


"""simple docstring"""





import argparse
import json
import os

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

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase    				=      logging.get_logger(__name__)





def     lowercase__	(  lowerCamelCase     :  Optional[int]				)					->     Any:
  lowerCAmelCase__    :    List[Any]								=					ConvNextVaConfig()

  if "atto" in checkpoint_url:
    lowerCAmelCase__    :    str								=					[2, 2, 6, 2]
    lowerCAmelCase__    :    int								=					[4_0, 8_0, 1_6_0, 3_2_0]
  if "femto" in checkpoint_url:
    lowerCAmelCase__    :    List[Any]								=					[2, 2, 6, 2]
    lowerCAmelCase__    :    int								=					[4_8, 9_6, 1_9_2, 3_8_4]
  if "pico" in checkpoint_url:
    lowerCAmelCase__    :    Union[str, Any]								=					[2, 2, 6, 2]
    lowerCAmelCase__    :    List[Any]								=					[6_4, 1_2_8, 2_5_6, 5_1_2]
  if "nano" in checkpoint_url:
    lowerCAmelCase__    :    Any								=					[2, 2, 8, 2]
    lowerCAmelCase__    :    Optional[int]								=					[8_0, 1_6_0, 3_2_0, 6_4_0]
  if "tiny" in checkpoint_url:
    lowerCAmelCase__    :    Optional[int]								=					[3, 3, 9, 3]
    lowerCAmelCase__    :    Dict								=					[9_6, 1_9_2, 3_8_4, 7_6_8]
  if "base" in checkpoint_url:
    lowerCAmelCase__    :    Optional[int]								=					[3, 3, 2_7, 3]
    lowerCAmelCase__    :    Union[str, Any]								=					[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
  if "large" in checkpoint_url:
    lowerCAmelCase__    :    List[str]								=					[3, 3, 2_7, 3]
    lowerCAmelCase__    :    Optional[int]								=					[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
  if "huge" in checkpoint_url:
    lowerCAmelCase__    :    Dict								=					[3, 3, 2_7, 3]
    lowerCAmelCase__    :    Any								=					[3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

  lowerCAmelCase__    :    List[str]								=					1_0_0_0
  lowerCAmelCase__    :    List[Any]								=					"imagenet-1k-id2label.json"
  lowerCAmelCase__    :    int								=					(1, 1_0_0_0)

  lowerCAmelCase__    :    Optional[int]								=					"huggingface/label-files"
  lowerCAmelCase__    :    Union[str, Any]								=					num_labels
  lowerCAmelCase__    :    List[Any]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
  lowerCAmelCase__    :    str								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}

  lowerCAmelCase__    :    List[Any]								=					idalabel
  lowerCAmelCase__    :    int								=					{v: k for k, v in idalabel.items()}
  lowerCAmelCase__    :    Optional[int]								=					hidden_sizes
  lowerCAmelCase__    :    List[str]								=					depths

  return config, expected_shape





def     lowercase__	(  lowerCamelCase     :  Any				)					->     List[Any]:
  if "downsample_layers.0.0" in name:
    lowerCAmelCase__    :    List[Any]								=					name.replace("downsample_layers.0.0"				,	"embeddings.patch_embeddings"				)
  if "downsample_layers.0.1" in name:
    lowerCAmelCase__    :    int								=					name.replace("downsample_layers.0.1"				,	"embeddings.norm"				)  # we rename to layernorm later on
  if "downsample_layers.1.0" in name:
    lowerCAmelCase__    :    str								=					name.replace("downsample_layers.1.0"				,	"stages.1.downsampling_layer.0"				)
  if "downsample_layers.1.1" in name:
    lowerCAmelCase__    :    Dict								=					name.replace("downsample_layers.1.1"				,	"stages.1.downsampling_layer.1"				)
  if "downsample_layers.2.0" in name:
    lowerCAmelCase__    :    Any								=					name.replace("downsample_layers.2.0"				,	"stages.2.downsampling_layer.0"				)
  if "downsample_layers.2.1" in name:
    lowerCAmelCase__    :    Union[str, Any]								=					name.replace("downsample_layers.2.1"				,	"stages.2.downsampling_layer.1"				)
  if "downsample_layers.3.0" in name:
    lowerCAmelCase__    :    Union[str, Any]								=					name.replace("downsample_layers.3.0"				,	"stages.3.downsampling_layer.0"				)
  if "downsample_layers.3.1" in name:
    lowerCAmelCase__    :    List[str]								=					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.
    lowerCAmelCase__    :    Optional[Any]								=					name[: len("stages.0"				)] + ".layers" + name[len("stages.0"				) :]
  if "gamma" in name:
    lowerCAmelCase__    :    List[Any]								=					name.replace("gamma"				,	"weight"				)
  if "beta" in name:
    lowerCAmelCase__    :    List[Any]								=					name.replace("beta"				,	"bias"				)
  if "stages" in name:
    lowerCAmelCase__    :    Tuple								=					name.replace("stages"				,	"encoder.stages"				)
  if "norm" in name:
    lowerCAmelCase__    :    Optional[int]								=					name.replace("norm"				,	"layernorm"				)
  if "head" in name:
    lowerCAmelCase__    :    Optional[Any]								=					name.replace("head"				,	"classifier"				)

