
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def 				a__   (   a   :				List[str]		):

  """simple docstring"""







  _snake_case     :						Dict						=   SwinConfig(image_size=192		)

  if "base" in model_name:
    _snake_case     :						List[Any]						=   6
    _snake_case     :						Dict						=   128
    _snake_case     :						Dict						=   (2, 2, 18, 2)
    _snake_case     :						Tuple						=   (4, 8, 16, 32)
  elif "large" in model_name:
    _snake_case     :						Dict						=   12
    _snake_case     :						Tuple						=   192
    _snake_case     :						List[str]						=   (2, 2, 18, 2)
    _snake_case     :						Tuple						=   (6, 12, 24, 48)
  else:
    raise ValueError("Model not supported, only supports base and large variants"		)

  _snake_case     :						Tuple						=   window_size
  _snake_case     :						Union[str, Any]						=   embed_dim
  _snake_case     :						int						=   depths
  _snake_case     :						Tuple						=   num_heads

  return config
def 				a__   (   a   :				List[str]		):

  """simple docstring"""







  if "encoder.mask_token" in name:
    _snake_case     :						Union[str, Any]						=   name.replace("encoder.mask_token"       ,     "embeddings.mask_token"		)
  if "encoder.patch_embed.proj" in name:
    _snake_case     :						Any						=   name.replace("encoder.patch_embed.proj"       ,     "embeddings.patch_embeddings.projection"		)
  if "encoder.patch_embed.norm" in name:
    _snake_case     :						Any						=   name.replace("encoder.patch_embed.norm"       ,     "embeddings.norm"		)
  if "attn.proj" in name:
    _snake_case     :						Optional[int]						=   name.replace("attn.proj"       ,     "attention.output.dense"		)
  if "attn" in name:
    _snake_case     :						Tuple						=   name.replace("attn"       ,     "attention.self"		)
  if "norm1" in name:
    _snake_case     :						Any						=   name.replace("norm1"       ,     "layernorm_before"		)
  if "norm2" in name:
    _snake_case     :						Optional[Any]						=   name.replace("norm2"       ,     "layernorm_after"		)
  if "mlp.fc1" in name:
    _snake_case     :						int						=   name.replace("mlp.fc1"       ,     "intermediate.dense"		)
  if "mlp.fc2" in name:
    _snake_case     :						Union[str, Any]						=   name.replace("mlp.fc2"       ,     "output.dense"		)

  if name == "encoder.norm.weight":
    _snake_case     :						int						=   "layernorm.weight"
  if name == "encoder.norm.bias":
    _snake_case     :						str						=   "layernorm.bias"

  if "decoder" in name:
    pass
  else:
    _snake_case     :						List[Any]						=   "swin." + name

  return name
def 				a__   (   a   :				int       ,     a   :				Dict		):

  """simple docstring"""







  for key in orig_state_dict.copy().keys():
    _snake_case     :						List[Any]						=   orig_state_dict.pop(a		)

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

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

  return orig_state_dict
def 				a__   (   a   :				Optional[Any]       ,     a   :				Optional[Any]       ,     a   :				Optional[Any]       ,     a   :				List[str]		):

  """simple docstring"""







  _snake_case     :						str						=   torch.load(a       ,     map_location="cpu"		)["model"]

  _snake_case     :						Union[str, Any]						=   get_swin_config(a		)
  _snake_case     :						Optional[Any]						=   SwinForMaskedImageModeling(a		)
  model.eval()

  _snake_case     :						List[str]						=   convert_state_dict(a       ,     a		)
  model.load_state_dict(a		)

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

  _snake_case     :						List[str]						=   ViTImageProcessor(size={"height": 192, "width": 192}		)
  _snake_case     :						int						=   Image.open(requests.get(a       ,     stream=a		).raw		)
  _snake_case     :						int						=   image_processor(images=a       ,     return_tensors="pt"		)

  with torch.no_grad():
    _snake_case     :						str						=   model(**a		).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(a		)

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

  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__":
    _a      :			Optional[Any]					=							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."""
    )

    _a      :			Optional[Any]					=							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

