
"""simple docstring"""





import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_a														=		logging.get_logger(__name__)

_a														=		torch.device("""cpu""")



def       lowerCamelCase__		(			)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase							 =  Image.open(requests.get(__snake_case,     stream=__snake_case	).raw	)
					return im



def       lowerCamelCase__		(			__snake_case	)							->		Any:


					"""simple docstring"""





					if swiftformer_name == "swiftformer_xs":
										return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]	)

					elif swiftformer_name == "swiftformer_s":
										return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]	)

					elif swiftformer_name == "swiftformer_l1":
										return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]	)

					elif swiftformer_name == "swiftformer_l3":
										return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  dct.pop(__snake_case	)
					_UpperCamelCase							 =  val



def       lowerCamelCase__		(			__snake_case	)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  []
					for k in state_dict.keys():
										_UpperCamelCase							 =  k
										if ".pwconv" in k:
															_UpperCamelCase							 =  k_new.replace('''.pwconv''',     '''.point_wise_conv'''	)
										if ".dwconv" in k:
															_UpperCamelCase							 =  k_new.replace('''.dwconv''',     '''.depth_wise_conv'''	)
										if ".Proj." in k:
															_UpperCamelCase							 =  k_new.replace('''.Proj.''',     '''.proj.'''	)
										if "patch_embed" in k_new:
															_UpperCamelCase							 =  k_new.replace('''patch_embed''',     '''swiftformer.patch_embed.patch_embedding'''	)
										if "network" in k_new:
															_UpperCamelCase							 =  k_new.split('''.'''	)
															if ls[2].isdigit():
																				_UpperCamelCase							 =  '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]	)
															else:
																				_UpperCamelCase							 =  k_new.replace('''network''',     '''swiftformer.encoder.network'''	)
										rename_keys.append((k, k_new)	)
					return rename_keys



@torch.no_grad()
def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					_UpperCamelCase							 =  10_00
					_UpperCamelCase							 =  '''huggingface/label-files'''
					_UpperCamelCase							 =  '''imagenet-1k-id2label.json'''
					_UpperCamelCase							 =  json.load(open(hf_hub_download(__snake_case,     __snake_case,     repo_type='''dataset'''	),     '''r'''	)	)
					_UpperCamelCase							 =  {int(__snake_case	): v for k, v in idalabel.items()}
					_UpperCamelCase							 =  idalabel
					_UpperCamelCase							 =  {v: k for k, v in idalabel.items()}

					# size of the architecture
					if swiftformer_name == "swiftformer_xs":
										_UpperCamelCase							 =  [3, 3, 6, 4]
										_UpperCamelCase							 =  [48, 56, 1_12, 2_20]

					elif swiftformer_name == "swiftformer_s":
										_UpperCamelCase							 =  [3, 3, 9, 6]
										_UpperCamelCase							 =  [48, 64, 1_68, 2_24]

					elif swiftformer_name == "swiftformer_l1":
										_UpperCamelCase							 =  [4, 3, 10, 5]
										_UpperCamelCase							 =  [48, 96, 1_92, 3_84]

					elif swiftformer_name == "swiftformer_l3":
										_UpperCamelCase							 =  [4, 4, 12, 6]
										_UpperCamelCase							 =  [64, 1_28, 3_20, 5_12]

					# load state_dict of original model, remove and rename some keys
					if original_ckpt:
										if original_ckpt.startswith('''https'''	):
															_UpperCamelCase							 =  torch.hub.load_state_dict_from_url(__snake_case,     map_location='''cpu''',     check_hash=__snake_case	)
										else:
															_UpperCamelCase							 =  torch.load(__snake_case,     map_location='''cpu'''	)
					_UpperCamelCase							 =  checkpoint

					_UpperCamelCase							 =  create_rename_keys(__snake_case	)
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(__snake_case,     __snake_case,     __snake_case	)

					# load HuggingFace model
					_UpperCamelCase							 =  SwiftFormerForImageClassification(__snake_case	).eval()
					hf_model.load_state_dict(__snake_case	)

					# prepare test inputs
					_UpperCamelCase							 =  prepare_img()
					_UpperCamelCase							 =  ViTImageProcessor.from_pretrained('''preprocessor_config'''	)
					_UpperCamelCase							 =  processor(images=__snake_case,     return_tensors='''pt'''	)

					# compare outputs from both models
					_UpperCamelCase							 =  get_expected_output(__snake_case	)
					_UpperCamelCase							 =  hf_model(inputs['''pixel_values''']	).logits

					assert hf_logits.shape == torch.Size([1, 10_00]	)
					assert torch.allclose(hf_logits[0, 0:5],     __snake_case,     atol=1e-3	)

					Path(__snake_case	).mkdir(exist_ok=__snake_case	)
					print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''	)
					hf_model.save_pretrained(__snake_case	)


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--swiftformer_name""",
				    default="""swiftformer_xs""",
				    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
				    type=str,
				    help="""Name of the SwiftFormer model you'd like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""",
				    default="""./converted_outputs/""",
				    type=str,
				    help="""Path to the output PyTorch model directory.""",
				)
				parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

				_a														=		parser.parse_args()
				convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






