


'''simple docstring'''



import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[Any]        =     SwinConfig()
			UpperCAmelCase__  :					Union[str, Any]        =     swin_name.split("""_"""					)

			UpperCAmelCase__  :					List[str]        =     name_split[1]
			UpperCAmelCase__  :					Any        =     int(name_split[4]					)
			UpperCAmelCase__  :					List[Any]        =     int(name_split[3][-1]					)

			if model_size == "tiny":
						UpperCAmelCase__  :					str        =     9_6
						UpperCAmelCase__  :					int        =     (2, 2, 6, 2)
						UpperCAmelCase__  :					Optional[Any]        =     (3, 6, 1_2, 2_4)
			elif model_size == "small":
						UpperCAmelCase__  :					Tuple        =     9_6
						UpperCAmelCase__  :					Optional[Any]        =     (2, 2, 1_8, 2)
						UpperCAmelCase__  :					str        =     (3, 6, 1_2, 2_4)
			elif model_size == "base":
						UpperCAmelCase__  :					str        =     1_2_8
						UpperCAmelCase__  :					int        =     (2, 2, 1_8, 2)
						UpperCAmelCase__  :					Any        =     (4, 8, 1_6, 3_2)
			else:
						UpperCAmelCase__  :					Tuple        =     1_9_2
						UpperCAmelCase__  :					Union[str, Any]        =     (2, 2, 1_8, 2)
						UpperCAmelCase__  :					List[Any]        =     (6, 1_2, 2_4, 4_8)

			if "in22k" in swin_name:
						UpperCAmelCase__  :					Tuple        =     2_1_8_4_1
			else:
						UpperCAmelCase__  :					Dict        =     1_0_0_0
						UpperCAmelCase__  :					Optional[int]        =     """huggingface/label-files"""
						UpperCAmelCase__  :					List[str]        =     """imagenet-1k-id2label.json"""
						UpperCAmelCase__  :					int        =     json.load(open(hf_hub_download(UpperCamelCase__ ,      UpperCamelCase__ ,      repo_type="""dataset"""					) ,      """r"""					)					)
						UpperCAmelCase__  :					int        =     {int(UpperCamelCase__					): v for k, v in idalabel.items()}
						UpperCAmelCase__  :					int        =     idalabel
						UpperCAmelCase__  :					Dict        =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase__  :					Any        =     img_size
			UpperCAmelCase__  :					Tuple        =     num_classes
			UpperCAmelCase__  :					str        =     embed_dim
			UpperCAmelCase__  :					Optional[int]        =     depths
			UpperCAmelCase__  :					Dict        =     num_heads
			UpperCAmelCase__  :					Optional[Any]        =     window_size

			return config







def     _UpperCamelCase      (		UpperCamelCase__					):
			if "patch_embed.proj" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""patch_embed.proj""" ,      """embeddings.patch_embeddings.projection"""					)
			if "patch_embed.norm" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""patch_embed.norm""" ,      """embeddings.norm"""					)
			if "layers" in name:
						UpperCAmelCase__  :					Dict        =     """encoder.""" + name
			if "attn.proj" in name:
						UpperCAmelCase__  :					List[str]        =     name.replace("""attn.proj""" ,      """attention.output.dense"""					)
			if "attn" in name:
						UpperCAmelCase__  :					List[str]        =     name.replace("""attn""" ,      """attention.self"""					)
			if "norm1" in name:
						UpperCAmelCase__  :					str        =     name.replace("""norm1""" ,      """layernorm_before"""					)
			if "norm2" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""norm2""" ,      """layernorm_after"""					)
			if "mlp.fc1" in name:
						UpperCAmelCase__  :					List[str]        =     name.replace("""mlp.fc1""" ,      """intermediate.dense"""					)
			if "mlp.fc2" in name:
						UpperCAmelCase__  :					List[str]        =     name.replace("""mlp.fc2""" ,      """output.dense"""					)

			if name == "norm.weight":
						UpperCAmelCase__  :					List[str]        =     """layernorm.weight"""
			if name == "norm.bias":
						UpperCAmelCase__  :					Any        =     """layernorm.bias"""

			if "head" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""head""" ,      """classifier"""					)
			else:
						UpperCAmelCase__  :					Union[str, Any]        =     """swin.""" + name

			return name







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase__  :					Optional[Any]        =     orig_state_dict.pop(UpperCamelCase__					)

						if "mask" in key:
									continue
						elif "qkv" in key:
									UpperCAmelCase__  :					List[str]        =     key.split("""."""					)
									UpperCAmelCase__  :					List[Any]        =     int(key_split[1]					)
									UpperCAmelCase__  :					Optional[Any]        =     int(key_split[3]					)
									UpperCAmelCase__  :					Any        =     model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

			return orig_state_dict







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     timm.create_model(UpperCamelCase__ ,      pretrained=UpperCamelCase__					)
			timm_model.eval()

			UpperCAmelCase__  :					Union[str, Any]        =     get_swin_config(UpperCamelCase__					)
			UpperCAmelCase__  :					List[str]        =     SwinForImageClassification(UpperCamelCase__					)
			model.eval()

			UpperCAmelCase__  :					Dict        =     convert_state_dict(timm_model.state_dict() ,      UpperCamelCase__					)
			model.load_state_dict(UpperCamelCase__					)

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

			UpperCAmelCase__  :					List[Any]        =     AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,      """-"""					)					)					)
			UpperCAmelCase__  :					str        =     Image.open(requests.get(UpperCamelCase__ ,      stream=UpperCamelCase__					).raw					)
			UpperCAmelCase__  :					List[str]        =     image_processor(images=UpperCamelCase__ ,      return_tensors="""pt"""					)

			UpperCAmelCase__  :					List[str]        =     timm_model(inputs["""pixel_values"""]					)
			UpperCAmelCase__  :					Tuple        =     model(**UpperCamelCase__					).logits

			assert torch.allclose(UpperCamelCase__ ,      UpperCamelCase__ ,      atol=1e-3					)

			print(f'''Saving model {swin_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 __name__ == "__main__":
		__A	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--swin_name',
		    default='swin_tiny_patch4_window7_224',
		    type=str,
		    help='Name of the Swin timm model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)

		__A	=parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



'''simple docstring'''



from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__A	=numpy.array([0, 0])
__A	=numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A	=numpy.array([1, 0])
__A	=[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     initial_vectors
			for _ in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     iteration_step(UpperCamelCase__					)
			return vectors







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     []
			for i, start_vector in enumerate(vectors[:-1]					):
						UpperCAmelCase__  :					Tuple        =     vectors[i + 1]
						new_vectors.append(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     end_vector - start_vector
						new_vectors.append(start_vector + difference_vector / 3					)
						new_vectors.append(
						    start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,      6_0					)					)
						new_vectors.append(start_vector + difference_vector * 2 / 3					)
			new_vectors.append(vectors[-1]					)
			return new_vectors







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     numpy.radians(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     numpy.cos(UpperCamelCase__					), numpy.sin(UpperCamelCase__					)
			UpperCAmelCase__  :					Dict        =     numpy.array(((c, -s), (s, c))					)
			return numpy.dot(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     plt.gca()
			axes.set_aspect("""equal"""					)

			# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
			# y-coordinates as inputs, which are constructed from the vector-list using
			# zip()
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     zip(*UpperCamelCase__					)
			plt.plot(UpperCamelCase__ ,      UpperCamelCase__					)
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__A	=iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)