													_A :				Dict				=  os.path.join(builder_cls.get_imported_module_dir()   ,     '''README.md'''			)
													_A :				int				=  Path(UpperCAmelCase__			).name + '''.py'''
													_A :				List[str]				=  os.path.join(UpperCAmelCase__   ,     UpperCAmelCase__			)
													if os.path.isfile(UpperCAmelCase__			):
																	_A :				int				=  os.path.dirname(UpperCAmelCase__			)
													elif os.path.isfile(UpperCAmelCase__			):
																	_A :				List[str]				=  path
													elif os.path.isdir(UpperCAmelCase__			):  # for local directories containing only data files
																	_A :				List[str]				=  path
													else:  # in case of a remote dataset
																	_A :				Dict				=  None
																	print(F"""Dataset card saved at {dataset_readme_path}"""			)

													# Move dataset_info back to the user
													if dataset_dir is not None:
																	_A :				Optional[int]				=  os.path.join(UpperCAmelCase__   ,     '''README.md'''			)
																	copyfile(UpperCAmelCase__   ,     UpperCAmelCase__			)
																	print(F"""Dataset card saved at {user_dataset_readme_path}"""			)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
									if self._clear_cache:
													if os.path.isdir(builder._cache_dir			):
																	logger.warning(F"""Clearing cache at {builder._cache_dir}"""			)
																	rmtree(builder._cache_dir			)
													_A :				int				=  os.path.join(self._cache_dir   ,     datasets.config.DOWNLOADED_DATASETS_DIR			)
													if os.path.isdir(UpperCAmelCase__			):
																	logger.warning(F"""Clearing cache at {download_dir}"""			)
																	rmtree(UpperCAmelCase__			)

					print('''Test successful.'''			)







'''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      lowercase  (  lowerCAmelCase :						Any):



				"""simple docstring"""


				_A :				Union[str, Any]				=  fname.split(os.path.sep)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   , lowerCAmelCase).groups()[0]



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None			)       ->				int:
					_A :				Tuple				=  file_names
					_A :				Any				=  image_transform
					_A :				str				=  label_to_id




	def __len__(					self			)       ->				Dict:
					return len(self.file_names			)




	def __getitem__(					self   ,     UpperCAmelCase__			)       ->				Tuple:
					_A :				Union[str, Any]				=  self.file_names[idx]
					_A :				str				=  PIL.Image.open(UpperCAmelCase__			)
					_A :				str				=  raw_image.convert('''RGB'''			)
					if self.image_transform is not None:
									_A :				Tuple				=  self.image_transform(UpperCAmelCase__			)
					_A :				List[Any]				=  extract_label(UpperCAmelCase__			)
					if self.label_to_id is not None:
									_A :				Tuple				=  self.label_to_id[label]
					return {"image": image, "label": label}





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				Union[str, Any]				=  Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision)

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

				# Grab all the image filenames
				_A :				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
				_A :				str				=  [extract_label(lowerCAmelCase) for fname in file_names]
				_A :				List[Any]				=  list(set(lowerCAmelCase))
				id_to_label.sort()
				_A :				Dict				=  {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
				_A :				Dict				=  np.random.permutation(len(lowerCAmelCase))
				_A :				List[str]				=  int(0.8 * len(lowerCAmelCase))
				_A :				Tuple				=  random_perm[:cut]
				_A :				Union[str, Any]				=  random_perm[cut:]

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

				# For evaluation, we use a deterministic Resize
				_A :				List[Any]				=  Compose([Resize(lowerCAmelCase), ToTensor()])
				_A :				str				=  PetsDataset([file_names[i] for i in eval_split]   , image_transform=lowerCAmelCase   , label_to_id=lowerCAmelCase)

				# Instantiate dataloaders.
				_A :				str				=  DataLoader(lowerCAmelCase   , shuffle=lowerCAmelCase   , batch_size=lowerCAmelCase   , num_workers=4)
				_A :				Optional[int]				=  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)
				_A :				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).
				_A :				Tuple				=  model.to(accelerator.device)

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

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

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

				# Instantiate learning rate scheduler
				_A :				List[str]				=  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.
				_A	,       _A	,       _A	,       _A	,       _A :				List[str]				=  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`.
												_A :				Optional[Any]				=  {k: v.to(accelerator.device) for k, v in batch.items()}
												_A :				Tuple				=  (batch['''image'''] - mean) / std
												_A :				Any				=  model(lowerCAmelCase)
												_A :				Optional[Any]				=  torch.nn.functional.cross_entropy(lowerCAmelCase   , batch['''label'''])
												accelerator.backward(lowerCAmelCase)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

								_A :				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      lowercase  (  ):



				"""simple docstring"""


				_A :				Any				=  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.''')
				_A :				Optional[int]				=  parser.parse_args()
				_A :				Optional[Any]				=  {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(lowerCAmelCase   , lowerCAmelCase)


if __name__ == "__main__":
	main()
