
'''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()
a_     =  logging.get_logger(__name__)

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      int            =  ConvNextConfig()

    if "tiny" in checkpoint_url:
        snake_case_	:      Dict            =  [3, 3, 9, 3]
        snake_case_	:      Union[str, Any]            =  [9_6, 1_9_2, 3_8_4, 7_6_8]
    if "small" in checkpoint_url:
        snake_case_	:      Optional[Any]            =  [3, 3, 2_7, 3]
        snake_case_	:      List[Any]            =  [9_6, 1_9_2, 3_8_4, 7_6_8]
    if "base" in checkpoint_url:
        snake_case_	:      Optional[int]            =  [3, 3, 2_7, 3]
        snake_case_	:      int            =  [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
    if "large" in checkpoint_url:
        snake_case_	:      Optional[int]            =  [3, 3, 2_7, 3]
        snake_case_	:      int            =  [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
    if "xlarge" in checkpoint_url:
        snake_case_	:      int            =  [3, 3, 2_7, 3]
        snake_case_	:      str            =  [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

    if "1k" in checkpoint_url:
        snake_case_	:      Optional[Any]            =  1_0_0_0
        snake_case_	:      str            =  "imagenet-1k-id2label.json"
        snake_case_	:      str            =  (1, 1_0_0_0)
    else:
        snake_case_	:      Union[str, Any]            =  2_1_8_4_1
        snake_case_	:      List[Any]            =  "imagenet-22k-id2label.json"
        snake_case_	:      Optional[int]            =  (1, 2_1_8_4_1)

    snake_case_	:      Optional[int]            =  "huggingface/label-files"
    snake_case_	:      List[str]            =  num_labels
    snake_case_	:      Tuple            =  json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						repo_type="dataset"    ),						"r"    )    )
    snake_case_	:      Optional[Any]            =  {int(__SCREAMING_SNAKE_CASE    ): 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_2_0_5]
        del idalabel[1_5_0_2_7]
    snake_case_	:      Union[str, Any]            =  idalabel
    snake_case_	:      Optional[int]            =  {v: k for k, v in idalabel.items()}
    snake_case_	:      Tuple            =  hidden_sizes
    snake_case_	:      Union[str, Any]            =  depths

    return config, expected_shape

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

    return name

def 				UpperCamelCase_       (		):
    """simple docstring"""






    snake_case_	:      List[Any]            =  "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case_	:      Optional[int]            =  Image.open(requests.get(__SCREAMING_SNAKE_CASE,						stream=__SCREAMING_SNAKE_CASE    ).raw    )
    return im

@torch.no_grad()
def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

    # load HuggingFace model
    snake_case_	:      Any            =  ConvNextForImageClassification(__SCREAMING_SNAKE_CASE    )
    model.load_state_dict(__SCREAMING_SNAKE_CASE    )
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    snake_case_	:      Optional[int]            =  2_2_4 if "224" in checkpoint_url else 3_8_4
    snake_case_	:      List[Any]            =  ConvNextImageProcessor(size=__SCREAMING_SNAKE_CASE    )
    snake_case_	:      Optional[int]            =  image_processor(images=prepare_img(),						return_tensors="pt"    ).pixel_values

    snake_case_	:      int            =  model(__SCREAMING_SNAKE_CASE    ).logits

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

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

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

    print("Pushing model to the hub..."    )
    snake_case_	:      Any            =  "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(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ),						organization="nielsr",						commit_message="Add model",						)


if __name__ == "__main__":
  a_     =  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.",
  )

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






'''simple docstring'''
from __future__ import annotations

from typing import Any






class 				UpperCAmelCase_			:





 def __init__(      self	,    lowercase_	,    lowercase_):  # noqa: A002
     snake_case_	:      List[str]            =  item
     snake_case_	:      Dict            =  next






class 				UpperCAmelCase_			:





 def __init__(      self):
     snake_case_	:      Node | None            =  None
     snake_case_	:      List[str]            =  0





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      List[Any]            =  Node(lowercase_	,    self.head)
     self.size += 1





 def     snake_case__						(      self):
     # Switched 'self.is_empty()' to 'self.head is None'
     # because mypy was considering the possibility that 'self.head'
     # can be None in below else part and giving error
     if self.head is None:
         return None
     else:
         snake_case_	:      List[Any]            =  self.head.item
         snake_case_	:      Any            =  self.head.next
         self.size -= 1
         return item





 def     snake_case__						(      self):
     return self.head is None





 def __str__(      self):
     if self.is_empty():
         return ""
     else:
         snake_case_	:      Dict            =  self.head
         snake_case_	:      Optional[Any]            =  ""
         snake_case_	:      list[str]            =  []
         while iterate:
             item_list.append(str(iterate.item))
             snake_case_	:      List[str]            =  iterate.next

         snake_case_	:      Optional[Any]            =  " --> ".join(lowercase_)

         return item_str





 def __len__(      self):
     return self.size




