



import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def     UpperCAmelCase_		(					snake_case__					)	->     Tuple:



				"""simple docstring"""





				lowerCAmelCase__		  =  [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
				lowerCAmelCase__		  =  True if 'large' in model_name or 'huge' in model_name else False
				lowerCAmelCase__		  =  True if 'large' in model_name or 'huge' in model_name else False
				lowerCAmelCase__		  =  True if 'large' in model_name or 'huge' in model_name else False

				if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
								if "fl3" in model_name:
												lowerCAmelCase__		  =  [3, 3, 3, 3]
												lowerCAmelCase__		  =  [5, 5, 5, 5]
								elif "fl4" in model_name:
												lowerCAmelCase__		  =  [4, 4, 4, 4]
												lowerCAmelCase__		  =  [3, 3, 3, 3]

				if "tiny" in model_name or "small" in model_name or "base" in model_name:
								lowerCAmelCase__		  =  [3, 3, 3, 3]
								if "lrf" in model_name:
												lowerCAmelCase__		  =  [3, 3, 3, 3]
								else:
												lowerCAmelCase__		  =  [2, 2, 2, 2]

				if "tiny" in model_name:
								lowerCAmelCase__		  =  96
				elif "small" in model_name:
								lowerCAmelCase__		  =  96
				elif "base" in model_name:
								lowerCAmelCase__		  =  128
				elif "large" in model_name:
								lowerCAmelCase__		  =  192
				elif "xlarge" in model_name:
								lowerCAmelCase__		  =  256
				elif "huge" in model_name:
								lowerCAmelCase__		  =  352

				# set label information
				lowerCAmelCase__		  =  'huggingface/label-files'
				if "large" in model_name or "huge" in model_name:
								lowerCAmelCase__		  =  'imagenet-22k-id2label.json'
				else:
								lowerCAmelCase__		  =  'imagenet-1k-id2label.json'

				lowerCAmelCase__		  =  json.load(open(hf_hub_download(A__    ,			A__    ,			repo_type='dataset'					)    ,			'r'					)					)
				lowerCAmelCase__		  =  {int(A__					): v for k, v in idalabel.items()}
				lowerCAmelCase__		  =  {v: k for k, v in idalabel.items()}

				lowerCAmelCase__		  =  FocalNetConfig(
				    embed_dim=A__    ,			depths=A__    ,			focal_levels=A__    ,			focal_windows=A__    ,			use_conv_embed=A__    ,			idalabel=A__    ,			labelaid=A__    ,			use_post_layernorm=A__    ,			use_layerscale=A__    ,			)

				return config







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				if "patch_embed.proj" in name:
								lowerCAmelCase__		  =  name.replace('patch_embed.proj'    ,			'embeddings.patch_embeddings.projection'					)
				if "patch_embed.norm" in name:
								lowerCAmelCase__		  =  name.replace('patch_embed.norm'    ,			'embeddings.norm'					)
				if "layers" in name:
								lowerCAmelCase__		  =  'encoder.' + name
				if "encoder.layers" in name:
								lowerCAmelCase__		  =  name.replace('encoder.layers'    ,			'encoder.stages'					)
				if "downsample.proj" in name:
								lowerCAmelCase__		  =  name.replace('downsample.proj'    ,			'downsample.projection'					)
				if "blocks" in name:
								lowerCAmelCase__		  =  name.replace('blocks'    ,			'layers'					)
				if "modulation.f.weight" in name or "modulation.f.bias" in name:
								lowerCAmelCase__		  =  name.replace('modulation.f'    ,			'modulation.projection_in'					)
				if "modulation.h.weight" in name or "modulation.h.bias" in name:
								lowerCAmelCase__		  =  name.replace('modulation.h'    ,			'modulation.projection_context'					)
				if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
								lowerCAmelCase__		  =  name.replace('modulation.proj'    ,			'modulation.projection_out'					)

				if name == "norm.weight":
								lowerCAmelCase__		  =  'layernorm.weight'
				if name == "norm.bias":
								lowerCAmelCase__		  =  'layernorm.bias'

