



'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]  )      ->   Any:
							UpperCAmelCase :       Union[str, Any] 							=		SwinConfig(image_size=192  )

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

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

							return config


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

							if name == "encoder.norm.weight":
														UpperCAmelCase :       List[Any] 							=		'''layernorm.weight'''
							if name == "encoder.norm.bias":
														UpperCAmelCase :       List[Any] 							=		'''layernorm.bias'''

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

							return name


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Tuple  )      ->   Dict:
							for key in orig_state_dict.copy().keys():
														UpperCAmelCase :       List[str] 							=		orig_state_dict.pop(_lowerCAmelCase  )

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

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

							return orig_state_dict


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						int  )      ->   int:
							UpperCAmelCase :       int 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )['''model''']

							UpperCAmelCase :       Dict 							=		get_swin_config(_lowerCAmelCase  )
							UpperCAmelCase :       Any 							=		SwinForMaskedImageModeling(_lowerCAmelCase  )
							model.eval()

							UpperCAmelCase :       str 							=		convert_state_dict(_lowerCAmelCase   ,      _lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )

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

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

							with torch.no_grad():
														UpperCAmelCase :       Dict 							=		model(**_lowerCAmelCase  ).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(_lowerCAmelCase  )

														print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
														image_processor.save_pretrained(_lowerCAmelCase  )

							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__:   List[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."
						)

						UpperCamelCase__:   Tuple								=			parser.parse_args()
						convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''






from math import isclose, sqrt


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float  )      ->   tuple[float, float, float]:
							UpperCAmelCase :       Optional[int] 							=		point_y / 4 / point_x
							UpperCAmelCase :       str 							=		2 * normal_gradient / (1 + normal_gradient * normal_gradient)
							UpperCAmelCase :       Any 							=		(1 - normal_gradient * normal_gradient) / (
							    1 + normal_gradient * normal_gradient
							)
							UpperCAmelCase :       Union[str, Any] 							=		(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

							# to find the next point, solve the simultaeneous equations:
							# y^2 + 4x^2 = 100
							# y - b = m * (x - a)
							# ==> A x^2 + B x + C = 0
							UpperCAmelCase :       Union[str, Any] 							=		outgoing_gradient**2 + 4
							UpperCAmelCase :       Dict 							=		2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
							UpperCAmelCase :       List[Any] 							=		(point_y - outgoing_gradient * point_x) ** 2 - 100

							UpperCAmelCase :       List[str] 							=		(
							    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)
							UpperCAmelCase :       Optional[int] 							=		(
							    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)

							# two solutions, one of which is our input point
							UpperCAmelCase :       Optional[Any] 							=		x_minus if isclose(_lowerCAmelCase   ,      _lowerCAmelCase  ) else x_plus
							UpperCAmelCase :       Union[str, Any] 							=		point_y + outgoing_gradient * (next_x - point_x)

							return next_x, next_y, outgoing_gradient


def  snake_case_     (					_lowerCAmelCase  :						float = 1.4   ,      _lowerCAmelCase  :						float = -9.6  )      ->   int:
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       float 							=		first_x_coord
							UpperCAmelCase :       float 							=		first_y_coord
							UpperCAmelCase :       float 							=		(1_0.1 - point_y) / (0.0 - point_x)

							while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		next_point(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														num_reflections += 1

							return num_reflections


if __name__ == "__main__":
						print(F"{solution() = }")





