

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()
UpperCamelCase__     :					Dict								=			logging.get_logger(__name__)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Any				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  ConvNextVaConfig()

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

    SCREAMING_SNAKE_CASE_													=  1_000
    SCREAMING_SNAKE_CASE_													=  'imagenet-1k-id2label.json'
    SCREAMING_SNAKE_CASE_													=  (1, 1_000)

    SCREAMING_SNAKE_CASE_													=  'huggingface/label-files'
    SCREAMING_SNAKE_CASE_													=  num_labels
    SCREAMING_SNAKE_CASE_													=  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      repo_type='dataset'				)       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE_													=  idalabel
    SCREAMING_SNAKE_CASE_													=  {v: k for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE_													=  hidden_sizes
    SCREAMING_SNAKE_CASE_													=  depths

    return config, expected_shape


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						str				):





    """simple docstring"""


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

    return name


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_													=  Image.open(requests.get(_SCREAMING_SNAKE_CASE       ,      stream=_SCREAMING_SNAKE_CASE				).raw				)
    return im


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    if "224" in checkpoint_url:
        SCREAMING_SNAKE_CASE_													=  224
        SCREAMING_SNAKE_CASE_													=  224 / 256
    elif "384" in checkpoint_url:
        SCREAMING_SNAKE_CASE_													=  384
        SCREAMING_SNAKE_CASE_													=  None
    else:
        SCREAMING_SNAKE_CASE_													=  512
        SCREAMING_SNAKE_CASE_													=  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    :						Dict       ,      _SCREAMING_SNAKE_CASE    :						Optional[Any]       ,      _SCREAMING_SNAKE_CASE    :						List[Any]       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    print('Downloading original model from checkpoint...'				)
    # define ConvNeXTV2 configuration based on URL
    SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  get_convnextva_config(_SCREAMING_SNAKE_CASE				)
    # load original state_dict from URL
    SCREAMING_SNAKE_CASE_													=  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE				)['model']

    print('Converting model parameters...'				)
    # rename keys
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_													=  state_dict.pop(_SCREAMING_SNAKE_CASE				)
        SCREAMING_SNAKE_CASE_													=  val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_													=  state_dict.pop(_SCREAMING_SNAKE_CASE				)
        if not key.startswith('classifier'				):
            SCREAMING_SNAKE_CASE_													=  'convnextv2.' + key
        SCREAMING_SNAKE_CASE_													=  val

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_													=  ConvNextVaForImageClassification(_SCREAMING_SNAKE_CASE				)
    model.load_state_dict(_SCREAMING_SNAKE_CASE				)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    SCREAMING_SNAKE_CASE_													=  convert_preprocessor(_SCREAMING_SNAKE_CASE				)
    SCREAMING_SNAKE_CASE_													=  preprocessor(images=prepare_img()       ,      return_tensors='pt'				)
    SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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":
        SCREAMING_SNAKE_CASE_													=  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				)

    SCREAMING_SNAKE_CASE_													=  '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__":
       UpperCamelCase__     :					List[Any]								=			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")

       UpperCamelCase__     :					int								=			parser.parse_args()
       convert_convnextva_checkpoint(
           args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
       )





def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						str				):





    """simple docstring"""


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


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

       testmod()


