"""simple docstring"""


import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_:       Dict			     =       logging.get_logger(__name__)







def        __a (		A   ):






     '''simple docstring'''



     lowercase__								=				ConvNextConfig()

     if "tiny" in checkpoint_url:
          lowercase__								=				[3, 3, 9, 3]
          lowercase__								=				[96, 1_92, 3_84, 7_68]
     if "small" in checkpoint_url:
          lowercase__								=				[3, 3, 27, 3]
          lowercase__								=				[96, 1_92, 3_84, 7_68]
     if "base" in checkpoint_url:
          lowercase__								=				[3, 3, 27, 3]
          lowercase__								=				[1_28, 2_56, 5_12, 10_24]
     if "large" in checkpoint_url:
          lowercase__								=				[3, 3, 27, 3]
          lowercase__								=				[1_92, 3_84, 7_68, 15_36]
     if "xlarge" in checkpoint_url:
          lowercase__								=				[3, 3, 27, 3]
          lowercase__								=				[2_56, 5_12, 10_24, 20_48]

     if "1k" in checkpoint_url:
          lowercase__								=				10_00
          lowercase__								=				"imagenet-1k-id2label.json"
          lowercase__								=				(1, 10_00)
     else:
          lowercase__								=				2_18_41
          lowercase__								=				"imagenet-22k-id2label.json"
          lowercase__								=				(1, 2_18_41)

     lowercase__								=				"huggingface/label-files"
     lowercase__								=				num_labels
     lowercase__								=				json.load(open(hf_hub_download(A     ,					A     ,					repo_type="dataset"   )     ,					"r"   )   )
     lowercase__								=				{int(A   ): v for k, v in idalabel.items()}
     if "1k" not in checkpoint_url:
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[92_05]
          del idalabel[1_50_27]
     lowercase__								=				idalabel
     lowercase__								=				{v: k for k, v in idalabel.items()}
     lowercase__								=				hidden_sizes
     lowercase__								=				depths

     return config, expected_shape







def        __a (		A   ):






     '''simple docstring'''



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

     return name







def        __a (		):






     '''simple docstring'''



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







@torch.no_grad()
def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				get_convnext_config(A   )
     # load original state_dict from URL
     lowercase__								=				torch.hub.load_state_dict_from_url(A   )["model"]
     # rename keys
     for key in state_dict.copy().keys():
          lowercase__								=				state_dict.pop(A   )
          lowercase__								=				val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          lowercase__								=				state_dict.pop(A   )
          if not key.startswith("classifier"   ):
               lowercase__								=				"convnext." + key
          lowercase__								=				val

     # load HuggingFace model
     lowercase__								=				ConvNextForImageClassification(A   )
     model.load_state_dict(A   )
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     lowercase__								=				2_24 if "224" in checkpoint_url else 3_84
     lowercase__								=				ConvNextImageProcessor(size=A   )
     lowercase__								=				image_processor(images=prepare_img()     ,					return_tensors="pt"   ).pixel_values

     lowercase__								=				model(A   ).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          lowercase__								=				torch.tensor([-0.1210, -0.6605, 0.1918]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          lowercase__								=				torch.tensor([-0.4473, -0.1847, -0.6365]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          lowercase__								=				torch.tensor([0.4525, 0.7539, 0.0308]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          lowercase__								=				torch.tensor([0.3561, 0.6350, -0.0384]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          lowercase__								=				torch.tensor([0.4174, -0.0989, 0.1489]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          lowercase__								=				torch.tensor([0.2513, -0.1349, -0.1613]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          lowercase__								=				torch.tensor([1.2980, 0.3631, -0.1198]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          lowercase__								=				torch.tensor([1.2963, 0.1227, 0.1723]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          lowercase__								=				torch.tensor([1.7956, 0.8390, 0.2820]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          lowercase__								=				torch.tensor([-0.2822, -0.0502, -0.0878]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          lowercase__								=				torch.tensor([-0.5672, -0.0730, -0.4348]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          lowercase__								=				torch.tensor([0.2681, 0.2365, 0.6246]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          lowercase__								=				torch.tensor([-0.2642, 0.3931, 0.5116]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          lowercase__								=				torch.tensor([-0.6677, -0.1873, -0.8379]   )
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          lowercase__								=				torch.tensor([-0.7749, -0.2967, -0.6444]   )
     else:
          raise ValueError(f'''Unknown URL: {checkpoint_url}'''   )

     assert torch.allclose(logits[0, :3]     ,					A     ,					atol=1e-3   )
     assert logits.shape == expected_shape

     Path(A   ).mkdir(exist_ok=A   )
     print(f'''Saving model to {pytorch_dump_folder_path}'''   )
     model.save_pretrained(A   )
     print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
     image_processor.save_pretrained(A   )

     print("Pushing model to the hub..."   )
     lowercase__								=				"convnext"
     if "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "small" in checkpoint_url:
          model_name += "-small"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
     elif "large" in checkpoint_url:
          model_name += "-large"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"

     model.push_to_hub(
         repo_path_or_name=Path(A     ,					A   )     ,					organization="nielsr"     ,					commit_message="Add model"     ,					)


if __name__ == "__main__":
 lowerCAmelCase_:       Union[str, Any]			     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
     type=str,
     help="URL of the original ConvNeXT checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     required=True,
     help="Path to the output PyTorch model directory.",
 )

 lowerCAmelCase_:       Dict			     =       parser.parse_args()
 convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_:       Optional[int]			     =       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Any			     =       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[Any]			     =       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 lowerCAmelCase_:       List[Any]			     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




