






"""simple docstring"""






import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def       _A    (__a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	SwinConfig()
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	swin_name.split('''_'''							)

    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name_split[1]
    SCREAMING_SNAKE_CASE_   :						Any							=	int(name_split[4]							)
    SCREAMING_SNAKE_CASE_   :						Any							=	int(name_split[3][-1]							)

    if model_size == "tiny":
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	96
        SCREAMING_SNAKE_CASE_   :						Tuple							=	(2, 2, 6, 2)
        SCREAMING_SNAKE_CASE_   :						Any							=	(3, 6, 12, 24)
    elif model_size == "small":
        SCREAMING_SNAKE_CASE_   :						Dict							=	96
        SCREAMING_SNAKE_CASE_   :						List[str]							=	(2, 2, 18, 2)
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	(3, 6, 12, 24)
    elif model_size == "base":
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	1_28
        SCREAMING_SNAKE_CASE_   :						str							=	(2, 2, 18, 2)
        SCREAMING_SNAKE_CASE_   :						Any							=	(4, 8, 16, 32)
    else:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	1_92
        SCREAMING_SNAKE_CASE_   :						Any							=	(2, 2, 18, 2)
        SCREAMING_SNAKE_CASE_   :						Any							=	(6, 12, 24, 48)

    if "in22k" in swin_name:
        SCREAMING_SNAKE_CASE_   :						Any							=	2_18_41
    else:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	10_00
        SCREAMING_SNAKE_CASE_   :						Any							=	'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	'''imagenet-1k-id2label.json'''
        SCREAMING_SNAKE_CASE_   :						str							=	json.load(open(hf_hub_download(__a					,       __a					,       repo_type='''dataset'''							)					,       '''r'''							)							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	idalabel
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{v: k for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	img_size
    SCREAMING_SNAKE_CASE_   :						int							=	num_classes
    SCREAMING_SNAKE_CASE_   :						Tuple							=	embed_dim
    SCREAMING_SNAKE_CASE_   :						Any							=	depths
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	num_heads
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	window_size

    return config



def       _A    (__a							)				-> Tuple:
    """simple docstring"""






    if "patch_embed.proj" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''patch_embed.proj'''					,       '''embeddings.patch_embeddings.projection'''							)
    if "patch_embed.norm" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''patch_embed.norm'''					,       '''embeddings.norm'''							)
    if "layers" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	'''encoder.''' + name
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''attn.proj'''					,       '''attention.output.dense'''							)
    if "attn" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''attn'''					,       '''attention.self'''							)
    if "norm1" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''norm1'''					,       '''layernorm_before'''							)
    if "norm2" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''norm2'''					,       '''layernorm_after'''							)
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''mlp.fc1'''					,       '''intermediate.dense'''							)
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''mlp.fc2'''					,       '''output.dense'''							)

    if name == "norm.weight":
        SCREAMING_SNAKE_CASE_   :						Dict							=	'''layernorm.weight'''
    if name == "norm.bias":
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	'''layernorm.bias'''

    if "head" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''head'''					,       '''classifier'''							)
    else:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''swin.''' + name

    return name



def       _A    (__a					,       __a							)				-> Optional[int]:
    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	orig_state_dict.pop(__a							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            SCREAMING_SNAKE_CASE_   :						List[str]							=	key.split('''.'''							)
            SCREAMING_SNAKE_CASE_   :						Optional[int]							=	int(key_split[1]							)
            SCREAMING_SNAKE_CASE_   :						Dict							=	int(key_split[3]							)
            SCREAMING_SNAKE_CASE_   :						int							=	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                SCREAMING_SNAKE_CASE_   :						Optional[int]							=	val[:dim, :]
                SCREAMING_SNAKE_CASE_   :						Tuple							=	val[
                    dim : dim * 2, :
                ]
                SCREAMING_SNAKE_CASE_   :						List[str]							=	val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE_   :						str							=	val[
                    :dim
                ]
                SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	val[
                    dim : dim * 2
                ]
                SCREAMING_SNAKE_CASE_   :						int							=	val[
                    -dim:
                ]
        else:
            SCREAMING_SNAKE_CASE_   :						List[str]							=	val

    return orig_state_dict



def       _A    (__a					,       __a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	timm.create_model(__a					,       pretrained=__a							)
    timm_model.eval()

    SCREAMING_SNAKE_CASE_   :						List[str]							=	get_swin_config(__a							)
    SCREAMING_SNAKE_CASE_   :						Dict							=	SwinForImageClassification(__a							)
    model.eval()

    SCREAMING_SNAKE_CASE_   :						Dict							=	convert_state_dict(timm_model.state_dict()					,       __a							)
    model.load_state_dict(__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    SCREAMING_SNAKE_CASE_   :						Any							=	AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''					,       '''-'''							)							)							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	Image.open(requests.get(__a					,       stream=__a							).raw							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	image_processor(images=__a					,       return_tensors='''pt'''							)

    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	timm_model(inputs['''pixel_values''']							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model(**__a							).logits

    assert torch.allclose(__a					,       __a					,       atol=1e-3							)

    print(f'Saving model {swin_name} to {pytorch_dump_folder_path}'							)
    model.save_pretrained(__a							)

    print(f'Saving image processor to {pytorch_dump_folder_path}'							)
    image_processor.save_pretrained(__a							)


if __name__ == "__main__":
  UpperCAmelCase_     :				Optional[int]         =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin timm 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."""
  )

  UpperCAmelCase_     :				Tuple         =				parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










"""simple docstring"""






# Algorithm for the pigeonhole sorting





def       _A    (__a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	min(__a							)  # min() finds the minimum value
    SCREAMING_SNAKE_CASE_   :						int							=	max(__a							)  # max() finds the maximum value

    SCREAMING_SNAKE_CASE_   :						Dict							=	max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    SCREAMING_SNAKE_CASE_   :						Any							=	[0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(__a					,       __a							), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    SCREAMING_SNAKE_CASE_   :						Any							=	0
    for count in range(__a							):
        while holes[count] > 0:
            holes[count] -= 1
            SCREAMING_SNAKE_CASE_   :						Dict							=	count + min_val
            i += 1



def       _A    ()				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	[8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(__a							)
    print('''Sorted order is:'''					,       ''' '''.join(__a							)							)


if __name__ == "__main__":
  main()


