


'''simple docstring'''



import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     SwinvaConfig()
						UpperCAmelCase					    =     swinva_name.split('''_'''					)

						UpperCAmelCase					    =     name_split[1]
						if "to" in name_split[3]:
												UpperCAmelCase					    =     int(name_split[3][-3:]					)
						else:
												UpperCAmelCase					    =     int(name_split[3]					)
						if "to" in name_split[2]:
												UpperCAmelCase					    =     int(name_split[2][-2:]					)
						else:
												UpperCAmelCase					    =     int(name_split[2][6:]					)

						if model_size == "tiny":
												UpperCAmelCase					    =     96
												UpperCAmelCase					    =     (2, 2, 6, 2)
												UpperCAmelCase					    =     (3, 6, 12, 24)
						elif model_size == "small":
												UpperCAmelCase					    =     96
												UpperCAmelCase					    =     (2, 2, 18, 2)
												UpperCAmelCase					    =     (3, 6, 12, 24)
						elif model_size == "base":
												UpperCAmelCase					    =     1_28
												UpperCAmelCase					    =     (2, 2, 18, 2)
												UpperCAmelCase					    =     (4, 8, 16, 32)
						else:
												UpperCAmelCase					    =     1_92
												UpperCAmelCase					    =     (2, 2, 18, 2)
												UpperCAmelCase					    =     (6, 12, 24, 48)

						if "to" in swinva_name:
												UpperCAmelCase					    =     (12, 12, 12, 6)

						if ("22k" in swinva_name) and ("to" not in swinva_name):
												UpperCAmelCase					    =     2_18_41
												UpperCAmelCase					    =     '''huggingface/label-files'''
												UpperCAmelCase					    =     '''imagenet-22k-id2label.json'''
												UpperCAmelCase					    =     json.load(open(hf_hub_download(A				,       A				,       repo_type='''dataset'''					)				,       '''r'''					)					)
												UpperCAmelCase					    =     {int(A					): v for k, v in idalabel.items()}
												UpperCAmelCase					    =     idalabel
												UpperCAmelCase					    =     {v: k for k, v in idalabel.items()}

						else:
												UpperCAmelCase					    =     10_00
												UpperCAmelCase					    =     '''huggingface/label-files'''
												UpperCAmelCase					    =     '''imagenet-1k-id2label.json'''
												UpperCAmelCase					    =     json.load(open(hf_hub_download(A				,       A				,       repo_type='''dataset'''					)				,       '''r'''					)					)
												UpperCAmelCase					    =     {int(A					): v for k, v in idalabel.items()}
												UpperCAmelCase					    =     idalabel
												UpperCAmelCase					    =     {v: k for k, v in idalabel.items()}

						UpperCAmelCase					    =     img_size
						UpperCAmelCase					    =     num_classes
						UpperCAmelCase					    =     embed_dim
						UpperCAmelCase					    =     depths
						UpperCAmelCase					    =     num_heads
						UpperCAmelCase					    =     window_size

						return config


def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						if "patch_embed.proj" in name:
												UpperCAmelCase					    =     name.replace('''patch_embed.proj'''				,       '''embeddings.patch_embeddings.projection'''					)
						if "patch_embed.norm" in name:
												UpperCAmelCase					    =     name.replace('''patch_embed.norm'''				,       '''embeddings.norm'''					)
						if "layers" in name:
												UpperCAmelCase					    =     '''encoder.''' + name
						if "attn.proj" in name:
												UpperCAmelCase					    =     name.replace('''attn.proj'''				,       '''attention.output.dense'''					)
						if "attn" in name:
												UpperCAmelCase					    =     name.replace('''attn'''				,       '''attention.self'''					)
						if "norm1" in name:
												UpperCAmelCase					    =     name.replace('''norm1'''				,       '''layernorm_before'''					)
						if "norm2" in name:
												UpperCAmelCase					    =     name.replace('''norm2'''				,       '''layernorm_after'''					)
						if "mlp.fc1" in name:
												UpperCAmelCase					    =     name.replace('''mlp.fc1'''				,       '''intermediate.dense'''					)
						if "mlp.fc2" in name:
												UpperCAmelCase					    =     name.replace('''mlp.fc2'''				,       '''output.dense'''					)
						if "q_bias" in name:
												UpperCAmelCase					    =     name.replace('''q_bias'''				,       '''query.bias'''					)
						if "k_bias" in name:
												UpperCAmelCase					    =     name.replace('''k_bias'''				,       '''key.bias'''					)
						if "v_bias" in name:
												UpperCAmelCase					    =     name.replace('''v_bias'''				,       '''value.bias'''					)
						if "cpb_mlp" in name:
												UpperCAmelCase					    =     name.replace('''cpb_mlp'''				,       '''continuous_position_bias_mlp'''					)
						if name == "norm.weight":
												UpperCAmelCase					    =     '''layernorm.weight'''
						if name == "norm.bias":
												UpperCAmelCase					    =     '''layernorm.bias'''

