import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def    lowerCAmelCase_ (     __A							)     ->				Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  SwinConfig(image_size=192							)

      if "base" in model_name:
            UpperCAmelCase__								=  6
            UpperCAmelCase__								=  128
            UpperCAmelCase__								=  (2, 2, 18, 2)
            UpperCAmelCase__								=  (4, 8, 16, 32)
      elif "large" in model_name:
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  192
            UpperCAmelCase__								=  (2, 2, 18, 2)
            UpperCAmelCase__								=  (6, 12, 24, 48)
      else:
            raise ValueError("Model not supported, only supports base and large variants"							)

      UpperCAmelCase__								=  window_size
      UpperCAmelCase__								=  embed_dim
      UpperCAmelCase__								=  depths
      UpperCAmelCase__								=  num_heads

      return config




def    lowerCAmelCase_ (     __A							)     ->				List[Any]:







      '''simple docstring'''


      if "encoder.mask_token" in name:
            UpperCAmelCase__								=  name.replace("encoder.mask_token",  "embeddings.mask_token"							)
      if "encoder.patch_embed.proj" in name:
            UpperCAmelCase__								=  name.replace("encoder.patch_embed.proj",  "embeddings.patch_embeddings.projection"							)
      if "encoder.patch_embed.norm" in name:
            UpperCAmelCase__								=  name.replace("encoder.patch_embed.norm",  "embeddings.norm"							)
      if "attn.proj" in name:
            UpperCAmelCase__								=  name.replace("attn.proj",  "attention.output.dense"							)
      if "attn" in name:
            UpperCAmelCase__								=  name.replace("attn",  "attention.self"							)
      if "norm1" in name:
            UpperCAmelCase__								=  name.replace("norm1",  "layernorm_before"							)
      if "norm2" in name:
            UpperCAmelCase__								=  name.replace("norm2",  "layernorm_after"							)
      if "mlp.fc1" in name:
            UpperCAmelCase__								=  name.replace("mlp.fc1",  "intermediate.dense"							)
      if "mlp.fc2" in name:
            UpperCAmelCase__								=  name.replace("mlp.fc2",  "output.dense"							)

      if name == "encoder.norm.weight":
            UpperCAmelCase__								=  """layernorm.weight"""
      if name == "encoder.norm.bias":
            UpperCAmelCase__								=  """layernorm.bias"""

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

      return name




def    lowerCAmelCase_ (     __A,  __A							)     ->				Tuple:







      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            UpperCAmelCase__								=  orig_state_dict.pop(UpperCamelCase__							)

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

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

      return orig_state_dict




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A							)     ->				Any:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(UpperCamelCase__,  map_location="cpu"							)["""model"""]

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

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

      UpperCAmelCase__								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""

      UpperCAmelCase__								=  ViTImageProcessor(size={"height": 192, "width": 192}							)
      UpperCAmelCase__								=  Image.open(requests.get(UpperCamelCase__,  stream=UpperCamelCase__							).raw							)
      UpperCAmelCase__								=  image_processor(images=UpperCamelCase__,  return_tensors="pt"							)

      with torch.no_grad():
            UpperCAmelCase__								=  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)











"""simple docstring"""




import unittest

import numpy as np

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = None     ,   ):



  '''simple docstring'''
  _a  :       List[Any]             =	np.shape(UpperCamelCase__				)
  _a  :       Any             =	np.shape(UpperCamelCase__				)
  _a  :       Union[str, Any]             =	np.shape(UpperCamelCase__				)

  if shape_a[0] != shape_b[0]:
    _a  :       int             =	(
        """Expected the same number of rows for A and B. """
        F"""Instead found A of size {shape_a} and B of size {shape_b}"""
    )
    raise ValueError(UpperCamelCase__				)

  if shape_b[1] != shape_c[1]:
    _a  :       Tuple             =	(
        """Expected the same number of columns for B and C. """
        F"""Instead found B of size {shape_b} and C of size {shape_c}"""
    )
    raise ValueError(UpperCamelCase__				)

  _a  :       int             =	pseudo_inv
  if a_inv is None:
    try:
      _a  :       Optional[int]             =	np.linalg.inv(UpperCamelCase__				)
    except np.linalg.LinAlgError:
      raise ValueError(
          """Input matrix A is not invertible. Cannot compute Schur complement."""				)

  return mat_c - mat_b.T @ a_inv @ mat_b






class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       str             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Tuple             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Optional[int]             =	np.array([[2, 1], [6, 3]]							)

   _a  :       Optional[Any]             =	schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	np.block([[a, b], [b.T, c]]							)

   _a  :       int             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       List[Any]             =	np.linalg.det(UpperCAmelCase__							)

   self.assertAlmostEqual(UpperCAmelCase__							,						det_a * det_s							)


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       Optional[int]             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Optional[int]             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Union[str, Any]             =	np.array([[2, 1], [6, 3]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						List[Any]							)					->       None:
   _a  :       Any             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Dict             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       List[Any]             =	np.array([[2, 1, 3], [6, 3, 5]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()

