





'''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()
a__							:							Union[str, Any]			=logging.get_logger(__name__)
def     lowercase__       (       __lowercase						:  Union[str, Any]					)			->				Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase	=  ConvNextVaConfig()

       if "atto" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [40, 80, 160, 320]
       if "femto" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [48, 96, 192, 384]
       if "pico" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [64, 128, 256, 512]
       if "nano" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 8, 2]
              __UpperCamelCase	=  [80, 160, 320, 640]
       if "tiny" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 9, 3]
              __UpperCamelCase	=  [96, 192, 384, 768]
       if "base" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [128, 256, 512, 1024]
       if "large" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [192, 384, 768, 1536]
       if "huge" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [352, 704, 1408, 2816]

       __UpperCamelCase	=  1000
       __UpperCamelCase	=  'imagenet-1k-id2label.json'
       __UpperCamelCase	=  (1, 1000)

       __UpperCamelCase	=  'huggingface/label-files'
       __UpperCamelCase	=  num_labels
       __UpperCamelCase	=  json.load(open(hf_hub_download(__lowercase    ,  __lowercase    ,  repo_type='dataset'					)    ,  'r'					)					)
       __UpperCamelCase	=  {int(__lowercase					): 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     lowercase__       (       __lowercase						:  Tuple					)			->				Optional[Any]:



       """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     lowercase__       (       )			->				Any:



       """simple docstring"""






       __UpperCamelCase	=  'http://images.cocodataset.org/val2017/000000039769.jpg'
       __UpperCamelCase	=  Image.open(requests.get(__lowercase    ,  stream=__lowercase					).raw					)
       return im
def     lowercase__       (       __lowercase						:  Optional[Any]					)			->				Dict:



       """simple docstring"""






       if "224" in checkpoint_url:
              __UpperCamelCase	=  224
              __UpperCamelCase	=  224 / 256
       elif "384" in checkpoint_url:
              __UpperCamelCase	=  384
              __UpperCamelCase	=  None
       else:
              __UpperCamelCase	=  512
              __UpperCamelCase	=  None

       return ConvNextImageProcessor(
           size=__lowercase    ,  crop_pct=__lowercase    ,  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     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict					)			->				Optional[int]:



       """simple docstring"""






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

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

       # load HuggingFace model
       __UpperCamelCase	=  ConvNextVaForImageClassification(__lowercase					)
       model.load_state_dict(__lowercase					)
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       __UpperCamelCase	=  convert_preprocessor(__lowercase					)
       __UpperCamelCase	=  preprocessor(images=prepare_img()    ,  return_tensors='pt'					)
       __UpperCamelCase	=  model(**__lowercase					).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]    ,  __lowercase    ,  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(__lowercase					):
                     os.mkdir(__lowercase					)

              model.save_pretrained(__lowercase					)
              preprocessor.save_pretrained(__lowercase					)

       __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(__lowercase					)
              preprocessor.push_to_hub(__lowercase					)


if __name__ == "__main__":
    a__							:							str			=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''')

    a__							:							Optional[Any]			=parser.parse_args()
    convert_convnextva_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
    )







'''simple docstring'''
a__							:							int			='''
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a__							:							List[Any]			=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a__							:							Dict			={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