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)






class      _UpperCAmelCase				(					unittest.TestCase):
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Vector([1, 2, 3]     )
      self.assertEqual(x.component(0     )						,    1     )
      self.assertEqual(x.component(2     )						,    3     )
      _snake_case     :						List[Any]						=   Vector()
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   Vector([0, 0, 0, 0, 0, 1]     )
      self.assertEqual(str(snake_case_     )						,    "(0,0,0,0,0,1)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   Vector([1, 2, 3, 4]     )
      self.assertEqual(len(snake_case_     )						,    4     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Vector([1, 2]     )
      _snake_case     :						List[str]						=   Vector([1, 2, 3, 4, 5]     )
      _snake_case     :						List[Any]						=   Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]     )
      _snake_case     :						Any						=   Vector([1, -1, 1, -1, 2, -3, 4, -5]     )
      self.assertAlmostEqual(x.euclidean_length()						,    2.236						,    3     )
      self.assertAlmostEqual(y.euclidean_length()						,    7.416						,    3     )
      self.assertEqual(z.euclidean_length()						,    0     )
      self.assertAlmostEqual(w.euclidean_length()						,    7.616						,    3     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Vector([1, 2, 3]     )
      _snake_case     :						Any						=   Vector([1, 1, 1]     )
      self.assertEqual((x + y).component(0     )						,    2     )
      self.assertEqual((x + y).component(1     )						,    3     )
      self.assertEqual((x + y).component(2     )						,    4     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   Vector([1, 2, 3]     )
      _snake_case     :						Union[str, Any]						=   Vector([1, 1, 1]     )
      self.assertEqual((x - y).component(0     )						,    0     )
      self.assertEqual((x - y).component(1     )						,    1     )
      self.assertEqual((x - y).component(2     )						,    2     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   Vector([1, 2, 3]     )
      _snake_case     :						List[Any]						=   Vector([2, -1, 4]     )  # for test of dot product
      _snake_case     :						Union[str, Any]						=   Vector([1, -2, -1]     )
      self.assertEqual(str(x * 3.0     )						,    "(3.0,6.0,9.0)"     )
      self.assertEqual((a * b)						,    0     )
    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(str(zero_vector(10     )     ).count("0"     )						,    10     )
    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(str(unit_basis_vector(3						,    1     )     )						,    "(0,1,0)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   Vector([1, 2, 3]     )
      _snake_case     :						Optional[Any]						=   Vector([1, 0, 1]     )
      self.assertEqual(str(axpy(2						,    snake_case_						,    snake_case_     )     )						,    "(3,4,7)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   Vector([1, 0, 0, 0, 0, 0]     )
      _snake_case     :						Optional[int]						=   x.copy()
      self.assertEqual(str(snake_case_     )						,    str(snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   Vector([1, 0, 0]     )
      x.change_component(0						,    0     )
      x.change_component(1						,    1     )
      self.assertEqual(str(snake_case_     )						,    "(0,1,0)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      self.assertEqual("|1,2,3|\n|2,4,5|\n|6,7,8|\n"						,    str(snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						str						=   [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
      for x in range(a.height()     ):
        for y in range(a.width()     ):
          self.assertEqual(minors[x][y]						,    a.minor(snake_case_						,    snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						Optional[Any]						=   [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
      for x in range(a.height()     ):
        for y in range(a.width()     ):
          self.assertEqual(cofactors[x][y]						,    a.cofactor(snake_case_						,    snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      self.assertEqual(-5						,    a.determinant()     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						,    3						,    3     )
      _snake_case     :						List[str]						=   Vector([1, 2, 3]     )
      self.assertEqual("(14,32,50)"						,    str(a * x     )     )
      self.assertEqual("|2,4,6|\n|8,10,12|\n|14,16,18|\n"						,    str(a * 2     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      a.change_component(0						,    2						,    5     )
      self.assertEqual("|1,2,5|\n|2,4,5|\n|6,7,8|\n"						,    str(snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      self.assertEqual(7						,    a.component(2						,    1     )						,    0.01     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						int						=   Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3     )
      self.assertEqual("|2,4,10|\n|4,8,10|\n|12,14,18|\n"						,    str(a + b     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						Optional[Any]						=   Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3     )
      self.assertEqual("|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"						,    str(a - b     )     )


    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(
          "|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"						,    str(square_zero_matrix(5     )     )						,    )


if __name__ == "__main__":
    unittest.main()

