
"""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 __A						(   a_ :							str     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= SwinConfig()
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= swin_name.split('''_'''     )

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

		if model_size == "tiny":
				SCREAMING_SNAKE_CASE       :     List[Any]				= 96
				SCREAMING_SNAKE_CASE       :     Dict				= (2, 2, 6, 2)
				SCREAMING_SNAKE_CASE       :     List[Any]				= (3, 6, 12, 24)
		elif model_size == "small":
				SCREAMING_SNAKE_CASE       :     Tuple				= 96
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (3, 6, 12, 24)
		elif model_size == "base":
				SCREAMING_SNAKE_CASE       :     List[Any]				= 1_28
				SCREAMING_SNAKE_CASE       :     Optional[int]				= (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE       :     Any				= (4, 8, 16, 32)
		else:
				SCREAMING_SNAKE_CASE       :     List[str]				= 1_92
				SCREAMING_SNAKE_CASE       :     Dict				= (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE       :     int				= (6, 12, 24, 48)

		if "in22k" in swin_name:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= 2_18_41
		else:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= 10_00
				SCREAMING_SNAKE_CASE       :     List[str]				= '''huggingface/label-files'''
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE       :     Any				= json.load(open(hf_hub_download(a_		,		a_		,		repo_type='''dataset'''     )		,		'''r'''     )     )
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {int(a_     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= idalabel
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= {v: k for k, v in idalabel.items()}

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

		return config





def __A						(   a_ :							str     )->							Optional[Any]:




		'''simple docstring'''






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

		if name == "norm.weight":
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= '''layernorm.weight'''
		if name == "norm.bias":
				SCREAMING_SNAKE_CASE       :     Optional[int]				= '''layernorm.bias'''

		if "head" in name:
				SCREAMING_SNAKE_CASE       :     Tuple				= name.replace('''head'''		,		'''classifier'''     )
		else:
				SCREAMING_SNAKE_CASE       :     Tuple				= '''swin.''' + name

		return name





def __A						(   a_ :							Any		,		a_ :							Any     )->							str:




		'''simple docstring'''






		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE       :     List[Any]				= orig_state_dict.pop(a_     )

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

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

		return orig_state_dict





def __A						(   a_ :							Optional[int]		,		a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= timm.create_model(a_		,		pretrained=a_     )
		timm_model.eval()

		SCREAMING_SNAKE_CASE       :     Any				= get_swin_config(a_     )
		SCREAMING_SNAKE_CASE       :     Any				= SwinForImageClassification(a_     )
		model.eval()

		SCREAMING_SNAKE_CASE       :     Optional[int]				= convert_state_dict(timm_model.state_dict()		,		a_     )
		model.load_state_dict(a_     )

		SCREAMING_SNAKE_CASE       :     List[str]				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

		SCREAMING_SNAKE_CASE       :     str				= timm_model(inputs['''pixel_values''']     )
		SCREAMING_SNAKE_CASE       :     List[str]				= model(**a_     ).logits

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

		print(F"Saving model {swin_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_     )


if __name__ == "__main__":
					lowerCamelCase__ :       Optional[Any]						     =			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__ :       List[Any]						     =			parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




"""simple docstring"""






import math





def __A						(   a_ :							list		,		a_ :							int     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= len(a_     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= int(math.floor(math.sqrt(a_     )     )     )
		SCREAMING_SNAKE_CASE       :     List[str]				= 0
		while arr[min(a_		,		a_     ) - 1] < x:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= step
				step += int(math.floor(math.sqrt(a_     )     )     )
				if prev >= n:
						return -1

		while arr[prev] < x:
				SCREAMING_SNAKE_CASE       :     Any				= prev + 1
				if prev == min(a_		,		a_     ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
					lowerCamelCase__ :       Union[str, Any]						     =			input("Enter numbers separated by a comma:\n").strip()
					lowerCamelCase__ :       List[str]						     =			[int(item) for item in user_input.split(",")]
					lowerCamelCase__ :       Dict						     =			int(input("Enter the number to be searched:\n"))
					lowerCamelCase__ :       Tuple						     =			jump_search(arr, x)
					if res == -1:
										print("Number not found!")
					else:
										print(f'''Number {x} is at index {res}''')





