





'''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							:			List[str]				=logging.get_logger(__name__)





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
    A__       =    ConvNextConfig()

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

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

    A__       =    '''huggingface/label-files'''
    A__       =    num_labels
    A__       =    json.load(open(hf_hub_download(_lowerCamelCase		, _lowerCamelCase		, repo_type="dataset" )		, "r" ) )
    A__       =    {int(_lowerCamelCase ): 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]
    A__       =    idalabel
    A__       =    {v: k for k, v in idalabel.items()}
    A__       =    hidden_sizes
    A__       =    depths

    return config, expected_shape





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

    return name





def 		UpperCamelCase       (							):
    A__       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__       =    Image.open(requests.get(_lowerCamelCase		, stream=_lowerCamelCase ).raw )
    return im





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

    # load HuggingFace model
    A__       =    ConvNextForImageClassification(_lowerCamelCase )
    model.load_state_dict(_lowerCamelCase )
    model.eval()

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

    A__       =    model(_lowerCamelCase ).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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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__       =    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]		, _lowerCamelCase		, atol=1e-3 )
    assert logits.shape == expected_shape

    Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
    print(F"Saving model to {pytorch_dump_folder_path}" )
    model.save_pretrained(_lowerCamelCase )
    print(F"Saving image processor to {pytorch_dump_folder_path}" )
    image_processor.save_pretrained(_lowerCamelCase )

    print("Pushing model to the hub..." )
    A__       =    '''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(_lowerCamelCase		, _lowerCamelCase )		, organization="nielsr"		, commit_message="Add model"		, )


if __name__ == "__main__":
  __lowerCAmelCase							:			Optional[int]				=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							:			List[str]				=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_vilt import ViltImageProcessor


__lowerCAmelCase							:			Dict				=logging.get_logger(__name__)






class      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :List[str]		, *lowercase_   :Dict		, **lowercase_   :int							)->							None:
     warnings.warn(
         "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use ViltImageProcessor instead."		, lowercase_		, )
     super().__init__(*lowercase_		, **lowercase_							)