						if "head" in name:
												UpperCAmelCase					    =     name.replace('''head'''				,       '''classifier'''					)
						else:
												UpperCAmelCase					    =     '''swinv2.''' + name

						return name


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Tuple					):




						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												UpperCAmelCase					    =     orig_state_dict.pop(A					)

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		UpperCAmelCase					    =     key.split('''.'''					)
																		UpperCAmelCase					    =     int(key_split[1]					)
																		UpperCAmelCase					    =     int(key_split[3]					)
																		UpperCAmelCase					    =     model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								UpperCAmelCase					    =     val[:dim, :]
																								UpperCAmelCase					    =     val[dim : dim * 2, :]
																								UpperCAmelCase					    =     val[-dim:, :]
																		else:
																								UpperCAmelCase					    =     val[:dim]
																								UpperCAmelCase					    =     val[
																								    dim : dim * 2
																								]
																								UpperCAmelCase					    =     val[-dim:]
												else:
																		UpperCAmelCase					    =     val

						return orig_state_dict


def 				lowerCamelCase__				(	A       :      str				,       A       :      List[str]					):




						'''simple docstring'''






						UpperCAmelCase					    =     timm.create_model(A				,       pretrained=A					)
						timm_model.eval()

						UpperCAmelCase					    =     get_swinva_config(A					)
						UpperCAmelCase					    =     SwinvaForImageClassification(A					)
						model.eval()

						UpperCAmelCase					    =     convert_state_dict(timm_model.state_dict()				,       A					)
						model.load_state_dict(A					)

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

						UpperCAmelCase					    =     AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''				,       '''-'''					)					)					)
						UpperCAmelCase					    =     Image.open(requests.get(A				,       stream=A					).raw					)
						UpperCAmelCase					    =     image_processor(images=A				,       return_tensors='''pt'''					)

						UpperCAmelCase					    =     timm_model(inputs['''pixel_values''']					)
						UpperCAmelCase					    =     model(**A					).logits

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

						print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""					)
						model.save_pretrained(A					)

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

						model.push_to_hub(
						    repo_path_or_name=Path(A				,       A					)				,       organization='''nandwalritik'''				,       commit_message='''Add model'''				,       )


if __name__ == "__main__":
	_lowercase      :      Any            =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--swinv2_name""",
	    default="""swinv2_tiny_patch4_window8_256""",
	    type=str,
	    help="""Name of the Swinv2 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."""
	)

	_lowercase      :      Dict            =					parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









'''simple docstring'''



def 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						return int(input_a == input_a == 0					)


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						print('''Truth Table of NOR Gate:'''					)
						print('''|   Input 1   |  Input 2  | Output |'''					)
						print(f"""|      0      |    0      |  {nor_gate(0				,       0					)}     |"""					)
						print(f"""|      0      |    1      |  {nor_gate(0				,       1					)}     |"""					)
						print(f"""|      1      |    0      |  {nor_gate(1				,       0					)}     |"""					)
						print(f"""|      1      |    1      |  {nor_gate(1				,       1					)}     |"""					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





