


'''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()
__lowercase:		Optional[Any]    						=  logging.get_logger(__name__)



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any] )		->       List[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    ConvNextVaConfig()

   if "atto" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 6, 2]
      UpperCamelCase__			    =    [40, 80, 1_60, 3_20]
   if "femto" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 6, 2]
      UpperCamelCase__			    =    [48, 96, 1_92, 3_84]
   if "pico" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 6, 2]
      UpperCamelCase__			    =    [64, 1_28, 2_56, 5_12]
   if "nano" in checkpoint_url:
      UpperCamelCase__			    =    [2, 2, 8, 2]
      UpperCamelCase__			    =    [80, 1_60, 3_20, 6_40]
   if "tiny" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 9, 3]
      UpperCamelCase__			    =    [96, 1_92, 3_84, 7_68]
   if "base" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 27, 3]
      UpperCamelCase__			    =    [1_28, 2_56, 5_12, 10_24]
   if "large" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 27, 3]
      UpperCamelCase__			    =    [1_92, 3_84, 7_68, 15_36]
   if "huge" in checkpoint_url:
      UpperCamelCase__			    =    [3, 3, 27, 3]
      UpperCamelCase__			    =    [3_52, 7_04, 14_08, 28_16]

   UpperCamelCase__			    =    10_00
   UpperCamelCase__			    =    "imagenet-1k-id2label.json"
   UpperCamelCase__			    =    (1, 10_00)

   UpperCamelCase__			    =    "huggingface/label-files"
   UpperCamelCase__			    =    num_labels
   UpperCamelCase__			    =    json.load(open(hf_hub_download(_UpperCamelCase     ,		_UpperCamelCase     ,		repo_type="dataset" )     ,		"r" ) )
   UpperCamelCase__			    =    {int(_UpperCamelCase ): v for k, v in idalabel.items()}

   UpperCamelCase__			    =    idalabel
   UpperCamelCase__			    =    {v: k for k, v in idalabel.items()}
   UpperCamelCase__			    =    hidden_sizes
   UpperCamelCase__			    =    depths

   return config, expected_shape



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple )		->       List[str]:





   '''simple docstring'''







   if "downsample_layers.0.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.0.0"     ,		"embeddings.patch_embeddings" )
   if "downsample_layers.0.1" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.0.1"     ,		"embeddings.norm" )  # we rename to layernorm later on
   if "downsample_layers.1.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.1.0"     ,		"stages.1.downsampling_layer.0" )
   if "downsample_layers.1.1" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.1.1"     ,		"stages.1.downsampling_layer.1" )
   if "downsample_layers.2.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.2.0"     ,		"stages.2.downsampling_layer.0" )
   if "downsample_layers.2.1" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.2.1"     ,		"stages.2.downsampling_layer.1" )
   if "downsample_layers.3.0" in name:
      UpperCamelCase__			    =    name.replace("downsample_layers.3.0"     ,		"stages.3.downsampling_layer.0" )
   if "downsample_layers.3.1" in name:
      UpperCamelCase__			    =    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.
      UpperCamelCase__			    =    name[: len("stages.0" )] + ".layers" + name[len("stages.0" ) :]
   if "gamma" in name:
      UpperCamelCase__			    =    name.replace("gamma"     ,		"weight" )
   if "beta" in name:
      UpperCamelCase__			    =    name.replace("beta"     ,		"bias" )
   if "stages" in name:
      UpperCamelCase__			    =    name.replace("stages"     ,		"encoder.stages" )
   if "norm" in name:
      UpperCamelCase__			    =    name.replace("norm"     ,		"layernorm" )
   if "head" in name:
      UpperCamelCase__			    =    name.replace("head"     ,		"classifier" )

   return name



def 							SCREAMING_SNAKE_CASE__(		)		->       Any:





   '''simple docstring'''







   UpperCamelCase__			    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCamelCase__			    =    Image.open(requests.get(_UpperCamelCase     ,		stream=_UpperCamelCase ).raw )
   return im



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any )		->       str:





   '''simple docstring'''







   if "224" in checkpoint_url:
      UpperCamelCase__			    =    2_24
      UpperCamelCase__			    =    2_24 / 2_56
   elif "384" in checkpoint_url:
      UpperCamelCase__			    =    3_84
      UpperCamelCase__			    =    None
   else:
      UpperCamelCase__			    =    5_12
      UpperCamelCase__			    =    None

   return ConvNextImageProcessor(
       size=_UpperCamelCase     ,		crop_pct=_UpperCamelCase     ,		image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]     ,		image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]     ,		resample=PILImageResampling.BICUBIC     ,		)



@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Optional[Any]     ,		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Dict )		->       Union[str, Any]:





   '''simple docstring'''







   print("Downloading original model from checkpoint..." )
   # define ConvNeXTV2 configuration based on URL
   UpperCamelCase__    ,		UpperCamelCase__			    =    get_convnextva_config(_UpperCamelCase )
   # load original state_dict from URL
   UpperCamelCase__			    =    torch.hub.load_state_dict_from_url(_UpperCamelCase )["model"]

   print("Converting model parameters..." )
   # rename keys
   for key in state_dict.copy().keys():
      UpperCamelCase__			    =    state_dict.pop(_UpperCamelCase )
      UpperCamelCase__			    =    val
   # add prefix to all keys expect classifier head
   for key in state_dict.copy().keys():
      UpperCamelCase__			    =    state_dict.pop(_UpperCamelCase )
      if not key.startswith("classifier" ):
         UpperCamelCase__			    =    "convnextv2." + key
      UpperCamelCase__			    =    val

   # load HuggingFace model
   UpperCamelCase__			    =    ConvNextVaForImageClassification(_UpperCamelCase )
   model.load_state_dict(_UpperCamelCase )
   model.eval()

   # Check outputs on an image, prepared by ConvNextImageProcessor
   UpperCamelCase__			    =    convert_preprocessor(_UpperCamelCase )
   UpperCamelCase__			    =    preprocessor(images=prepare_img()     ,		return_tensors="pt" )
   UpperCamelCase__			    =    model(**_UpperCamelCase ).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":
      UpperCamelCase__			    =    torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9] )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
      UpperCamelCase__			    =    torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6] )
   else:
      raise ValueError(F'Unknown URL: {checkpoint_url}' )

   assert torch.allclose(logits[0, :5]     ,		_UpperCamelCase     ,		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(_UpperCamelCase ):
         os.mkdir(_UpperCamelCase )

      model.save_pretrained(_UpperCamelCase )
      preprocessor.save_pretrained(_UpperCamelCase )

   UpperCamelCase__			    =    "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(_UpperCamelCase )
      preprocessor.push_to_hub(_UpperCamelCase )


if __name__ == "__main__":
       __lowercase:		Union[str, Any]    						=  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")

       __lowercase:		Tuple    						=  parser.parse_args()
       convert_convnextva_checkpoint(
           args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
       )



"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






