



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()
UpperCamelCase													=		logging.get_logger(__name__)

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]):
    lowercase__				:     List[str]					      =       ConvNextConfig()

    if "tiny" in checkpoint_url:
        lowercase__				:     Tuple					      =       [3, 3, 9, 3]
        lowercase__				:     str					      =       [96, 192, 384, 768]
    if "small" in checkpoint_url:
        lowercase__				:     Optional[Any]					      =       [3, 3, 27, 3]
        lowercase__				:     int					      =       [96, 192, 384, 768]
    if "base" in checkpoint_url:
        lowercase__				:     int					      =       [3, 3, 27, 3]
        lowercase__				:     List[Any]					      =       [128, 256, 512, 1024]
    if "large" in checkpoint_url:
        lowercase__				:     Dict					      =       [3, 3, 27, 3]
        lowercase__				:     Optional[int]					      =       [192, 384, 768, 1536]
    if "xlarge" in checkpoint_url:
        lowercase__				:     str					      =       [3, 3, 27, 3]
        lowercase__				:     Optional[Any]					      =       [256, 512, 1024, 2048]

    if "1k" in checkpoint_url:
        lowercase__				:     int					      =       1000
        lowercase__				:     Any					      =       "imagenet-1k-id2label.json"
        lowercase__				:     Optional[int]					      =       (1, 1000)
    else:
        lowercase__				:     Dict					      =       2_1841
        lowercase__				:     Dict					      =       "imagenet-22k-id2label.json"
        lowercase__				:     Tuple					      =       (1, 2_1841)

    lowercase__				:     Tuple					      =       "huggingface/label-files"
    lowercase__				:     Tuple					      =       num_labels
    lowercase__				:     List[str]					      =       json.load(open(hf_hub_download(_lowerCamelCase	,			_lowerCamelCase	,			repo_type="dataset")	,			"r"))
    lowercase__				:     Union[str, Any]					      =       {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[9205]
        del idalabel[1_5027]
    lowercase__				:     Any					      =       idalabel
    lowercase__				:     Union[str, Any]					      =       {v: k for k, v in idalabel.items()}
    lowercase__				:     Optional[int]					      =       hidden_sizes
    lowercase__				:     List[Any]					      =       depths

    return config, expected_shape

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

    return name

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

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

    # load HuggingFace model
    lowercase__				:     List[str]					      =       ConvNextForImageClassification(_lowerCamelCase)
    model.load_state_dict(_lowerCamelCase)
    model.eval()

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

    lowercase__				:     str					      =       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":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([-0.1210, -0.6605, 0.1918])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
        lowercase__				:     int					      =       torch.tensor([-0.4473, -0.1847, -0.6365])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
        lowercase__				:     Any					      =       torch.tensor([0.4525, 0.7539, 0.0308])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
        lowercase__				:     List[str]					      =       torch.tensor([0.3561, 0.6350, -0.0384])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
        lowercase__				:     List[str]					      =       torch.tensor([0.4174, -0.0989, 0.1489])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([0.2513, -0.1349, -0.1613])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([1.2980, 0.3631, -0.1198])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([1.2963, 0.1227, 0.1723])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
        lowercase__				:     str					      =       torch.tensor([1.7956, 0.8390, 0.2820])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
        lowercase__				:     Optional[int]					      =       torch.tensor([-0.2822, -0.0502, -0.0878])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
        lowercase__				:     List[str]					      =       torch.tensor([-0.5672, -0.0730, -0.4348])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
        lowercase__				:     Tuple					      =       torch.tensor([0.2681, 0.2365, 0.6246])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
        lowercase__				:     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":
        lowercase__				:     Optional[int]					      =       torch.tensor([-0.6677, -0.1873, -0.8379])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
        lowercase__				:     Optional[int]					      =       torch.tensor([-0.7749, -0.2967, -0.6444])
    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...")
    lowercase__				:     Dict					      =       "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__":
      UpperCamelCase													=		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.''',
      )

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




from typing import TYPE_CHECKING

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


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

      UpperCamelCase													=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



