






'''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__		(					a_  :     Union[str, Any]			)   -> Tuple:
       UpperCAmelCase__	:   List[str]												=	fname.split(os.path.sep			)[-1]
       return re.search(r'''^(.*)_\d+\.jpg$'''			,						a_			).groups()[0]




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=None						):
             UpperCAmelCase__	:   Any												=	file_names
             UpperCAmelCase__	:   Union[str, Any]												=	image_transform
             UpperCAmelCase__	:   List[Any]												=	label_to_id




      def __len__(					self						):
             return len(self.file_names						)




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






def  lowerCAmelCase__		(					a_  :     Optional[Any]			,						a_  :     Union[str, Any]			)   -> Optional[Any]:
       UpperCAmelCase__	:   List[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__	:   Optional[int]												=	int(config['''num_epochs''']			)
       UpperCAmelCase__	:   Any												=	int(config['''seed''']			)
       UpperCAmelCase__	:   Tuple												=	int(config['''batch_size''']			)
       UpperCAmelCase__	:   int												=	config['''image_size''']
       if not isinstance(a_			,						(list, tuple)			):
              UpperCAmelCase__	:   List[Any]												=	(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__	:   str												=	[extract_label(a_			) for fname in file_names]
       UpperCAmelCase__	:   Union[str, Any]												=	list(set(a_			)			)
       id_to_label.sort()
       UpperCAmelCase__	:   Any												=	{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__	:   Optional[Any]												=	np.random.permutation(len(a_			)			)
       UpperCAmelCase__	:   Optional[int]												=	int(0.8 * len(a_			)			)
       UpperCAmelCase__	:   List[str]												=	random_perm[:cut]
       UpperCAmelCase__	:   Optional[Any]												=	random_perm[cut:]

       # For training we use a simple RandomResizedCrop
       UpperCAmelCase__	:   List[Any]												=	Compose([RandomResizedCrop(a_			,						scale=(0.5, 1.0)			), ToTensor()]			)
       UpperCAmelCase__	:   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__	:   int												=	Compose([Resize(a_			), ToTensor()]			)
       UpperCAmelCase__	:   str												=	PetsDataset([file_names[i] for i in eval_split]			,						image_transform=a_			,						label_to_id=a_			)

       # Instantiate dataloaders.
       UpperCAmelCase__	:   Optional[int]												=	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__	:   int												=	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__	:   Tuple												=	model.to(accelerator.device			)

       # Freezing the base model
       for param in model.parameters():
              UpperCAmelCase__	:   List[Any]												=	False
       for param in model.get_classifier().parameters():
              UpperCAmelCase__	:   List[Any]												=	True

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

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

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

              UpperCAmelCase__	:   Tuple												=	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}"""			)






def  lowerCAmelCase__		(					)   -> Dict:
       UpperCAmelCase__	:   str												=	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__	:   Any												=	parser.parse_args()
       UpperCAmelCase__	:   Optional[Any]												=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
       training_function(a_			,						a_			)


if __name__ == "__main__":
  main()





import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			if "model" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""model."""       ,						"""""" )
			if "norm1" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm1"""       ,						"""attention.output.LayerNorm""" )
			if "norm2" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm2"""       ,						"""output.LayerNorm""" )
			if "norm" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm"""       ,						"""LayerNorm""" )
			if "transformer" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.split(""".""" )[0].split("""_""" )[-1]
						SCREAMING_SNAKE_CASE     =       orig_key.replace(f"""transformer_{layer_num}"""       ,						f"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mha.attn"""       ,						"""attention.self""" )
			if "mha" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mha"""       ,						"""attention""" )
			if "W_q" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_q"""       ,						"""self.query""" )
			if "W_k" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_k"""       ,						"""self.key""" )
			if "W_v" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_v"""       ,						"""self.value""" )
			if "ff1" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff1"""       ,						"""intermediate.dense""" )
			if "ff2" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff2"""       ,						"""output.dense""" )
			if "ff" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff"""       ,						"""output.dense""" )
			if "mlm_class" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mlm.mlm_class"""       ,						"""cls.predictions.decoder""" )
			if "mlm" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mlm"""       ,						"""cls.predictions.transform""" )
			if "cls" not in orig_key:
						SCREAMING_SNAKE_CASE     =       """nystromformer.""" + orig_key

			return orig_key
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

						if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
									continue
						else:
									SCREAMING_SNAKE_CASE     =       val

			SCREAMING_SNAKE_CASE     =       orig_state_dict["""cls.predictions.decoder.bias"""]
			SCREAMING_SNAKE_CASE     =       (
			    torch.arange(config.max_position_embeddings ).expand((1, -1) ) + 2
			)

			return orig_state_dict
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.load(SCREAMING_SNAKE_CASE       ,						map_location="""cpu""" )["""model_state_dict"""]
			SCREAMING_SNAKE_CASE     =       NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       NystromformerForMaskedLM(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       convert_checkpoint_helper(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

			model.load_state_dict(SCREAMING_SNAKE_CASE )
			model.eval()
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
		__A    :				List[str]    						=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
		)
		parser.add_argument(
		    """--config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help="""The json file for Nystromformer model config.""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__A    :				Union[str, Any]    						=	parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






