






"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase						:    Optional[Any]		=						logging.get_logger(__name__)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			ConvNextConfig()

  if "tiny" in checkpoint_url:
    lowercase				       =			[3, 3, 9, 3]
    lowercase				       =			[96, 1_92, 3_84, 7_68]
  if "small" in checkpoint_url:
    lowercase				       =			[3, 3, 27, 3]
    lowercase				       =			[96, 1_92, 3_84, 7_68]
  if "base" in checkpoint_url:
    lowercase				       =			[3, 3, 27, 3]
    lowercase				       =			[1_28, 2_56, 5_12, 10_24]
  if "large" in checkpoint_url:
    lowercase				       =			[3, 3, 27, 3]
    lowercase				       =			[1_92, 3_84, 7_68, 15_36]
  if "xlarge" in checkpoint_url:
    lowercase				       =			[3, 3, 27, 3]
    lowercase				       =			[2_56, 5_12, 10_24, 20_48]

  if "1k" in checkpoint_url:
    lowercase				       =			10_00
    lowercase				       =			'imagenet-1k-id2label.json'
    lowercase				       =			(1, 10_00)
  else:
    lowercase				       =			2_18_41
    lowercase				       =			'imagenet-22k-id2label.json'
    lowercase				       =			(1, 2_18_41)

  lowercase				       =			'huggingface/label-files'
  lowercase				       =			num_labels
  lowercase				       =			json.load(open(hf_hub_download(__snake_case						,   __snake_case						,   repo_type='dataset'       )						,   'r'       )       )
  lowercase				       =			{int(__snake_case       ): v for k, v in idalabel.items()}
  if "1k" not in checkpoint_url:
    # this dataset contains 21843 labels but the model only has 21841
    # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
    del idalabel[92_05]
    del idalabel[1_50_27]
  lowercase				       =			idalabel
  lowercase				       =			{v: k for k, v in idalabel.items()}
  lowercase				       =			hidden_sizes
  lowercase				       =			depths

  return config, expected_shape
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]       ):





  '''simple docstring'''





  if "downsample_layers.0.0" in name:
    lowercase				       =			name.replace('downsample_layers.0.0'						,   'embeddings.patch_embeddings'       )
  if "downsample_layers.0.1" in name:
    lowercase				       =			name.replace('downsample_layers.0.1'						,   'embeddings.norm'       )  # we rename to layernorm later on
  if "downsample_layers.1.0" in name:
    lowercase				       =			name.replace('downsample_layers.1.0'						,   'stages.1.downsampling_layer.0'       )
  if "downsample_layers.1.1" in name:
    lowercase				       =			name.replace('downsample_layers.1.1'						,   'stages.1.downsampling_layer.1'       )
  if "downsample_layers.2.0" in name:
    lowercase				       =			name.replace('downsample_layers.2.0'						,   'stages.2.downsampling_layer.0'       )
  if "downsample_layers.2.1" in name:
    lowercase				       =			name.replace('downsample_layers.2.1'						,   'stages.2.downsampling_layer.1'       )
  if "downsample_layers.3.0" in name:
    lowercase				       =			name.replace('downsample_layers.3.0'						,   'stages.3.downsampling_layer.0'       )
  if "downsample_layers.3.1" in name:
    lowercase				       =			name.replace('downsample_layers.3.1'						,   'stages.3.downsampling_layer.1'       )
  if "stages" in name and "downsampling_layer" not in name:
    # stages.0.0. for instance should be renamed to stages.0.layers.0.
    lowercase				       =			name[: len('stages.0'       )] + '.layers' + name[len('stages.0'       ) :]
  if "stages" in name:
    lowercase				       =			name.replace('stages'						,   'encoder.stages'       )
  if "norm" in name:
    lowercase				       =			name.replace('norm'						,   'layernorm'       )
  if "gamma" in name:
    lowercase				       =			name.replace('gamma'						,   'layer_scale_parameter'       )
  if "head" in name:
    lowercase				       =			name.replace('head'						,   'classifier'       )

  return name
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase				       =			Image.open(requests.get(__snake_case						,   stream=__snake_case       ).raw       )
  return im
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase       ,	lowercase				       =			get_convnext_config(__snake_case       )
  # load original state_dict from URL
  lowercase				       =			torch.hub.load_state_dict_from_url(__snake_case       )['model']
  # rename keys
  for key in state_dict.copy().keys():
    lowercase				       =			state_dict.pop(__snake_case       )
    lowercase				       =			val
  # add prefix to all keys expect classifier head
  for key in state_dict.copy().keys():
    lowercase				       =			state_dict.pop(__snake_case       )
    if not key.startswith('classifier'       ):
      lowercase				       =			'convnext.' + key
    lowercase				       =			val

  # load HuggingFace model
  lowercase				       =			ConvNextForImageClassification(__snake_case       )
  model.load_state_dict(__snake_case       )
  model.eval()

