


"""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()
__lowercase          = logging.get_logger(__name__)


def 	lowercase     (							A_			)-> Optional[Any]:
  '''simple docstring'''







  a		:						str 				=     ConvNextConfig()

  if "tiny" in checkpoint_url:
    a		:						int 				=     [3, 3, 9, 3]
    a		:						List[Any] 				=     [96, 192, 384, 768]
  if "small" in checkpoint_url:
    a		:						Any 				=     [3, 3, 27, 3]
    a		:						List[Any] 				=     [96, 192, 384, 768]
  if "base" in checkpoint_url:
    a		:						Tuple 				=     [3, 3, 27, 3]
    a		:						Any 				=     [128, 256, 512, 1_024]
  if "large" in checkpoint_url:
    a		:						List[str] 				=     [3, 3, 27, 3]
    a		:						Optional[Any] 				=     [192, 384, 768, 1_536]
  if "xlarge" in checkpoint_url:
    a		:						int 				=     [3, 3, 27, 3]
    a		:						str 				=     [256, 512, 1_024, 2_048]

  if "1k" in checkpoint_url:
    a		:						Optional[Any] 				=     1_000
    a		:						List[Any] 				=     "imagenet-1k-id2label.json"
    a		:						Dict 				=     (1, 1_000)
  else:
    a		:						int 				=     21_841
    a		:						int 				=     "imagenet-22k-id2label.json"
    a		:						Optional[int] 				=     (1, 21_841)

  a		:						Dict 				=     "huggingface/label-files"
  a		:						str 				=     num_labels
  a		:						Any 				=     json.load(open(hf_hub_download(A_    ,  A_    ,  repo_type="dataset"			)    ,  "r"			)			)
  a		:						Any 				=     {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[9_205]
    del idalabel[15_027]
  a		:						Tuple 				=     idalabel
  a		:						Optional[int] 				=     {v: k for k, v in idalabel.items()}
  a		:						Union[str, Any] 				=     hidden_sizes
  a		:						Tuple 				=     depths

  return config, expected_shape


def 	lowercase     (							A_			)-> Union[str, Any]:
  '''simple docstring'''







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

  return name


def 	lowercase     (							)-> Dict:
  '''simple docstring'''







  a		:						List[str] 				=     "http://images.cocodataset.org/val2017/000000039769.jpg"
  a		:						Dict 				=     Image.open(requests.get(A_    ,  stream=A_			).raw			)
  return im


@torch.no_grad()
def 	lowercase     (							A_    ,  A_			)-> Dict:
  '''simple docstring'''







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

  # load HuggingFace model
  a		:						Optional[Any] 				=     ConvNextForImageClassification(A_			)
  model.load_state_dict(A_			)
  model.eval()

  # Check outputs on an image, prepared by ConvNextImageProcessor
  a		:						Tuple 				=     224 if "224" in checkpoint_url else 384
  a		:						Optional[int] 				=     ConvNextImageProcessor(size=A_			)
  a		:						List[Any] 				=     image_processor(images=prepare_img()    ,  return_tensors="pt"			).pixel_values

  a		:						str 				=     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":
    a		:						int 				=     torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
    a		:						Optional[Any] 				=     torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
    a		:						Any 				=     torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
    a		:						Dict 				=     torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
    a		:						List[Any] 				=     torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
    a		:						Any 				=     torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
    a		:						int 				=     torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
    a		:						Dict 				=     torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
    a		:						Union[str, Any] 				=     torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
    a		:						List[str] 				=     torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
    a		:						Optional[Any] 				=     torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
    a		:						str 				=     torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
    a		:						Optional[Any] 				=     torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
    a		:						Tuple 				=     torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]			)
  elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
    a		:						int 				=     torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]			)
  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..."			)
  a		:						str 				=     "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__":
 __lowercase          = 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.""",
 )

 __lowercase          = parser.parse_args()
 convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""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)






