


"""simple docstring"""







import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _A						(						lowercase		):







    """simple docstring"""
    a 		=SwinvaConfig()
    a 		=swinva_name.split('''_'''		)

    a 		=name_split[1]
    if "to" in name_split[3]:
        a 		=int(name_split[3][-3:]		)
    else:
        a 		=int(name_split[3]		)
    if "to" in name_split[2]:
        a 		=int(name_split[2][-2:]		)
    else:
        a 		=int(name_split[2][6:]		)

    if model_size == "tiny":
        a 		=96
        a 		=(2, 2, 6, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "small":
        a 		=96
        a 		=(2, 2, 18, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "base":
        a 		=1_28
        a 		=(2, 2, 18, 2)
        a 		=(4, 8, 16, 32)
    else:
        a 		=1_92
        a 		=(2, 2, 18, 2)
        a 		=(6, 12, 24, 48)

    if "to" in swinva_name:
        a 		=(12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        a 		=2_18_41
        a 		='''huggingface/label-files'''
        a 		='''imagenet-22k-id2label.json'''
        a 		=json.load(open(hf_hub_download(lowercase       ,    lowercase       ,    repo_type='''dataset'''		)       ,    '''r'''		)		)
        a 		={int(lowercase		): v for k, v in idalabel.items()}
        a 		=idalabel
        a 		={v: k for k, v in idalabel.items()}

    else:
        a 		=10_00
        a 		='''huggingface/label-files'''
        a 		='''imagenet-1k-id2label.json'''
        a 		=json.load(open(hf_hub_download(lowercase       ,    lowercase       ,    repo_type='''dataset'''		)       ,    '''r'''		)		)
        a 		={int(lowercase		): v for k, v in idalabel.items()}
        a 		=idalabel
        a 		={v: k for k, v in idalabel.items()}

    a 		=img_size
    a 		=num_classes
    a 		=embed_dim
    a 		=depths
    a 		=num_heads
    a 		=window_size

    return config
def _A						(						lowercase		):







    """simple docstring"""
    if "patch_embed.proj" in name:
        a 		=name.replace('''patch_embed.proj'''       ,    '''embeddings.patch_embeddings.projection'''		)
    if "patch_embed.norm" in name:
        a 		=name.replace('''patch_embed.norm'''       ,    '''embeddings.norm'''		)
    if "layers" in name:
        a 		='''encoder.''' + name
    if "attn.proj" in name:
        a 		=name.replace('''attn.proj'''       ,    '''attention.output.dense'''		)
    if "attn" in name:
        a 		=name.replace('''attn'''       ,    '''attention.self'''		)
    if "norm1" in name:
        a 		=name.replace('''norm1'''       ,    '''layernorm_before'''		)
    if "norm2" in name:
        a 		=name.replace('''norm2'''       ,    '''layernorm_after'''		)
    if "mlp.fc1" in name:
        a 		=name.replace('''mlp.fc1'''       ,    '''intermediate.dense'''		)
    if "mlp.fc2" in name:
        a 		=name.replace('''mlp.fc2'''       ,    '''output.dense'''		)
    if "q_bias" in name:
        a 		=name.replace('''q_bias'''       ,    '''query.bias'''		)
    if "k_bias" in name:
        a 		=name.replace('''k_bias'''       ,    '''key.bias'''		)
    if "v_bias" in name:
        a 		=name.replace('''v_bias'''       ,    '''value.bias'''		)
    if "cpb_mlp" in name:
        a 		=name.replace('''cpb_mlp'''       ,    '''continuous_position_bias_mlp'''		)
    if name == "norm.weight":
        a 		='''layernorm.weight'''
    if name == "norm.bias":
        a 		='''layernorm.bias'''

    if "head" in name:
        a 		=name.replace('''head'''       ,    '''classifier'''		)
    else:
        a 		='''swinv2.''' + name

    return name
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    for key in orig_state_dict.copy().keys():
        a 		=orig_state_dict.pop(lowercase		)

        if "mask" in key:
            continue
        elif "qkv" in key:
            a 		=key.split('''.'''		)
            a 		=int(key_split[1]		)
            a 		=int(key_split[3]		)
            a 		=model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                a 		=val[:dim, :]
                a 		=val[dim : dim * 2, :]
                a 		=val[-dim:, :]
            else:
                a 		=val[:dim]
                a 		=val[
                    dim : dim * 2
                ]
                a 		=val[-dim:]
        else:
            a 		=val

    return orig_state_dict
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=timm.create_model(lowercase       ,    pretrained=lowercase		)
    timm_model.eval()

    a 		=get_swinva_config(lowercase		)
    a 		=SwinvaForImageClassification(lowercase		)
    model.eval()

    a 		=convert_state_dict(timm_model.state_dict()       ,    lowercase		)
    model.load_state_dict(lowercase		)

    a 		='''http://images.cocodataset.org/val2017/000000039769.jpg'''

    a 		=AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''       ,    '''-'''		)		)		)
    a 		=Image.open(requests.get(lowercase       ,    stream=lowercase		).raw		)
    a 		=image_processor(images=lowercase       ,    return_tensors='''pt'''		)

    a 		=timm_model(inputs['''pixel_values''']		)
    a 		=model(**lowercase		).logits

    assert torch.allclose(lowercase       ,    lowercase       ,    atol=1E-3		)

    print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(lowercase		)

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

    model.push_to_hub(
        repo_path_or_name=Path(lowercase       ,    lowercase		)       ,    organization='''nandwalritik'''       ,    commit_message='''Add model'''       ,    )


if __name__ == "__main__":
       lowerCamelCase_   :   Tuple									=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--swinv2_name""",
           default="""swinv2_tiny_patch4_window8_256""",
           type=str,
           help="""Name of the Swinv2 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_   :   Union[str, Any]									=  parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)

def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		if index == number_of_items:
				return 0
		UpperCamelCase		=     0
		UpperCamelCase		=     0
		UpperCamelCase		=     knapsack(lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       index + 1 )
		if weights[index] <= max_weight:
				UpperCamelCase		=     values[index] + knapsack(
				    lowercase    ,       lowercase    ,       lowercase    ,       max_weight - weights[index]    ,       index + 1 )
		return max(lowercase    ,       lowercase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