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

				return name







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__=False					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  {
				    'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
				    'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
				    'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
				    'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
				    'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
				    'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
				    'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
				    'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
				    'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
				    'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
				}
				# fmt: on

				lowerCAmelCase__		  =  model_name_to_url[model_name]
				print('Checkpoint URL: '    ,			A__					)
				lowerCAmelCase__		  =  torch.hub.load_state_dict_from_url(A__    ,			map_location='cpu'					)['model']

				# rename keys
				for key in state_dict.copy().keys():
								lowerCAmelCase__		  =  state_dict.pop(A__					)
								lowerCAmelCase__		  =  val

				lowerCAmelCase__		  =  get_focalnet_config(A__					)
				lowerCAmelCase__		  =  FocalNetForImageClassification(A__					)
				model.eval()

				# load state dict
				model.load_state_dict(A__					)

				# verify conversion
				lowerCAmelCase__		  =  'http://images.cocodataset.org/val2017/000000039769.jpg'

				lowerCAmelCase__		  =  BitImageProcessor(
				    do_resize=A__    ,			size={'shortest_edge': 256}    ,			resample=PILImageResampling.BILINEAR    ,			do_center_crop=A__    ,			crop_size=224    ,			do_normalize=A__    ,			image_mean=A__    ,			image_std=A__    ,			)
				lowerCAmelCase__		  =  Image.open(requests.get(A__    ,			stream=A__					).raw					)
				lowerCAmelCase__		  =  processor(images=A__    ,			return_tensors='pt'					)

				lowerCAmelCase__		  =  transforms.Compose(
				    [
				        transforms.Resize(256					),
				        transforms.CenterCrop(224					),
				        transforms.ToTensor(),
				        transforms.Normalize(mean=[0.485, 0.456, 0.406]    ,			std=[0.229, 0.224, 0.225]					),
				    ]					)

				lowerCAmelCase__		  =  image_transforms(A__					).unsqueeze(0					)

				# verify pixel_values
				assert torch.allclose(inputs.pixel_values    ,			A__    ,			atol=1E-4					)

				lowerCAmelCase__		  =  model(**A__					)

				lowerCAmelCase__		  =  outputs.logits.argmax(-1					).item()
				print('Predicted class:'    ,			model.config.idalabel[predicted_class_idx]					)

				print('First values of logits:'    ,			outputs.logits[0, :3]					)

				if model_name == "focalnet-tiny":
								lowerCAmelCase__		  =  torch.tensor([0.2166, -0.4368, 0.2191]					)
				elif model_name == "focalnet-tiny-lrf":
								lowerCAmelCase__		  =  torch.tensor([1.1669, 0.0125, -0.1695]					)
				elif model_name == "focalnet-small":
								lowerCAmelCase__		  =  torch.tensor([0.4917, -0.0430, 0.1341]					)
				elif model_name == "focalnet-small-lrf":
								lowerCAmelCase__		  =  torch.tensor([-0.2588, -0.5342, -0.2331]					)
				elif model_name == "focalnet-base":
								lowerCAmelCase__		  =  torch.tensor([-0.1655, -0.4090, -0.1730]					)
				elif model_name == "focalnet-base-lrf":
								lowerCAmelCase__		  =  torch.tensor([0.5306, -0.0483, -0.3928]					)
				assert torch.allclose(outputs.logits[0, :3]    ,			A__    ,			atol=1E-4					)
				print('Looks ok!'					)

				if pytorch_dump_folder_path is not None:
								print(f'Saving model and processor of {model_name} to {pytorch_dump_folder_path}'					)
								model.save_pretrained(A__					)
								processor.save_pretrained(A__					)

				if push_to_hub:
								print(f'Pushing model and processor of {model_name} to the hub...'					)
								model.push_to_hub(f'{model_name}'					)
								processor.push_to_hub(f'{model_name}'					)


if __name__ == "__main__":
	_lowerCAmelCase	:   List[Any]    =   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="focalnet-tiny",
	    type=str,
	    help="Name of the FocalNet 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."
	)
	parser.add_argument(
	    "--push_to_hub",
	    action="store_true",
	    help="Whether to push the model and processor to the hub.",
	)

