

'''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 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						SwinConfig()
	_UpperCamelCase   :		Optional[int]			=						swin_name.split('_'	)

	_UpperCamelCase   :		str			=						name_split[1]
	_UpperCamelCase   :		str			=						int(name_split[4]	)
	_UpperCamelCase   :		Optional[Any]			=						int(name_split[3][-1]	)

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

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

	_UpperCamelCase   :		Dict			=						img_size
	_UpperCamelCase   :		int			=						num_classes
	_UpperCamelCase   :		Union[str, Any]			=						embed_dim
	_UpperCamelCase   :		List[Any]			=						depths
	_UpperCamelCase   :		Optional[Any]			=						num_heads
	_UpperCamelCase   :		Tuple			=						window_size

	return config






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]	)						->				int:




	'''simple docstring'''




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

	if name == "norm.weight":
		_UpperCamelCase   :		Dict			=						'layernorm.weight'
	if name == "norm.bias":
		_UpperCamelCase   :		int			=						'layernorm.bias'

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

	return name






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     str	)						->				int:




	'''simple docstring'''




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

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

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

	return orig_state_dict






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     int	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						timm.create_model(UpperCAmelCase_							,       pretrained=UpperCAmelCase_	)
	timm_model.eval()

	_UpperCamelCase   :		Optional[Any]			=						get_swin_config(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						SwinForImageClassification(UpperCAmelCase_	)
	model.eval()

	_UpperCamelCase   :		Any			=						convert_state_dict(timm_model.state_dict()							,       UpperCAmelCase_	)
	model.load_state_dict(UpperCAmelCase_	)

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

	_UpperCamelCase   :		Optional[Any]			=						AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'							,       '-'	)	)	)
	_UpperCamelCase   :		Any			=						Image.open(requests.get(UpperCAmelCase_							,       stream=UpperCAmelCase_	).raw	)
	_UpperCamelCase   :		Optional[int]			=						image_processor(images=UpperCAmelCase_							,       return_tensors='pt'	)

	_UpperCamelCase   :		Union[str, Any]			=						timm_model(inputs['pixel_values']	)
	_UpperCamelCase   :		int			=						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__":
							lowerCAmelCase__								=		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."""
							)

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







from __future__ import annotations

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

# initial triangle of Koch snowflake
lowerCAmelCase__								=		numpy.array([0, 0])
lowerCAmelCase__								=		numpy.array([0.5, 0.8_66_02_54])
lowerCAmelCase__								=		numpy.array([1, 0])
lowerCAmelCase__								=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]							,       UpperCAmelCase_							:     int	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						initial_vectors
	for _ in range(UpperCAmelCase_	):
		_UpperCamelCase   :		str			=						iteration_step(UpperCAmelCase_	)
	return vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						[]
	for i, start_vector in enumerate(vectors[:-1]	):
		_UpperCamelCase   :		Union[str, Any]			=						vectors[i + 1]
		new_vectors.append(UpperCAmelCase_	)
		_UpperCamelCase   :		Tuple			=						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 					lowerCamelCase_     (							UpperCAmelCase_							:     numpy.ndarray							,       UpperCAmelCase_							:     float	)						->				numpy.ndarray:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						numpy.radians(UpperCAmelCase_	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						numpy.cos(UpperCAmelCase_	), numpy.sin(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						numpy.array(((c, -s), (s, c))	)
	return numpy.dot(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				None:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						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   :		Dict			=						zip(*UpperCAmelCase_	)
	plt.plot(UpperCAmelCase_							,       UpperCAmelCase_	)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

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




