"""simple docstring"""


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):


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

   return orig_key


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       List[str]       ):


   for key in orig_state_dict.copy().keys():
      _lowercase							     =       orig_state_dict.pop(_snake_case       )

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

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

   return orig_state_dict


def   __UpperCAmelCase     (	_snake_case		:       List[Any],				_snake_case		:       List[str],				_snake_case		:       str       ):
   _lowercase							     =       torch.load(_snake_case,				map_location="cpu"       )['''model_state_dict''']
   _lowercase							     =       NystromformerConfig.from_json_file(_snake_case       )
   _lowercase							     =       NystromformerForMaskedLM(_snake_case       )

   _lowercase							     =       convert_checkpoint_helper(_snake_case,				_snake_case       )

   model.load_state_dict(_snake_case       )
   model.eval()
   model.save_pretrained(_snake_case       )

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


if __name__ == "__main__":
  __UpperCamelCase       :    List[Any]         =		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."
  )
  __UpperCamelCase       :    Optional[int]         =		parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''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()
