



'''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= SwinConfig()
    lowerCAmelCase__			:  Any											= swin_name.split("""_"""      )

    lowerCAmelCase__			:  List[Any]											= name_split[1]
    lowerCAmelCase__			:  Any											= int(name_split[4]      )
    lowerCAmelCase__			:  Any											= int(name_split[3][-1]      )

    if model_size == "tiny":
        lowerCAmelCase__			:  Optional[int]											= 96
        lowerCAmelCase__			:  Optional[int]											= (2, 2, 6, 2)
        lowerCAmelCase__			:  Tuple											= (3, 6, 12, 24)
    elif model_size == "small":
        lowerCAmelCase__			:  Dict											= 96
        lowerCAmelCase__			:  List[Any]											= (2, 2, 18, 2)
        lowerCAmelCase__			:  List[str]											= (3, 6, 12, 24)
    elif model_size == "base":
        lowerCAmelCase__			:  Dict											= 128
        lowerCAmelCase__			:  List[Any]											= (2, 2, 18, 2)
        lowerCAmelCase__			:  List[str]											= (4, 8, 16, 32)
    else:
        lowerCAmelCase__			:  Union[str, Any]											= 192
        lowerCAmelCase__			:  str											= (2, 2, 18, 2)
        lowerCAmelCase__			:  Any											= (6, 12, 24, 48)

    if "in22k" in swin_name:
        lowerCAmelCase__			:  Dict											= 21841
    else:
        lowerCAmelCase__			:  Union[str, Any]											= 1000
        lowerCAmelCase__			:  Dict											= """huggingface/label-files"""
        lowerCAmelCase__			:  Optional[Any]											= """imagenet-1k-id2label.json"""
        lowerCAmelCase__			:  str											= json.load(open(hf_hub_download(UpperCamelCase							,   UpperCamelCase							,   repo_type="""dataset"""      )							,   """r"""      )      )
        lowerCAmelCase__			:  Tuple											= {int(UpperCamelCase      ): v for k, v in idalabel.items()}
        lowerCAmelCase__			:  Optional[Any]											= idalabel
        lowerCAmelCase__			:  Union[str, Any]											= {v: k for k, v in idalabel.items()}

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

    return config
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





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

    if name == "norm.weight":
        lowerCAmelCase__			:  List[Any]											= """layernorm.weight"""
    if name == "norm.bias":
        lowerCAmelCase__			:  Any											= """layernorm.bias"""

    if "head" in name:
        lowerCAmelCase__			:  Tuple											= name.replace("""head"""							,   """classifier"""      )
    else:
        lowerCAmelCase__			:  Any											= """swin.""" + name

    return name
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





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

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

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

    return orig_state_dict
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Tuple											= timm.create_model(UpperCamelCase							,   pretrained=UpperCamelCase      )
    timm_model.eval()

    lowerCAmelCase__			:  Optional[Any]											= get_swin_config(UpperCamelCase      )
    lowerCAmelCase__			:  Tuple											= SwinForImageClassification(UpperCamelCase      )
    model.eval()

    lowerCAmelCase__			:  Tuple											= convert_state_dict(timm_model.state_dict()							,   UpperCamelCase      )
    model.load_state_dict(UpperCamelCase      )

    lowerCAmelCase__			:  Any											= """http://images.cocodataset.org/val2017/000000039769.jpg"""

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

    lowerCAmelCase__			:  Optional[int]											= timm_model(inputs["""pixel_values"""]      )
    lowerCAmelCase__			:  List[str]											= model(**UpperCamelCase      ).logits

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

    print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""      )
    model.save_pretrained(UpperCamelCase      )

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""      )
    image_processor.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
 _lowerCAmelCase										=     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.'''
 )

 _lowerCAmelCase										=     parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    if any(not isinstance(UpperCamelCase							,   UpperCamelCase      ) or x < 0 for x in sequence      ):
        raise TypeError("""Sequence must be list of non-negative integers"""      )
    for _ in range(len(UpperCamelCase      )      ):
        for i, (rod_upper, rod_lower) in enumerate(zip(UpperCamelCase							,   sequence[1:]      )      ):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
 assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
 assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]


