
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 1_8, 2]
      UpperCamelCase__    :   Optional[Any]					=				True if '''large''' in model_name or '''huge''' in model_name else False
      UpperCamelCase__    :   Optional[int]					=				True if '''large''' in model_name or '''huge''' in model_name else False
      UpperCamelCase__    :   Optional[int]					=				True if '''large''' in model_name or '''huge''' in model_name else False

      if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
            if "fl3" in model_name:
                  UpperCamelCase__    :   Dict					=				[3, 3, 3, 3]
                  UpperCamelCase__    :   Union[str, Any]					=				[5, 5, 5, 5]
            elif "fl4" in model_name:
                  UpperCamelCase__    :   Union[str, Any]					=				[4, 4, 4, 4]
                  UpperCamelCase__    :   Optional[int]					=				[3, 3, 3, 3]

      if "tiny" in model_name or "small" in model_name or "base" in model_name:
            UpperCamelCase__    :   Tuple					=				[3, 3, 3, 3]
            if "lrf" in model_name:
                  UpperCamelCase__    :   List[str]					=				[3, 3, 3, 3]
            else:
                  UpperCamelCase__    :   Optional[int]					=				[2, 2, 2, 2]

      if "tiny" in model_name:
            UpperCamelCase__    :   int					=				9_6
      elif "small" in model_name:
            UpperCamelCase__    :   Optional[Any]					=				9_6
      elif "base" in model_name:
            UpperCamelCase__    :   Tuple					=				1_2_8
      elif "large" in model_name:
            UpperCamelCase__    :   List[str]					=				1_9_2
      elif "xlarge" in model_name:
            UpperCamelCase__    :   Optional[int]					=				2_5_6
      elif "huge" in model_name:
            UpperCamelCase__    :   Any					=				3_5_2

      # set label information
      UpperCamelCase__    :   str					=				'''huggingface/label-files'''
      if "large" in model_name or "huge" in model_name:
            UpperCamelCase__    :   str					=				'''imagenet-22k-id2label.json'''
      else:
            UpperCamelCase__    :   Optional[Any]					=				'''imagenet-1k-id2label.json'''

      UpperCamelCase__    :   Optional[Any]					=				json.load(open(hf_hub_download(UpperCamelCase__ ,		UpperCamelCase__ ,		repo_type='''dataset'''		) ,		'''r'''		)		)
      UpperCamelCase__    :   List[Any]					=				{int(UpperCamelCase__		): v for k, v in idalabel.items()}
      UpperCamelCase__    :   Union[str, Any]					=				{v: k for k, v in idalabel.items()}

      UpperCamelCase__    :   Dict					=				FocalNetConfig(
          embed_dim=UpperCamelCase__ ,		depths=UpperCamelCase__ ,		focal_levels=UpperCamelCase__ ,		focal_windows=UpperCamelCase__ ,		use_conv_embed=UpperCamelCase__ ,		idalabel=UpperCamelCase__ ,		labelaid=UpperCamelCase__ ,		use_post_layernorm=UpperCamelCase__ ,		use_layerscale=UpperCamelCase__ ,		)

      return config


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if "patch_embed.proj" in name:
            UpperCamelCase__    :   List[str]					=				name.replace('''patch_embed.proj''' ,		'''embeddings.patch_embeddings.projection'''		)
      if "patch_embed.norm" in name:
            UpperCamelCase__    :   Union[str, Any]					=				name.replace('''patch_embed.norm''' ,		'''embeddings.norm'''		)
      if "layers" in name:
            UpperCamelCase__    :   int					=				'''encoder.''' + name
      if "encoder.layers" in name:
            UpperCamelCase__    :   Union[str, Any]					=				name.replace('''encoder.layers''' ,		'''encoder.stages'''		)
      if "downsample.proj" in name:
            UpperCamelCase__    :   List[Any]					=				name.replace('''downsample.proj''' ,		'''downsample.projection'''		)
      if "blocks" in name:
            UpperCamelCase__    :   List[Any]					=				name.replace('''blocks''' ,		'''layers'''		)
      if "modulation.f.weight" in name or "modulation.f.bias" in name:
            UpperCamelCase__    :   Any					=				name.replace('''modulation.f''' ,		'''modulation.projection_in'''		)
      if "modulation.h.weight" in name or "modulation.h.bias" in name:
            UpperCamelCase__    :   List[Any]					=				name.replace('''modulation.h''' ,		'''modulation.projection_context'''		)
      if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
            UpperCamelCase__    :   Any					=				name.replace('''modulation.proj''' ,		'''modulation.projection_out'''		)

      if name == "norm.weight":
            UpperCamelCase__    :   Dict					=				'''layernorm.weight'''
      if name == "norm.bias":
            UpperCamelCase__    :   List[Any]					=				'''layernorm.bias'''

      if "head" in name:
            UpperCamelCase__    :   Any					=				name.replace('''head''' ,		'''classifier'''		)
      else:
            UpperCamelCase__    :   List[Any]					=				'''focalnet.''' + name

      return name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__=False		):
      # fmt: off
      UpperCamelCase__    :   Dict					=				{
          '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
          '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
          '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
          '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
          '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
          '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
          '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
          '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
          '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
          '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
      }
      # fmt: on

