



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      __UpperCamelCase     (  _lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	Union[str, Any]										=       SwinvaConfig()
    A			:	Optional[int]										=       swinva_name.split("""_"""		)

    A			:	Union[str, Any]										=       name_split[1]
    if "to" in name_split[3]:
        A			:	Optional[Any]										=       int(name_split[3][-3:]		)
    else:
        A			:	List[str]										=       int(name_split[3]		)
    if "to" in name_split[2]:
        A			:	List[Any]										=       int(name_split[2][-2:]		)
    else:
        A			:	Union[str, Any]										=       int(name_split[2][6:]		)

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

    if "to" in swinva_name:
        A			:	Union[str, Any]										=       (12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        A			:	Union[str, Any]										=       2_1841
        A			:	Optional[Any]										=       """huggingface/label-files"""
        A			:	Optional[int]										=       """imagenet-22k-id2label.json"""
        A			:	Tuple										=       json.load(open(hf_hub_download(_lowerCAmelCase				,					_lowerCAmelCase				,					repo_type="""dataset"""		)				,					"""r"""		)		)
        A			:	Union[str, Any]										=       {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
        A			:	List[Any]										=       idalabel
        A			:	Any										=       {v: k for k, v in idalabel.items()}

    else:
        A			:	Optional[int]										=       1000
        A			:	Optional[Any]										=       """huggingface/label-files"""
        A			:	int										=       """imagenet-1k-id2label.json"""
        A			:	Dict										=       json.load(open(hf_hub_download(_lowerCAmelCase				,					_lowerCAmelCase				,					repo_type="""dataset"""		)				,					"""r"""		)		)
        A			:	Optional[Any]										=       {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
        A			:	Optional[int]										=       idalabel
        A			:	Optional[int]										=       {v: k for k, v in idalabel.items()}

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

    return config




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    if "patch_embed.proj" in name:
        A			:	Dict										=       name.replace("""patch_embed.proj"""				,					"""embeddings.patch_embeddings.projection"""		)
    if "patch_embed.norm" in name:
        A			:	Dict										=       name.replace("""patch_embed.norm"""				,					"""embeddings.norm"""		)
    if "layers" in name:
        A			:	List[str]										=       """encoder.""" + name
    if "attn.proj" in name:
        A			:	int										=       name.replace("""attn.proj"""				,					"""attention.output.dense"""		)
    if "attn" in name:
        A			:	str										=       name.replace("""attn"""				,					"""attention.self"""		)
    if "norm1" in name:
        A			:	Tuple										=       name.replace("""norm1"""				,					"""layernorm_before"""		)
    if "norm2" in name:
        A			:	Dict										=       name.replace("""norm2"""				,					"""layernorm_after"""		)
    if "mlp.fc1" in name:
        A			:	int										=       name.replace("""mlp.fc1"""				,					"""intermediate.dense"""		)
    if "mlp.fc2" in name:
        A			:	List[Any]										=       name.replace("""mlp.fc2"""				,					"""output.dense"""		)
    if "q_bias" in name:
        A			:	List[Any]										=       name.replace("""q_bias"""				,					"""query.bias"""		)
    if "k_bias" in name:
        A			:	Tuple										=       name.replace("""k_bias"""				,					"""key.bias"""		)
    if "v_bias" in name:
        A			:	Optional[Any]										=       name.replace("""v_bias"""				,					"""value.bias"""		)
    if "cpb_mlp" in name:
        A			:	Any										=       name.replace("""cpb_mlp"""				,					"""continuous_position_bias_mlp"""		)
    if name == "norm.weight":
        A			:	List[Any]										=       """layernorm.weight"""
    if name == "norm.bias":
        A			:	List[Any]										=       """layernorm.bias"""

    if "head" in name:
        A			:	int										=       name.replace("""head"""				,					"""classifier"""		)
    else:
        A			:	List[str]										=       """swinv2.""" + name

    return name




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        A			:	str										=       orig_state_dict.pop(_lowerCAmelCase		)

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

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

    return orig_state_dict




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    A			:	List[Any]										=       timm.create_model(_lowerCAmelCase				,					pretrained=_lowerCAmelCase		)
    timm_model.eval()

    A			:	Tuple										=       get_swinva_config(_lowerCAmelCase		)
    A			:	Optional[Any]										=       SwinvaForImageClassification(_lowerCAmelCase		)
    model.eval()

    A			:	Tuple										=       convert_state_dict(timm_model.state_dict()				,					_lowerCAmelCase		)
    model.load_state_dict(_lowerCAmelCase		)

    A			:	List[str]										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""

    A			:	Optional[int]										=       AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""				,					"""-"""		)		)		)
    A			:	Dict										=       Image.open(requests.get(_lowerCAmelCase				,					stream=_lowerCAmelCase		).raw		)
    A			:	Union[str, Any]										=       image_processor(images=_lowerCAmelCase				,					return_tensors="""pt"""		)

    A			:	List[Any]										=       timm_model(inputs["""pixel_values"""]		)
    A			:	Union[str, Any]										=       model(**_lowerCAmelCase		).logits

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

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

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

    model.push_to_hub(
        repo_path_or_name=Path(_lowerCAmelCase				,					_lowerCAmelCase		)				,					organization="""nandwalritik"""				,					commit_message="""Add model"""				,					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_: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."""
     )

     SCREAMING_SNAKE_CASE_:Optional[Any]					=						parser.parse_args()
     convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







import os
from pathlib import Path




def      __UpperCamelCase     (  )	->  Any:



    """simple docstring"""






    from torch.utils.cpp_extension import load

    A			:	Any										=       Path(_lowerCAmelCase		).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
    A			:	int										=       [
        root / filename
        for filename in [
            """vision.cpp""",
            os.path.join("""cpu"""				,					"""ms_deform_attn_cpu.cpp"""		),
            os.path.join("""cuda"""				,					"""ms_deform_attn_cuda.cu"""		),
        ]
    ]

    load(
        """MultiScaleDeformableAttention"""				,					_lowerCAmelCase				,					with_cuda=_lowerCAmelCase				,					extra_include_paths=[str(_lowerCAmelCase		)]				,					extra_cflags=["""-DWITH_CUDA=1"""]				,					extra_cuda_cflags=[
            """-DCUDA_HAS_FP16=1""",
            """-D__CUDA_NO_HALF_OPERATORS__""",
            """-D__CUDA_NO_HALF_CONVERSIONS__""",
            """-D__CUDA_NO_HALF2_OPERATORS__""",
        ]				,					)

    import MultiScaleDeformableAttention as MSDA

    return MSDA