  return name





def     lowercase__	(  )					->     Optional[int]:
  lowerCAmelCase__    :    Optional[Any]								=					"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase__    :    Optional[Any]								=					Image.open(requests.get(lowerCamelCase				,	stream=lowerCamelCase				).raw				)
  return im





def     lowercase__	(  lowerCamelCase     :  str				)					->     Optional[int]:
  if "224" in checkpoint_url:
    lowerCAmelCase__    :    List[str]								=					2_2_4
    lowerCAmelCase__    :    Dict								=					2_2_4 / 2_5_6
  elif "384" in checkpoint_url:
    lowerCAmelCase__    :    Union[str, Any]								=					3_8_4
    lowerCAmelCase__    :    Union[str, Any]								=					None
  else:
    lowerCAmelCase__    :    List[Any]								=					5_1_2
    lowerCAmelCase__    :    List[str]								=					None

  return ConvNextImageProcessor(
      size=lowerCamelCase				,	crop_pct=lowerCamelCase				,	image_mean=[0.4_85, 0.4_56, 0.4_06]				,	image_std=[0.2_29, 0.2_24, 0.2_25]				,	resample=PILImageResampling.BICUBIC				,	)





@torch.no_grad()
def     lowercase__	(  lowerCamelCase     :  str				,	lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  List[Any]				,	lowerCamelCase     :  Optional[Any]				)					->     List[str]:
  print("Downloading original model from checkpoint..."				)
  # define ConvNeXTV2 configuration based on URL
  lowerCAmelCase__				,		lowerCAmelCase__    :    Union[str, Any]								=					get_convnextva_config(lowerCamelCase				)
  # load original state_dict from URL
  lowerCAmelCase__    :    Dict								=					torch.hub.load_state_dict_from_url(lowerCamelCase				)["model"]

  print("Converting model parameters..."				)
  # rename keys
  for key in state_dict.copy().keys():
    lowerCAmelCase__    :    int								=					state_dict.pop(lowerCamelCase				)
    lowerCAmelCase__    :    List[str]								=					val
  # add prefix to all keys expect classifier head
  for key in state_dict.copy().keys():
    lowerCAmelCase__    :    str								=					state_dict.pop(lowerCamelCase				)
    if not key.startswith("classifier"				):
      lowerCAmelCase__    :    int								=					"convnextv2." + key
    lowerCAmelCase__    :    Optional[Any]								=					val

  # load HuggingFace model
  lowerCAmelCase__    :    Dict								=					ConvNextVaForImageClassification(lowerCamelCase				)
  model.load_state_dict(lowerCamelCase				)
  model.eval()

  # Check outputs on an image, prepared by ConvNextImageProcessor
  lowerCAmelCase__    :    Tuple								=					convert_preprocessor(lowerCamelCase				)
  lowerCAmelCase__    :    List[Any]								=					preprocessor(images=prepare_img()				,	return_tensors="pt"				)
  lowerCAmelCase__    :    List[Any]								=					model(**lowerCamelCase				).logits

