




"""simple docstring"""







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      __snake_case (				_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
    UpperCamelCase              =	True if '''large''' in model_name or '''huge''' in model_name else False
    UpperCamelCase              =	True if '''large''' in model_name or '''huge''' in model_name else False
    UpperCamelCase              =	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              =	[3, 3, 3, 3]
            UpperCamelCase              =	[5, 5, 5, 5]
        elif "fl4" in model_name:
            UpperCamelCase              =	[4, 4, 4, 4]
            UpperCamelCase              =	[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        UpperCamelCase              =	[3, 3, 3, 3]
        if "lrf" in model_name:
            UpperCamelCase              =	[3, 3, 3, 3]
        else:
            UpperCamelCase              =	[2, 2, 2, 2]

    if "tiny" in model_name:
        UpperCamelCase              =	96
    elif "small" in model_name:
        UpperCamelCase              =	96
    elif "base" in model_name:
        UpperCamelCase              =	128
    elif "large" in model_name:
        UpperCamelCase              =	192
    elif "xlarge" in model_name:
        UpperCamelCase              =	256
    elif "huge" in model_name:
        UpperCamelCase              =	352

    # set label information
    UpperCamelCase              =	'''huggingface/label-files'''
    if "large" in model_name or "huge" in model_name:
        UpperCamelCase              =	'''imagenet-22k-id2label.json'''
    else:
        UpperCamelCase              =	'''imagenet-1k-id2label.json'''

    UpperCamelCase              =	json.load(open(hf_hub_download(_lowercase			,_lowercase			,repo_type='''dataset'''    )			,'''r'''    )    )
    UpperCamelCase              =	{int(_lowercase    ): v for k, v in idalabel.items()}
    UpperCamelCase              =	{v: k for k, v in idalabel.items()}

    UpperCamelCase              =	FocalNetConfig(
        embed_dim=_lowercase			,depths=_lowercase			,focal_levels=_lowercase			,focal_windows=_lowercase			,use_conv_embed=_lowercase			,idalabel=_lowercase			,labelaid=_lowercase			,use_post_layernorm=_lowercase			,use_layerscale=_lowercase			,)

    return config





def      __snake_case (				_lowercase    ):


    """simple docstring"""







    if "patch_embed.proj" in name:
        UpperCamelCase              =	name.replace('''patch_embed.proj'''			,'''embeddings.patch_embeddings.projection'''    )
    if "patch_embed.norm" in name:
        UpperCamelCase              =	name.replace('''patch_embed.norm'''			,'''embeddings.norm'''    )
    if "layers" in name:
        UpperCamelCase              =	'''encoder.''' + name
    if "encoder.layers" in name:
        UpperCamelCase              =	name.replace('''encoder.layers'''			,'''encoder.stages'''    )
    if "downsample.proj" in name:
        UpperCamelCase              =	name.replace('''downsample.proj'''			,'''downsample.projection'''    )
    if "blocks" in name:
        UpperCamelCase              =	name.replace('''blocks'''			,'''layers'''    )
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        UpperCamelCase              =	name.replace('''modulation.f'''			,'''modulation.projection_in'''    )
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        UpperCamelCase              =	name.replace('''modulation.h'''			,'''modulation.projection_context'''    )
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        UpperCamelCase              =	name.replace('''modulation.proj'''			,'''modulation.projection_out'''    )

    if name == "norm.weight":
        UpperCamelCase              =	'''layernorm.weight'''
    if name == "norm.bias":
        UpperCamelCase              =	'''layernorm.bias'''

    if "head" in name:
        UpperCamelCase              =	name.replace('''head'''			,'''classifier'''    )
    else:
        UpperCamelCase              =	'''focalnet.''' + name

    return name





def      __snake_case (				_lowercase			,_lowercase			,_lowercase=False    ):


    """simple docstring"""







    UpperCamelCase              =	{
        '''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              =	model_name_to_url[model_name]
    print('''Checkpoint URL: '''			,_lowercase    )
    UpperCamelCase              =	torch.hub.load_state_dict_from_url(_lowercase			,map_location='''cpu'''    )['''model''']

    # rename keys
    for key in state_dict.copy().keys():
        UpperCamelCase              =	state_dict.pop(_lowercase    )
        UpperCamelCase              =	val

    UpperCamelCase              =	get_focalnet_config(_lowercase    )
    UpperCamelCase              =	FocalNetForImageClassification(_lowercase    )
    model.eval()

    # load state dict
    model.load_state_dict(_lowercase    )

    # verify conversion
    UpperCamelCase              =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    UpperCamelCase              =	BitImageProcessor(
        do_resize=_lowercase			,size={'''shortest_edge''': 256}			,resample=PILImageResampling.BILINEAR			,do_center_crop=_lowercase			,crop_size=224			,do_normalize=_lowercase			,image_mean=_lowercase			,image_std=_lowercase			,)
    UpperCamelCase              =	Image.open(requests.get(_lowercase			,stream=_lowercase    ).raw    )
    UpperCamelCase              =	processor(images=_lowercase			,return_tensors='''pt'''    )

    UpperCamelCase              =	transforms.Compose(
        [
            transforms.Resize(256    ),
            transforms.CenterCrop(224    ),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406]			,std=[0.229, 0.224, 0.225]    ),
        ]    )

    UpperCamelCase              =	image_transforms(_lowercase    ).unsqueeze(0    )

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

    UpperCamelCase              =	model(**_lowercase    )

    UpperCamelCase              =	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              =	torch.tensor([0.2166, -0.4368, 0.2191]    )
    elif model_name == "focalnet-tiny-lrf":
        UpperCamelCase              =	torch.tensor([1.1669, 0.0125, -0.1695]    )
    elif model_name == "focalnet-small":
        UpperCamelCase              =	torch.tensor([0.4917, -0.0430, 0.1341]    )
    elif model_name == "focalnet-small-lrf":
        UpperCamelCase              =	torch.tensor([-0.2588, -0.5342, -0.2331]    )
    elif model_name == "focalnet-base":
        UpperCamelCase              =	torch.tensor([-0.1655, -0.4090, -0.1730]    )
    elif model_name == "focalnet-base-lrf":
        UpperCamelCase              =	torch.tensor([0.5306, -0.0483, -0.3928]    )
    assert torch.allclose(outputs.logits[0, :3]			,_lowercase			,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(_lowercase    )
        processor.save_pretrained(_lowercase    )

    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__":
  SCREAMING_SNAKE_CASE_	     =       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.',
  )

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





import os





def 						__A						(      ) ->			Dict:
	with open(os.path.dirname(__lowerCamelCase       ) + """/p022_names.txt"""       ) as file:
		a									=				str(file.readlines()[0]       )
		a									=				names.replace("""\""""				,     """"""       ).split(""","""       )

	names.sort()

	a									=				0
	a									=				0

	for i, name in enumerate(__lowerCamelCase       ):
		for letter in name:
			name_score += ord(__lowerCamelCase       ) - 64

		total_score += (i + 1) * name_score
		a									=				0
	return total_score


if __name__ == "__main__":
		print(solution())

