'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def __lowerCAmelCase (							UpperCamelCase__				)	->    int:
    __lowerCamelCase		=  SwinConfig(image_size=1_92				)

    if "base" in model_name:
        __lowerCamelCase		=  6
        __lowerCamelCase		=  1_28
        __lowerCamelCase		=  (2, 2, 18, 2)
        __lowerCamelCase		=  (4, 8, 16, 32)
    elif "large" in model_name:
        __lowerCamelCase		=  12
        __lowerCamelCase		=  1_92
        __lowerCamelCase		=  (2, 2, 18, 2)
        __lowerCamelCase		=  (6, 12, 24, 48)
    else:
        raise ValueError('''Model not supported, only supports base and large variants'''				)

    __lowerCamelCase		=  window_size
    __lowerCamelCase		=  embed_dim
    __lowerCamelCase		=  depths
    __lowerCamelCase		=  num_heads

    return config






def __lowerCAmelCase (							UpperCamelCase__				)	->    Tuple:
    if "encoder.mask_token" in name:
        __lowerCamelCase		=  name.replace('''encoder.mask_token'''				,  '''embeddings.mask_token'''				)
    if "encoder.patch_embed.proj" in name:
        __lowerCamelCase		=  name.replace('''encoder.patch_embed.proj'''				,  '''embeddings.patch_embeddings.projection'''				)
    if "encoder.patch_embed.norm" in name:
        __lowerCamelCase		=  name.replace('''encoder.patch_embed.norm'''				,  '''embeddings.norm'''				)
    if "attn.proj" in name:
        __lowerCamelCase		=  name.replace('''attn.proj'''				,  '''attention.output.dense'''				)
    if "attn" in name:
        __lowerCamelCase		=  name.replace('''attn'''				,  '''attention.self'''				)
    if "norm1" in name:
        __lowerCamelCase		=  name.replace('''norm1'''				,  '''layernorm_before'''				)
    if "norm2" in name:
        __lowerCamelCase		=  name.replace('''norm2'''				,  '''layernorm_after'''				)
    if "mlp.fc1" in name:
        __lowerCamelCase		=  name.replace('''mlp.fc1'''				,  '''intermediate.dense'''				)
    if "mlp.fc2" in name:
        __lowerCamelCase		=  name.replace('''mlp.fc2'''				,  '''output.dense'''				)

    if name == "encoder.norm.weight":
        __lowerCamelCase		=  '''layernorm.weight'''
    if name == "encoder.norm.bias":
        __lowerCamelCase		=  '''layernorm.bias'''

    if "decoder" in name:
        pass
    else:
        __lowerCamelCase		=  '''swin.''' + name

    return name






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    str:
    for key in orig_state_dict.copy().keys():
        __lowerCamelCase		=  orig_state_dict.pop(UpperCamelCase__				)

        if "attn_mask" in key:
            pass
        elif "qkv" in key:
            __lowerCamelCase		=  key.split('''.'''				)
            __lowerCamelCase		=  int(key_split[2]				)
            __lowerCamelCase		=  int(key_split[4]				)
            __lowerCamelCase		=  model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

    return orig_state_dict






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    __lowerCamelCase		=  torch.load(UpperCamelCase__				,  map_location='''cpu'''				)['''model''']

    __lowerCamelCase		=  get_swin_config(UpperCamelCase__				)
    __lowerCamelCase		=  SwinForMaskedImageModeling(UpperCamelCase__				)
    model.eval()

    __lowerCamelCase		=  convert_state_dict(UpperCamelCase__				,  UpperCamelCase__				)
    model.load_state_dict(UpperCamelCase__				)

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

    __lowerCamelCase		=  ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}				)
    __lowerCamelCase		=  Image.open(requests.get(UpperCamelCase__				,  stream=UpperCamelCase__				).raw				)
    __lowerCamelCase		=  image_processor(images=UpperCamelCase__				,  return_tensors='''pt'''				)

    with torch.no_grad():
        __lowerCamelCase		=  model(**UpperCamelCase__				).logits

    print(outputs.keys()				)
    print('''Looks ok!'''				)

    if pytorch_dump_folder_path is not None:
        print(f"""Saving model {model_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 push_to_hub:
        print(f"""Pushing model and image processor for {model_name} to hub"""				)
        model.push_to_hub(f"""microsoft/{model_name}"""				)
        image_processor.push_to_hub(f"""microsoft/{model_name}"""				)


if __name__ == "__main__":
  __UpperCAmelCase     							=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="swin-base-simmim-window6-192",
      type=str,
      choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
      help="Name of the Swin SimMIM model you'd like to convert.",
  )
  parser.add_argument(
      "--checkpoint_path",
      default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
      type=str,
      help="Path to the original PyTorch checkpoint (.pth file).",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  __UpperCAmelCase     							=parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