      UpperCamelCase__    :   Tuple					=				model_name_to_url[model_name]
      print('''Checkpoint URL: ''' ,		UpperCamelCase__		)
      UpperCamelCase__    :   str					=				torch.hub.load_state_dict_from_url(UpperCamelCase__ ,		map_location='''cpu'''		)['''model''']

      # rename keys
      for key in state_dict.copy().keys():
            UpperCamelCase__    :   List[Any]					=				state_dict.pop(UpperCamelCase__		)
            UpperCamelCase__    :   Dict					=				val

      UpperCamelCase__    :   Tuple					=				get_focalnet_config(UpperCamelCase__		)
      UpperCamelCase__    :   List[str]					=				FocalNetForImageClassification(UpperCamelCase__		)
      model.eval()

      # load state dict
      model.load_state_dict(UpperCamelCase__		)

      # verify conversion
      UpperCamelCase__    :   Optional[int]					=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''

      UpperCamelCase__    :   List[Any]					=				BitImageProcessor(
          do_resize=UpperCamelCase__ ,		size={'''shortest_edge''': 2_5_6} ,		resample=PILImageResampling.BILINEAR ,		do_center_crop=UpperCamelCase__ ,		crop_size=2_2_4 ,		do_normalize=UpperCamelCase__ ,		image_mean=UpperCamelCase__ ,		image_std=UpperCamelCase__ ,		)
      UpperCamelCase__    :   str					=				Image.open(requests.get(UpperCamelCase__ ,		stream=UpperCamelCase__		).raw		)
      UpperCamelCase__    :   List[Any]					=				processor(images=UpperCamelCase__ ,		return_tensors='''pt'''		)

      UpperCamelCase__    :   Tuple					=				transforms.Compose(
          [
              transforms.Resize(2_5_6		),
              transforms.CenterCrop(2_2_4		),
              transforms.ToTensor(),
              transforms.Normalize(mean=[0.485, 0.456, 0.406] ,		std=[0.229, 0.224, 0.225]		),
          ]		)

      UpperCamelCase__    :   Union[str, Any]					=				image_transforms(UpperCamelCase__		).unsqueeze(0		)

      # verify pixel_values
      assert torch.allclose(inputs.pixel_values ,		UpperCamelCase__ ,		atol=1e-4		)

      UpperCamelCase__    :   List[str]					=				model(**UpperCamelCase__		)

      UpperCamelCase__    :   Any					=				outputs.logits.argmax(-1		).item()
      print('''Predicted class:''' ,		model.config.idalabel[predicted_class_idx]		)

      print('''First values of logits:''' ,		outputs.logits[0, :3]		)

      if model_name == "focalnet-tiny":
            UpperCamelCase__    :   str					=				torch.tensor([0.2166, -0.4368, 0.2191]		)
      elif model_name == "focalnet-tiny-lrf":
            UpperCamelCase__    :   int					=				torch.tensor([1.1669, 0.0125, -0.1695]		)
      elif model_name == "focalnet-small":
            UpperCamelCase__    :   Dict					=				torch.tensor([0.4917, -0.0430, 0.1341]		)
      elif model_name == "focalnet-small-lrf":
            UpperCamelCase__    :   Tuple					=				torch.tensor([-0.2588, -0.5342, -0.2331]		)
      elif model_name == "focalnet-base":
            UpperCamelCase__    :   Optional[Any]					=				torch.tensor([-0.1655, -0.4090, -0.1730]		)
      elif model_name == "focalnet-base-lrf":
            UpperCamelCase__    :   str					=				torch.tensor([0.5306, -0.0483, -0.3928]		)
      assert torch.allclose(outputs.logits[0, :3] ,		UpperCamelCase__ ,		atol=1e-4		)
      print('''Looks ok!'''		)

      if pytorch_dump_folder_path is not None:
            print(f'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''		)
            model.save_pretrained(UpperCamelCase__		)
            processor.save_pretrained(UpperCamelCase__		)

      if push_to_hub:
            print(f'''Pushing model and processor of {model_name} to the hub...'''		)
            model.push_to_hub(f'''{model_name}'''		)
            processor.push_to_hub(f'''{model_name}'''		)


if __name__ == "__main__":
    lowerCamelCase      =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="focalnet-tiny",
        type=str,
        help="Name of the FocalNet model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
        help="Whether to push the model and processor to the hub.",
    )

    lowerCamelCase      =parser.parse_args()
    convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 5_0_0_0_0_0_0_0		):
      UpperCamelCase__    :   Any					=				set()
      UpperCamelCase__    :   Any					=				int((limit - 2_4) ** (1 / 2)		)

      UpperCamelCase__    :   Dict					=				set(range(3 ,		prime_square_limit + 1 ,		2		)		)
      primes.add(2		)
      for p in range(3 ,		prime_square_limit + 1 ,		2		):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p ,		prime_square_limit + 1 ,		UpperCamelCase__		)		)		)

      for primea in primes:
            UpperCamelCase__    :   Dict					=				primea * primea
            for primea in primes:
                  UpperCamelCase__    :   str					=				primea * primea * primea
                  if square + cube >= limit - 1_6:
                        break
                  for primea in primes:
                        UpperCamelCase__    :   Dict					=				primea * primea * primea * primea
                        UpperCamelCase__    :   Tuple					=				square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(UpperCamelCase__		)

      return len(UpperCamelCase__		)


if __name__ == "__main__":
    print(F'''{solution() = }''')


