


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






def       lowerCAmelCase__       (				lowerCamelCase     :			Dict   ):
       _A     :	Union[str, Any]      =					ConvNextVaConfig()

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

       _A     :	Any      =					1000
       _A     :	str      =					'imagenet-1k-id2label.json'
       _A     :	Any      =					(1, 1000)

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

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

       return config, expected_shape






def       lowerCAmelCase__       (				lowerCamelCase     :			Any   ):







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

       return name






def       lowerCAmelCase__       (				):
       _A     :	Optional[int]      =					'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A     :	Tuple      =					Image.open(requests.get(lowercase_  ,stream=lowercase_   ).raw   )
       return im






def       lowerCAmelCase__       (				lowerCamelCase     :			Union[str, Any]   ):







       if "224" in checkpoint_url:
              _A     :	str      =					224
              _A     :	List[str]      =					224 / 256
       elif "384" in checkpoint_url:
              _A     :	str      =					384
              _A     :	str      =					None
       else:
              _A     :	Any      =					512
              _A     :	Tuple      =					None

       return ConvNextImageProcessor(
           size=lowercase_  ,crop_pct=lowercase_  ,image_mean=[0.485, 0.456, 0.406]  ,image_std=[0.229, 0.224, 0.225]  ,resample=PILImageResampling.BICUBIC  ,)






@torch.no_grad()
def       lowerCAmelCase__       (				lowerCamelCase     :			Dict  ,lowerCamelCase     :			Any  ,lowerCamelCase     :			Optional[int]  ,lowerCamelCase     :			Any   ):
       print('Downloading original model from checkpoint...'   )
       # define ConvNeXTV2 configuration based on URL
       _A  , _A     :	Tuple      =					get_convnextva_config(lowercase_   )
       # load original state_dict from URL
       _A     :	Optional[Any]      =					torch.hub.load_state_dict_from_url(lowercase_   )['model']

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

       # load HuggingFace model
       _A     :	Optional[Any]      =					ConvNextVaForImageClassification(lowercase_   )
       model.load_state_dict(lowercase_   )
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       _A     :	str      =					convert_preprocessor(lowercase_   )
       _A     :	Optional[int]      =					preprocessor(images=prepare_img()  ,return_tensors='pt'   )
       _A     :	Tuple      =					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":
              _A     :	Tuple      =					torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              _A     :	Dict      =					torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              _A     :	Tuple      =					torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              _A     :	Dict      =					torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              _A     :	Optional[Any]      =					torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              _A     :	Any      =					torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              _A     :	List[Any]      =					torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              _A     :	Optional[Any]      =					torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              _A     :	Tuple      =					torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              _A     :	Dict      =					torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              _A     :	Any      =					torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              _A     :	Optional[Any]      =					torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              _A     :	Union[str, Any]      =					torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              _A     :	Dict      =					torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              _A     :	Any      =					torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              _A     :	Tuple      =					torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              _A     :	Optional[int]      =					torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]   )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              _A     :	Optional[Any]      =					torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]   )
       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_   )

       _A     :	Dict      =					'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      :     List[str]										= parser.parse_args()
   convert_convnextva_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
   )












'''simple docstring'''







def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		return "".join(chr(ord(lowercase_  ) + 32  ) if "A" <= char <= "Z" else char for char in word  )


if __name__ == "__main__":
						from doctest import testmod

						testmod()





