





'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """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 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  for key in orig_state_dict.copy().keys():
    _UpperCamelCase       			=orig_state_dict.pop(__SCREAMING_SNAKE_CASE   )

    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 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=torch.load(__SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''   )['''model''']

  _UpperCamelCase       			=get_swin_config(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE   )
  model.eval()

  _UpperCamelCase       			=convert_state_dict(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  model.load_state_dict(__SCREAMING_SNAKE_CASE   )

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

  _UpperCamelCase       			=ViTImageProcessor(size={'''height''': 192, '''width''': 192}   )
  _UpperCamelCase       			=Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,      stream=__SCREAMING_SNAKE_CASE   ).raw   )
  _UpperCamelCase       			=image_processor(images=__SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''   )

  with torch.no_grad():
    _UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   ).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(__SCREAMING_SNAKE_CASE   )

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

  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__":
     __lowerCamelCase						: 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.'
     )

     __lowerCamelCase						: Dict		=   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

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					List[Any]=3							,	UpperCamelCase__    :					Dict=30							,	UpperCamelCase__    :					Union[str, Any]=400							,	UpperCamelCase__    :					List[Any]=True							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					List[str]=0.9							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					Optional[Any]=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					List[str]=[0.5, 0.5, 0.5]							,	)     ->						Dict:
    _UpperCamelCase       			=size if size is not None else {'''shortest_edge''': 30}
    _UpperCamelCase       			=crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize_and_center_crop
    _UpperCamelCase       			=size
    _UpperCamelCase       			=crop_pct
    _UpperCamelCase       			=crop_size
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std




  def   UpperCamelCase__      (				self    :					str  )     ->						Union[str, Any]:
    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }







@require_torch
@require_vision
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				PoolFormerImageProcessor if is_vision_available() else None


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						int:
    _UpperCamelCase       			=PoolFormerImageProcessingTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    return self.image_processor_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_resize_and_center_crop'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''size'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''crop_pct'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_std'''  )  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Dict:
    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 30}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 30, '''width''': 30}  )

    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict							,	size=42							,	crop_size=84  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 42}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 84, '''width''': 84}  )


  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[Any]:
    pass


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[Any]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	Image.Image  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values

    self.assertEqual(
        encoded_images.shape							,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Optional[int]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	numpify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	np.ndarray  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)




  def   UpperCamelCase__      (				self    :					str  )     ->						List[Any]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	torchify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	torch.Tensor  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)