	_lowerCAmelCase	:   Optional[int]    =   parser.parse_args()
	convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					''
			for i in table:
						res += inp[i - 1]
			return res






def 						a__			(				A__				):
			return data[1:] + data[0]






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					''
			for i in range(len(A__				)				):
						if a[i] == b[i]:
									res += "0"
						else:
									res += "1"
			return res






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					int('0b' + data[0] + data[-1],      2				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					int('0b' + data[1:3],      2				)
			return bin(s[row][col]				)[2:]






def 						a__			(				A__,      A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					message[:4]
			SCREAMING_SNAKE_CASE_		:     int					=					message[4:]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					apply_table(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					xor(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					apply_sbox(A__,      temp[:4]				)  # noqa: E741
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					apply_sbox(A__,      temp[4:]				)
			SCREAMING_SNAKE_CASE_		:     Any					=					'0' * (2 - len(A__				)) + l  # noqa: E741
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					'0' * (2 - len(A__				)) + r
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					apply_table(l + r,      A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					xor(A__,      A__				)
			return temp + right


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[Any]								=input('Enter 10 bit key: ')
				lowerCAmelCase__			:      int								=input('Enter 8 bit message: ')

				lowerCAmelCase__			:      Any								=[6, 3, 7, 4, 8, 5, 10, 9]
				lowerCAmelCase__			:      List[str]								=[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
				lowerCAmelCase__			:      Dict								=[2, 4, 3, 1]
				lowerCAmelCase__			:      Dict								=[2, 6, 3, 1, 4, 8, 5, 7]
				lowerCAmelCase__			:      Union[str, Any]								=[4, 1, 3, 5, 7, 2, 8, 6]
				lowerCAmelCase__			:      Dict								=[4, 1, 2, 3, 2, 3, 4, 1]
				lowerCAmelCase__			:      str								=[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
				lowerCAmelCase__			:      Any								=[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

				# key generation
				lowerCAmelCase__			:      Dict								=apply_table(key, paa_table)
				lowerCAmelCase__			:      List[str]								=temp[:5]
				lowerCAmelCase__			:      List[Any]								=temp[5:]
				lowerCAmelCase__			:      Tuple								=left_shift(left)
				lowerCAmelCase__			:      Optional[Any]								=left_shift(right)
				lowerCAmelCase__			:      Optional[int]								=apply_table(left + right, pa_table)
				lowerCAmelCase__			:      Optional[Any]								=left_shift(left)
				lowerCAmelCase__			:      Optional[Any]								=left_shift(right)
				lowerCAmelCase__			:      List[str]								=left_shift(left)
				lowerCAmelCase__			:      Optional[int]								=left_shift(right)
				lowerCAmelCase__			:      Tuple								=apply_table(left + right, pa_table)

				# encryption
				lowerCAmelCase__			:      Tuple								=apply_table(message, IP)
				lowerCAmelCase__			:      List[Any]								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      str								=temp[4:] + temp[:4]
				lowerCAmelCase__			:      Optional[Any]								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      Optional[int]								=apply_table(temp, IP_inv)
				print('Cipher text is:', CT)

				# decryption
				lowerCAmelCase__			:      Union[str, Any]								=apply_table(CT, IP)
				lowerCAmelCase__			:      Tuple								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      Union[str, Any]								=temp[4:] + temp[:4]
				lowerCAmelCase__			:      Dict								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      int								=apply_table(temp, IP_inv)
				print('Plain text after decypting is:', PT)





