'''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										= logging.get_logger(__name__)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Union[str, Any]  					=       ConvNextVaConfig()

     if "atto" in checkpoint_url:
          lowercase_       :							Union[str, Any]  					=       [2, 2, 6, 2]
          lowercase_       :							str  					=       [40, 80, 160, 320]
     if "femto" in checkpoint_url:
          lowercase_       :							str  					=       [2, 2, 6, 2]
          lowercase_       :							int  					=       [48, 96, 192, 384]
     if "pico" in checkpoint_url:
          lowercase_       :							List[str]  					=       [2, 2, 6, 2]
          lowercase_       :							Tuple  					=       [64, 128, 256, 512]
     if "nano" in checkpoint_url:
          lowercase_       :							Any  					=       [2, 2, 8, 2]
          lowercase_       :							Dict  					=       [80, 160, 320, 640]
     if "tiny" in checkpoint_url:
          lowercase_       :							Tuple  					=       [3, 3, 9, 3]
          lowercase_       :							List[str]  					=       [96, 192, 384, 768]
     if "base" in checkpoint_url:
          lowercase_       :							Union[str, Any]  					=       [3, 3, 27, 3]
          lowercase_       :							str  					=       [128, 256, 512, 1_024]
     if "large" in checkpoint_url:
          lowercase_       :							Optional[int]  					=       [3, 3, 27, 3]
          lowercase_       :							List[str]  					=       [192, 384, 768, 1_536]
     if "huge" in checkpoint_url:
          lowercase_       :							List[str]  					=       [3, 3, 27, 3]
          lowercase_       :							Optional[int]  					=       [352, 704, 1_408, 2_816]

     lowercase_       :							Any  					=       1_000
     lowercase_       :							Union[str, Any]  					=       'imagenet-1k-id2label.json'
     lowercase_       :							Tuple  					=       (1, 1_000)

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

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

     return config, expected_shape

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

     return name

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

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     if "224" in checkpoint_url:
          lowercase_       :							str  					=       224
          lowercase_       :							List[str]  					=       224 / 256
     elif "384" in checkpoint_url:
          lowercase_       :							Dict  					=       384
          lowercase_       :							List[str]  					=       None
     else:
          lowercase_       :							Optional[Any]  					=       512
          lowercase_       :							Any  					=       None

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

@torch.no_grad()
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     print('Downloading original model from checkpoint...'				)
     # define ConvNeXTV2 configuration based on URL
     lowercase_					,lowercase_       :							int  					=       get_convnextva_config(SCREAMING_SNAKE_CASE_				)
     # load original state_dict from URL
     lowercase_       :							List[Any]  					=       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				)['model']

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

     # load HuggingFace model
     lowercase_       :							Union[str, Any]  					=       ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE_				)
     model.load_state_dict(SCREAMING_SNAKE_CASE_				)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     lowercase_       :							str  					=       convert_preprocessor(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Union[str, Any]  					=       preprocessor(images=prepare_img()	,							return_tensors='pt'				)
     lowercase_       :							Optional[int]  					=       model(**SCREAMING_SNAKE_CASE_				).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":
          lowercase_       :							Dict  					=       torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
          lowercase_       :							Dict  					=       torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
          lowercase_       :							Any  					=       torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
          lowercase_       :							Dict  					=       torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
          lowercase_       :							Optional[int]  					=       torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
          lowercase_       :							Optional[int]  					=       torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
          lowercase_       :							Union[str, Any]  					=       torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
          lowercase_       :							Union[str, Any]  					=       torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
          lowercase_       :							Optional[Any]  					=       torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
          lowercase_       :							Union[str, Any]  					=       torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
          lowercase_       :							Any  					=       torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
          lowercase_       :							Union[str, Any]  					=       torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
          lowercase_       :							Optional[Any]  					=       torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
          lowercase_       :							Optional[Any]  					=       torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
          lowercase_       :							int  					=       torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
          lowercase_       :							List[str]  					=       torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
          lowercase_       :							List[str]  					=       torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]				)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
          lowercase_       :							Union[str, Any]  					=       torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]				)
     else:
          raise ValueError(f'''Unknown URL: {checkpoint_url}'''				)

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

          model.save_pretrained(SCREAMING_SNAKE_CASE_				)
          preprocessor.save_pretrained(SCREAMING_SNAKE_CASE_				)

     lowercase_       :							Tuple  					=       '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(SCREAMING_SNAKE_CASE_				)
          preprocessor.push_to_hub(SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
 _A										= 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										= parser.parse_args()
 convert_convnextva_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
 )





'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return " ".join([word[::-1] for word in input_str.split()]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