  # Check outputs on an image, prepared by ConvNextImageProcessor
  lowercase				       =			2_24 if '224' in checkpoint_url else 3_84
  lowercase				       =			ConvNextImageProcessor(size=__snake_case       )
  lowercase				       =			image_processor(images=prepare_img()						,   return_tensors='pt'       ).pixel_values

  lowercase				       =			model(__snake_case       ).logits

  # note: the logits below were obtained without center cropping
  if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
    lowercase				       =			torch.tensor([-0.1210, -0.6605, 0.1918]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
    lowercase				       =			torch.tensor([-0.4473, -0.1847, -0.6365]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
    lowercase				       =			torch.tensor([0.4525, 0.7539, 0.0308]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
    lowercase				       =			torch.tensor([0.3561, 0.6350, -0.0384]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
    lowercase				       =			torch.tensor([0.4174, -0.0989, 0.1489]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
    lowercase				       =			torch.tensor([0.2513, -0.1349, -0.1613]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
    lowercase				       =			torch.tensor([1.2980, 0.3631, -0.1198]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
    lowercase				       =			torch.tensor([1.2963, 0.1227, 0.1723]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
    lowercase				       =			torch.tensor([1.7956, 0.8390, 0.2820]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
    lowercase				       =			torch.tensor([-0.2822, -0.0502, -0.0878]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
    lowercase				       =			torch.tensor([-0.5672, -0.0730, -0.4348]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
    lowercase				       =			torch.tensor([0.2681, 0.2365, 0.6246]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
    lowercase				       =			torch.tensor([-0.2642, 0.3931, 0.5116]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
    lowercase				       =			torch.tensor([-0.6677, -0.1873, -0.8379]       )
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
    lowercase				       =			torch.tensor([-0.7749, -0.2967, -0.6444]       )
  else:
    raise ValueError(f'Unknown URL: {checkpoint_url}'       )

  assert torch.allclose(logits[0, :3]						,   __snake_case						,   atol=1e-3       )
  assert logits.shape == expected_shape

  Path(__snake_case       ).mkdir(exist_ok=__snake_case       )
  print(f'Saving model to {pytorch_dump_folder_path}'       )
  model.save_pretrained(__snake_case       )
  print(f'Saving image processor to {pytorch_dump_folder_path}'       )
  image_processor.save_pretrained(__snake_case       )

  print('Pushing model to the hub...'       )
  lowercase				       =			'convnext'
  if "tiny" in checkpoint_url:
    model_name += "-tiny"
  elif "small" in checkpoint_url:
    model_name += "-small"
  elif "base" in checkpoint_url:
    model_name += "-base"
  elif "xlarge" in checkpoint_url:
    model_name += "-xlarge"
  elif "large" in checkpoint_url:
    model_name += "-large"
  if "224" in checkpoint_url:
    model_name += "-224"
  elif "384" in checkpoint_url:
    model_name += "-384"
  if "22k" in checkpoint_url and "1k" not in checkpoint_url:
    model_name += "-22k"
  if "22k" in checkpoint_url and "1k" in checkpoint_url:
    model_name += "-22k-1k"

  model.push_to_hub(
      repo_path_or_name=Path(__snake_case						,   __snake_case       )						,   organization='nielsr'						,   commit_message='Add model'						,   )


if __name__ == "__main__":
    _UpperCamelCase						:    Union[str, Any]		=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--checkpoint_url',
        default='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth',
        type=str,
        help='URL of the original ConvNeXT checkpoint you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the output PyTorch model directory.',
    )

    _UpperCamelCase						:    List[Any]		=						parser.parse_args()
    convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








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 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	fname.split(os.path.sep		)[-1]
						return re.search(R"^(.*)_\d+\.jpg$"      ,       snake_case_		).groups()[0]






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     Union[str, Any]       ,       snake_case__       :     Tuple       ,       snake_case__       :     Dict=None       ,       snake_case__       :     Union[str, Any]=None ):




									"""simple docstring"""


									_UpperCAmelCase						=	file_names
									_UpperCAmelCase						=	image_transform
									_UpperCAmelCase						=	label_to_id







			def __len__(			self       :     Tuple ):




									"""simple docstring"""


									return len(self.file_names )







			def __getitem__(			self       :     List[str]       ,       snake_case__       :     Tuple ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.file_names[idx]
									_UpperCAmelCase						=	PIL.Image.open(snake_case__ )
									_UpperCAmelCase						=	raw_image.convert("RGB" )
									if self.image_transform is not None:
															_UpperCAmelCase						=	self.image_transform(snake_case__ )
									_UpperCAmelCase						=	extract_label(snake_case__ )
									if self.label_to_id is not None:
															_UpperCAmelCase						=	self.label_to_id[label]
									return {"image": image, "label": label}




def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision		)

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

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

						# Build the label correspondences
						_UpperCAmelCase						=	[extract_label(snake_case_		) for fname in file_names]
						_UpperCAmelCase						=	list(set(snake_case_		)		)
						id_to_label.sort()
						_UpperCAmelCase						=	{lbl: i for i, lbl in enumerate(snake_case_		)}

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

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

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

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

						# Instantiate dataloaders.
						_UpperCAmelCase						=	DataLoader(snake_case_      ,       shuffle=snake_case_      ,       batch_size=snake_case_      ,       num_workers=4		)
						_UpperCAmelCase						=	DataLoader(snake_case_      ,       shuffle=snake_case_      ,       batch_size=snake_case_      ,       num_workers=4		)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_UpperCAmelCase						=	create_model("resnet50d"      ,       pretrained=snake_case_      ,       num_classes=len(snake_case_		)		)

						# 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						=	model.to(accelerator.device		)

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

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

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

						# Instantiate learning rate scheduler
						_UpperCAmelCase						=	OneCycleLR(optimizer=snake_case_      ,       max_lr=snake_case_      ,       epochs=snake_case_      ,       steps_per_epoch=len(snake_case_		)		)

						# 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						=	accelerator.prepare(
						    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)

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

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

												_UpperCAmelCase						=	accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}"""		)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser(description="Simple example of training script."		)
						parser.add_argument("--data_dir"      ,       required=snake_case_      ,       help="The data folder on disk."		)
						parser.add_argument(
						    "--mixed_precision"      ,       type=snake_case_      ,       default=snake_case_      ,       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=snake_case_      ,       default=snake_case_      ,       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						=	parser.parse_args()
						_UpperCAmelCase						=	{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
						training_function(snake_case_      ,       snake_case_		)


if __name__ == "__main__":
				main()