  # note: the logits below were obtained without center cropping
  if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
    lowerCAmelCase__    :    List[Any]								=					torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
    lowerCAmelCase__    :    int								=					torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
    lowerCAmelCase__    :    List[Any]								=					torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
    lowerCAmelCase__    :    Dict								=					torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
    lowerCAmelCase__    :    int								=					torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
    lowerCAmelCase__    :    List[str]								=					torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
    lowerCAmelCase__    :    Dict								=					torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
    lowerCAmelCase__    :    str								=					torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
    lowerCAmelCase__    :    List[Any]								=					torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
    lowerCAmelCase__    :    Dict								=					torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
    lowerCAmelCase__    :    Optional[Any]								=					torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
    lowerCAmelCase__    :    List[str]								=					torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
    lowerCAmelCase__    :    int								=					torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
    lowerCAmelCase__    :    Dict								=					torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
    lowerCAmelCase__    :    str								=					torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
    lowerCAmelCase__    :    Optional[Any]								=					torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09]				)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
    lowerCAmelCase__    :    Dict								=					torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26]				)
  else:
    raise ValueError(F"Unknown URL: {checkpoint_url}"				)

  assert torch.allclose(logits[0, :5]				,	lowerCamelCase				,	atol=1e-3				)
  assert logits.shape == expected_shape
  print("Model outputs match the original results!"				)

  if save_model:
    print("Saving model to local..."				)
    # Create folder to save model
    if not os.path.isdir(lowerCamelCase				):
      os.mkdir(lowerCamelCase				)

    model.save_pretrained(lowerCamelCase				)
    preprocessor.save_pretrained(lowerCamelCase				)

  lowerCAmelCase__    :    List[Any]								=					"convnextv2"
  if "atto" in checkpoint_url:
    model_name += "-atto"
  if "femto" in checkpoint_url:
    model_name += "-femto"
  if "pico" in checkpoint_url:
    model_name += "-pico"
  if "nano" in checkpoint_url:
    model_name += "-nano"
  elif "tiny" in checkpoint_url:
    model_name += "-tiny"
  elif "base" in checkpoint_url:
    model_name += "-base"
  elif "large" in checkpoint_url:
    model_name += "-large"
  elif "huge" in checkpoint_url:
    model_name += "-huge"
  if "22k" in checkpoint_url and "1k" not in checkpoint_url:
    model_name += "-22k"
  elif "22k" in checkpoint_url and "1k" in checkpoint_url:
    model_name += "-22k-1k"
  elif "1k" in checkpoint_url:
    model_name += "-1k"
  if "224" in checkpoint_url:
    model_name += "-224"
  elif "384" in checkpoint_url:
    model_name += "-384"
  elif "512" in checkpoint_url:
    model_name += "-512"

  if push_to_hub:
    print(F"Pushing {model_name} to the hub..."				)
    model.push_to_hub(lowerCamelCase				)
    preprocessor.push_to_hub(lowerCamelCase				)


if __name__ == "__main__":
   __UpperCAmelCase    				=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint_url",
       default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
       type=str,
       help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="model",
       type=str,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument("--save_model", action="store_true", help="Save model to local")
   parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

   __UpperCAmelCase    				=      parser.parse_args()
   convert_convnextva_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
   )




"""simple docstring"""





from __future__ import annotations





def     lowercase__	(  lowerCamelCase     :  list				,	lowerCamelCase     :  list				)					->     list:
  lowerCAmelCase__    :    Dict								=					[None] * (len(lowerCamelCase				) + len(lowerCamelCase				))

  lowerCAmelCase__    :    Optional[Any]								=					0  # pointer to current index for left Half
  lowerCAmelCase__    :    Any								=					0  # pointer to current index for the right Half
  lowerCAmelCase__    :    Dict								=					0  # pointer to current index for the sorted array Half

  while pointera < len(lowerCamelCase				) and pointera < len(lowerCamelCase				):
    if left_half[pointera] < right_half[pointera]:
      lowerCAmelCase__    :    Tuple								=					left_half[pointera]
      pointera += 1
      index += 1
    else:
      lowerCAmelCase__    :    int								=					right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Tuple								=					left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Optional[int]								=					right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array





def     lowercase__	(  lowerCamelCase     :  list				)					->     list:
  if len(lowerCamelCase				) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  lowerCAmelCase__    :    Optional[int]								=					0 + (len(lowerCamelCase				) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  lowerCAmelCase__    :    Union[str, Any]								=					array[:middle]
  lowerCAmelCase__    :    List[str]								=					array[middle:]

  return merge(merge_sort(lowerCamelCase				)				,	merge_sort(lowerCamelCase				)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
