






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   (				SCREAMING_SNAKE_CASE__  :	Any       ):
    UpperCamelCase       :Any								=   SwinvaConfig()
    UpperCamelCase       :Optional[int]								=   swinva_name.split('''_'''       )

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

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

    if "to" in swinva_name:
        UpperCamelCase       :Optional[int]								=   (12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        UpperCamelCase       :str								=   21841
        UpperCamelCase       :List[Any]								=   """huggingface/label-files"""
        UpperCamelCase       :List[str]								=   """imagenet-22k-id2label.json"""
        UpperCamelCase       :Union[str, Any]								=   json.load(open(hf_hub_download(UpperCamelCase__     ,  UpperCamelCase__     ,  repo_type='''dataset'''       )     ,  '''r'''       )       )
        UpperCamelCase       :str								=   {int(UpperCamelCase__       ): v for k, v in idalabel.items()}
        UpperCamelCase       :int								=   idalabel
        UpperCamelCase       :Optional[int]								=   {v: k for k, v in idalabel.items()}

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

    UpperCamelCase       :List[Any]								=   img_size
    UpperCamelCase       :List[Any]								=   num_classes
    UpperCamelCase       :List[str]								=   embed_dim
    UpperCamelCase       :Dict								=   depths
    UpperCamelCase       :List[str]								=   num_heads
    UpperCamelCase       :Optional[Any]								=   window_size

    return config


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

    if "head" in name:
        UpperCamelCase       :Dict								=   name.replace('''head'''     ,  '''classifier'''       )
    else:
        UpperCamelCase       :Union[str, Any]								=   """swinv2.""" + name

    return name


def   _A   (				SCREAMING_SNAKE_CASE__  :	int     ,  SCREAMING_SNAKE_CASE__  :	Optional[int]       ):
    for key in orig_state_dict.copy().keys():
        UpperCamelCase       :Optional[Any]								=   orig_state_dict.pop(UpperCamelCase__       )

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

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

    return orig_state_dict


def   _A   (				SCREAMING_SNAKE_CASE__  :	Dict     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]       ):
    UpperCamelCase       :int								=   timm.create_model(UpperCamelCase__     ,  pretrained=UpperCamelCase__       )
    timm_model.eval()

    UpperCamelCase       :Union[str, Any]								=   get_swinva_config(UpperCamelCase__       )
    UpperCamelCase       :Tuple								=   SwinvaForImageClassification(UpperCamelCase__       )
    model.eval()

    UpperCamelCase       :int								=   convert_state_dict(timm_model.state_dict()     ,  UpperCamelCase__       )
    model.load_state_dict(UpperCamelCase__       )

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

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

    UpperCamelCase       :Optional[Any]								=   timm_model(inputs['''pixel_values''']       )
    UpperCamelCase       :Dict								=   model(**UpperCamelCase__       ).logits

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

    print(F'''Saving model {swinva_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__       )

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


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

 __snake_case												=  parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)













"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_snake_case          =   logging.get_logger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Any							,						*UpperCAmelCase__		:						Optional[Any]							,						**UpperCAmelCase__		:						Tuple							)					->       None:
   warnings.warn(
       """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use CLIPImageProcessor instead."""							,						UpperCAmelCase__							,						)
   super().__init__(*UpperCAmelCase__							,						**UpperCAmelCase__							)

