






'''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__	:  int ):

			"""simple docstring"""





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



class _A  (    UpperCamelCase__    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=None ,       __UpperCAmelCase=None				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     file_names
					__UpperCAmelCase   :      Any										=     image_transform
					__UpperCAmelCase   :      str										=     label_to_id





		def __len__(     self				)       ->     Dict:

					'''simple docstring'''
					return len(self.file_names				)





		def __getitem__(     self ,       __UpperCAmelCase				)       ->     List[str]:

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


def   lowercase_	(   lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  Union[str, Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Any										=     Accelerator(cpu=args.cpu			,							mixed_precision=args.mixed_precision )

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

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

			# Build the label correspondences
			__UpperCAmelCase   :      List[str]										=     [extract_label(__a ) for fname in file_names]
			__UpperCAmelCase   :      int										=     list(set(__a ) )
			id_to_label.sort()
			__UpperCAmelCase   :      Optional[int]										=     {lbl: i for i, lbl in enumerate(__a )}

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

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

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

			# For evaluation, we use a deterministic Resize
			__UpperCAmelCase   :      Union[str, Any]										=     Compose([Resize(__a ), ToTensor()] )
			__UpperCAmelCase   :      int										=     PetsDataset([file_names[i] for i in eval_split]			,							image_transform=__a			,							label_to_id=__a )

			# Instantiate dataloaders.
			__UpperCAmelCase   :      Optional[Any]										=     DataLoader(__a			,							shuffle=__a			,							batch_size=__a			,							num_workers=4 )
			__UpperCAmelCase   :      List[str]										=     DataLoader(__a			,							shuffle=__a			,							batch_size=__a			,							num_workers=4 )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__UpperCAmelCase   :      List[str]										=     create_model("""resnet50d"""			,							pretrained=__a			,							num_classes=len(__a ) )

			# 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).
			__UpperCAmelCase   :      List[str]										=     model.to(accelerator.device )

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

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

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

			# Instantiate learning rate scheduler
			__UpperCAmelCase   :      Tuple										=     OneCycleLR(optimizer=__a			,							max_lr=__a			,							epochs=__a			,							steps_per_epoch=len(__a ) )

			# 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.
			__UpperCAmelCase   :      int										=     accelerator.prepare(
			    __a			,							__a			,							__a			,							__a			,							__a )

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

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

						__UpperCAmelCase   :      Any										=     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"""





			__UpperCAmelCase   :      Dict										=     argparse.ArgumentParser(description="""Simple example of training script.""" )
			parser.add_argument("""--data_dir"""			,							required=__a			,							help="""The data folder on disk.""" )
			parser.add_argument(
			    """--mixed_precision"""			,							type=__a			,							default=__a			,							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=__a			,							default=__a			,							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.""" )
			__UpperCAmelCase   :      Union[str, Any]										=     parser.parse_args()
			__UpperCAmelCase   :      int										=     {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
			training_function(__a			,							__a )


if __name__ == "__main__":
					main()









'''simple docstring'''
import torch

from transformers import AutoTokenizer



class _A  (    __SCREAMING_SNAKE_CASE    ):





		def __init__(     self ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     AutoTokenizer.from_pretrained(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      Dict										=     None
								for l in x:
											__UpperCAmelCase   :      Dict										=     self.tokenizer(
											    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )
											__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor([len(__UpperCAmelCase				)]				)
											if d is not None:
														for k in d.keys():
																	__UpperCAmelCase   :      Dict										=     torch.cat((d[k], t[k]) ,       0				)
											else:
														__UpperCAmelCase   :      List[str]										=     t

								__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""				)				)
								__UpperCAmelCase   :      Any										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""				)				)

					elif isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      int										=     self.tokenizer(
								    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )

					else:
								raise Exception(
								    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
								    """ strings` for supports are supported."""				)

					return d





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=0.70				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     []
					for idx in range(len(W_query["""input_ids"""]				)				):
								__UpperCAmelCase   :      Optional[int]										=     range(p_start.shape[1]				)

								__UpperCAmelCase   :      Union[str, Any]										=     []
								for start_id in start_indexes:
											for end_id in end_indexes:
														if start_id < end_id:
																	output.append(
																	    (
																	        start_id,
																	        end_id,
																	        p_start[idx][start_id].item(),
																	        p_end[idx][end_id].item(),
																	    )				)

								output.sort(key=lambda __UpperCAmelCase				: (tup[2] * tup[3]) ,       reverse=__UpperCAmelCase				)
								__UpperCAmelCase   :      Optional[Any]										=     []
								for k in range(len(__UpperCAmelCase				)				):
											if output[k][2] * output[k][3] >= thresh:
														__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     output[k][0], output[k][1]
														__UpperCAmelCase   :      int										=     self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]				)
														temp.append((decoded, output[k][2] * output[k][3])				)

								final_outputs.append(__UpperCAmelCase				)

					return final_outputs




