





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def        __lowerCamelCase (							__a       :int   )		->    Any:






		"""simple docstring"""

		A__  				=       SwinConfig(image_size=1_9_2   )

		if "base" in model_name:
				A__  				=       6
				A__  				=       1_2_8
				A__  				=       (2, 2, 1_8, 2)
				A__  				=       (4, 8, 1_6, 3_2)
		elif "large" in model_name:
				A__  				=       1_2
				A__  				=       1_9_2
				A__  				=       (2, 2, 1_8, 2)
				A__  				=       (6, 1_2, 2_4, 4_8)
		else:
				raise ValueError("""Model not supported, only supports base and large variants"""   )

		A__  				=       window_size
		A__  				=       embed_dim
		A__  				=       depths
		A__  				=       num_heads

		return config







def        __lowerCamelCase (							__a       :List[str]   )		->    List[Any]:






		"""simple docstring"""

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

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

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

		return name







def        __lowerCamelCase (							__a       :Dict   ,					__a       :Any   )		->    List[Any]:






		"""simple docstring"""

		for key in orig_state_dict.copy().keys():
				A__  				=       orig_state_dict.pop(__a   )

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

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

		return orig_state_dict







def        __lowerCamelCase (							__a       :str   ,					__a       :str   ,					__a       :List[Any]   ,					__a       :int   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       torch.load(__a   ,					map_location="""cpu"""   )["""model"""]

		A__  				=       get_swin_config(__a   )
		A__  				=       SwinForMaskedImageModeling(__a   )
		model.eval()

		A__  				=       convert_state_dict(__a   ,					__a   )
		model.load_state_dict(__a   )

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

		A__  				=       ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2}   )
		A__  				=       Image.open(requests.get(__a   ,					stream=__a   ).raw   )
		A__  				=       image_processor(images=__a   ,					return_tensors="""pt"""   )

		with torch.no_grad():
				A__  				=       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   : Union[str, 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[int]			=				parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import unittest

import numpy as np







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray | None = None   ,					)		->    np.ndarray:






		"""simple docstring"""

		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )

		if shape_a[0] != shape_b[0]:
				A__  				=       (
				    """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(__a   )

		if shape_b[1] != shape_c[1]:
				A__  				=       (
				    """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(__a   )

		A__  				=       pseudo_inv
		if a_inv is None:
				try:
						A__  				=       np.linalg.inv(__a   )
				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   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   Union[str, Any]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					A__  				=       schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       np.block([[a, b], [b.T, c]]       )

					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )

					self.assertAlmostEqual(__lowerCAmelCase							,     det_a * det_s       )






			def        a_	(      self       :   str       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

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







			def        a_	(      self       :   List[str]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1, 3], [6, 3, 5]]       )

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					unittest.main()



