
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_   (	lowerCamelCase   ):
				__magic_name__	:	List[str]								=SwinConfig()
				__magic_name__	:	Optional[int]								=swin_name.split("""_"""   )

				__magic_name__	:	Union[str, Any]								=name_split[1]
				__magic_name__	:	Union[str, Any]								=int(name_split[4]   )
				__magic_name__	:	Union[str, Any]								=int(name_split[3][-1]   )

				if model_size == "tiny":
								__magic_name__	:	List[str]								=96
								__magic_name__	:	Tuple								=(2, 2, 6, 2)
								__magic_name__	:	Union[str, Any]								=(3, 6, 12, 24)
				elif model_size == "small":
								__magic_name__	:	Dict								=96
								__magic_name__	:	Union[str, Any]								=(2, 2, 18, 2)
								__magic_name__	:	Optional[Any]								=(3, 6, 12, 24)
				elif model_size == "base":
								__magic_name__	:	List[Any]								=128
								__magic_name__	:	Union[str, Any]								=(2, 2, 18, 2)
								__magic_name__	:	Any								=(4, 8, 16, 32)
				else:
								__magic_name__	:	List[str]								=192
								__magic_name__	:	Union[str, Any]								=(2, 2, 18, 2)
								__magic_name__	:	Dict								=(6, 12, 24, 48)

				if "in22k" in swin_name:
								__magic_name__	:	str								=21841
				else:
								__magic_name__	:	Union[str, Any]								=1000
								__magic_name__	:	Dict								="""huggingface/label-files"""
								__magic_name__	:	str								="""imagenet-1k-id2label.json"""
								__magic_name__	:	Dict								=json.load(open(hf_hub_download(lowerCamelCase	,     lowerCamelCase	,     repo_type="""dataset"""   )	,     """r"""   )   )
								__magic_name__	:	Optional[int]								={int(lowerCamelCase   ): v for k, v in idalabel.items()}
								__magic_name__	:	Dict								=idalabel
								__magic_name__	:	Any								={v: k for k, v in idalabel.items()}

				__magic_name__	:	Any								=img_size
				__magic_name__	:	int								=num_classes
				__magic_name__	:	Dict								=embed_dim
				__magic_name__	:	List[Any]								=depths
				__magic_name__	:	Tuple								=num_heads
				__magic_name__	:	Optional[int]								=window_size

				return config




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

				if name == "norm.weight":
								__magic_name__	:	int								="""layernorm.weight"""
				if name == "norm.bias":
								__magic_name__	:	Optional[int]								="""layernorm.bias"""

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

				return name




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				for key in orig_state_dict.copy().keys():
								__magic_name__	:	Optional[int]								=orig_state_dict.pop(lowerCamelCase   )

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

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

				return orig_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	List[Any]								=timm.create_model(lowerCamelCase	,     pretrained=lowerCamelCase   )
				timm_model.eval()

				__magic_name__	:	Optional[int]								=get_swin_config(lowerCamelCase   )
				__magic_name__	:	Optional[int]								=SwinForImageClassification(lowerCamelCase   )
				model.eval()

				__magic_name__	:	List[Any]								=convert_state_dict(timm_model.state_dict()	,     lowerCamelCase   )
				model.load_state_dict(lowerCamelCase   )

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

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

				__magic_name__	:	Dict								=timm_model(inputs["""pixel_values"""]   )
				__magic_name__	:	Dict								=model(**lowerCamelCase   ).logits

				assert torch.allclose(lowerCamelCase	,     lowerCamelCase	,     atol=1E-3   )

				print(F"Saving model {swin_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 __name__ == "__main__":
							UpperCAmelCase_		:				str									=     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."
							)

							UpperCAmelCase_		:				Union[str, Any]									=     parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










import numpy as np




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = 1E-12	,     lowerCamelCase = 100	,     ):
				assert np.shape(lowerCamelCase   )[0] == np.shape(lowerCamelCase   )[1]
				# Ensure proper dimensionality.
				assert np.shape(lowerCamelCase   )[0] == np.shape(lowerCamelCase   )[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowerCamelCase   ) == np.iscomplexobj(lowerCamelCase   )
				__magic_name__	:	List[Any]								=np.iscomplexobj(lowerCamelCase   )
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowerCamelCase	,     input_matrix.conj().T   )

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__magic_name__	:	List[Any]								=False
				__magic_name__	:	List[Any]								=0
				__magic_name__	:	List[str]								=0
				__magic_name__	:	str								=1E12

				while not convergence:
								# Multiple matrix by the vector.
								__magic_name__	:	Dict								=np.dot(lowerCamelCase	,     lowerCamelCase   )
								# Normalize the resulting output vector.
								__magic_name__	:	List[str]								=w / np.linalg.norm(lowerCamelCase   )
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__magic_name__	:	Any								=vector.conj().T if is_complex else vector.T
								__magic_name__	:	List[str]								=np.dot(lowerCamelCase	,     np.dot(lowerCamelCase	,     lowerCamelCase   )   )

								# Check convergence.
								__magic_name__	:	List[str]								=np.abs(lambda_ - lambda_previous   ) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__magic_name__	:	int								=True

								__magic_name__	:	Optional[Any]								=lambda_

				if is_complex:
								__magic_name__	:	Union[str, Any]								=np.real(lambda_   )

				return lambda_, vector




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Optional[int]								=np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]   )
				__magic_name__	:	Any								=np.array([41, 4, 20]   )
				__magic_name__	:	Optional[Any]								=real_input_matrix.astype(np.complexaaa   )
				__magic_name__	:	str								=np.triu(1J * complex_input_matrix	,     1   )
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__magic_name__	:	List[Any]								=np.array([41, 4, 20]   ).astype(np.complexaaa   )

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__magic_name__	:	Union[str, Any]								=real_input_matrix
												__magic_name__	:	List[Any]								=real_vector
								elif problem_type == "complex":
												__magic_name__	:	Optional[Any]								=complex_input_matrix
												__magic_name__	:	Tuple								=complex_vector

								# Our implementation.
								__magic_name__				, __magic_name__	:	Any								=power_iteration(lowerCamelCase	,     lowerCamelCase   )

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__magic_name__				, __magic_name__	:	int								=np.linalg.eigh(lowerCamelCase   )
								# Last eigenvalue is the maximum one.
								__magic_name__	:	List[str]								=eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__magic_name__	:	Dict								=eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max   ) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowerCamelCase   ) - np.abs(lowerCamelCase   )   ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




