





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_    (					_lowerCAmelCase					)						->			Tuple:
 UpperCamelCase     : Any	     =       SwinvaConfig()
 UpperCamelCase     : List[Any]	     =       swinva_name.split("_"					)

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

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

 if "to" in swinva_name:
  UpperCamelCase     : List[str]	     =       (12, 12, 12, 6)

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

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

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

 return config



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

 if "head" in name:
  UpperCamelCase     : int	     =       name.replace("head"							,       "classifier"					)
 else:
  UpperCamelCase     : Any	     =       "swinv2." + name

 return name



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Optional[int]:
 for key in orig_state_dict.copy().keys():
  UpperCamelCase     : List[str]	     =       orig_state_dict.pop(_lowerCAmelCase					)

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

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

 return orig_state_dict



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 UpperCamelCase     : Optional[Any]	     =       timm.create_model(_lowerCAmelCase							,       pretrained=_lowerCAmelCase					)
 timm_model.eval()

 UpperCamelCase     : Optional[int]	     =       get_swinva_config(_lowerCAmelCase					)
 UpperCamelCase     : Union[str, Any]	     =       SwinvaForImageClassification(_lowerCAmelCase					)
 model.eval()

 UpperCamelCase     : Optional[int]	     =       convert_state_dict(timm_model.state_dict()							,       _lowerCAmelCase					)
 model.load_state_dict(_lowerCAmelCase					)

 UpperCamelCase     : Optional[Any]	     =       "http://images.cocodataset.org/val2017/000000039769.jpg"

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

 UpperCamelCase     : str	     =       timm_model(inputs["pixel_values"]					)
 UpperCamelCase     : List[str]	     =       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__":
    __lowerCamelCase	:					List[Any]			  =					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	:					List[Any]			  =					parser.parse_args()
    convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase	:					str			  =					{"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""EncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""TFEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Union[str, Any]			  =					["""FlaxEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __lowerCamelCase	:					Dict			  =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
