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



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=None  ,							snake_case_=None							)   ->       Optional[Any]:
													__lowerCAmelCase        =       file_names
													__lowerCAmelCase        =       image_transform
													__lowerCAmelCase        =       label_to_id




							def __len__(				self							)   ->       Union[str, Any]:
													return len(self.file_names							)




							def __getitem__(				self  ,							snake_case_							)   ->       Tuple:
													__lowerCAmelCase        =       self.file_names[idx]
													__lowerCAmelCase        =       PIL.Image.open(snake_case_							)
													__lowerCAmelCase        =       raw_image.convert("""RGB"""							)
													if self.image_transform is not None:
																			__lowerCAmelCase        =       self.image_transform(snake_case_							)
													__lowerCAmelCase        =       extract_label(snake_case_							)
													if self.label_to_id is not None:
																			__lowerCAmelCase        =       self.label_to_id[label]
													return {"image": image, "label": label}

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						# Initialize accelerator
						__lowerCAmelCase        =       Accelerator(cpu=args.cpu				,  mixed_precision=args.mixed_precision		)

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

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

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

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

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

						# Instantiate dataloaders.
						__lowerCAmelCase        =       DataLoader(_lowerCAmelCase				,  shuffle=_lowerCAmelCase				,  batch_size=_lowerCAmelCase				,  num_workers=4		)
						__lowerCAmelCase        =       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        =       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        =       model.to(accelerator.device		)

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

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

						# Instantiate optimizer
						__lowerCAmelCase        =       torch.optim.Adam(params=model.parameters()				,  lr=lr / 25		)

						# Instantiate learning rate scheduler
						__lowerCAmelCase        =       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 ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       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        =       {k: v.to(accelerator.device		) for k, v in batch.items()}
																		__lowerCAmelCase        =       (batch["""image"""] - mean) / std
																		__lowerCAmelCase        =       model(_lowerCAmelCase		)
																		__lowerCAmelCase        =       torch.nn.functional.cross_entropy(_lowerCAmelCase				,  batch["""label"""]		)
																		accelerator.backward(_lowerCAmelCase		)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

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

												__lowerCAmelCase        =       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      ():
						__lowerCAmelCase        =       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        =       parser.parse_args()
						__lowerCAmelCase        =       {"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
						training_function(_lowerCAmelCase				,  _lowerCAmelCase		)


if __name__ == "__main__":
			main()


"""simple docstring"""
import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
			import torch
class      lowerCAmelCase_						(							unittest.TestCase							,							A__				):



							'''simple docstring'''





							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       load_tool("""speech-to-text"""							)
													self.tool.setup()




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       self.tool(torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)




							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.tool(audio=torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)
