





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 		__SCREAMING_SNAKE_CASE    (							UpperCamelCase__					)    ->							Optional[int]:







			'''simple docstring'''

			UpperCAmelCase  						=							[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
			UpperCAmelCase  						=							True if '''large''' in model_name or '''huge''' in model_name else False
			UpperCAmelCase  						=							True if '''large''' in model_name or '''huge''' in model_name else False
			UpperCAmelCase  						=							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:
									UpperCAmelCase  						=							[3, 3, 3, 3]
									UpperCAmelCase  						=							[5, 5, 5, 5]
						elif "fl4" in model_name:
									UpperCAmelCase  						=							[4, 4, 4, 4]
									UpperCAmelCase  						=							[3, 3, 3, 3]

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

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

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

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

			UpperCAmelCase  						=							FocalNetConfig(
			    embed_dim=UpperCamelCase__     ,     depths=UpperCamelCase__     ,     focal_levels=UpperCamelCase__     ,     focal_windows=UpperCamelCase__     ,     use_conv_embed=UpperCamelCase__     ,     idalabel=UpperCamelCase__     ,     labelaid=UpperCamelCase__     ,     use_post_layernorm=UpperCamelCase__     ,     use_layerscale=UpperCamelCase__     ,     )

			return config





def 		__SCREAMING_SNAKE_CASE    (							UpperCamelCase__					)    ->							str:







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

			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  						=							'''focalnet.''' + name

			return name





def 		__SCREAMING_SNAKE_CASE    (							UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__=False					)    ->							str:







			'''simple docstring'''

			UpperCAmelCase  						=							{
			    '''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

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

			# rename keys
			for key in state_dict.copy().keys():
						UpperCAmelCase  						=							state_dict.pop(UpperCamelCase__					)
						UpperCAmelCase  						=							val

			UpperCAmelCase  						=							get_focalnet_config(UpperCamelCase__					)
			UpperCAmelCase  						=							FocalNetForImageClassification(UpperCamelCase__					)
			model.eval()

			# load state dict
			model.load_state_dict(UpperCamelCase__					)

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

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

			UpperCAmelCase  						=							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]					),
			    ]					)

			UpperCAmelCase  						=							image_transforms(UpperCamelCase__					).unsqueeze(0					)

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

			UpperCAmelCase  						=							model(**UpperCamelCase__					)

			UpperCAmelCase  						=							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":
						UpperCAmelCase  						=							torch.tensor([0.2166, -0.4368, 0.2191]					)
			elif model_name == "focalnet-tiny-lrf":
						UpperCAmelCase  						=							torch.tensor([1.1669, 0.0125, -0.1695]					)
			elif model_name == "focalnet-small":
						UpperCAmelCase  						=							torch.tensor([0.4917, -0.0430, 0.1341]					)
			elif model_name == "focalnet-small-lrf":
						UpperCAmelCase  						=							torch.tensor([-0.2588, -0.5342, -0.2331]					)
			elif model_name == "focalnet-base":
						UpperCAmelCase  						=							torch.tensor([-0.1655, -0.4090, -0.1730]					)
			elif model_name == "focalnet-base-lrf":
						UpperCAmelCase  						=							torch.tensor([0.5306, -0.0483, -0.3928]					)
			assert torch.allclose(outputs.logits[0, :3]     ,     UpperCamelCase__     ,     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(UpperCamelCase__					)
						processor.save_pretrained(UpperCamelCase__					)

			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__":
				__A							:       Optional[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.",
				)

				__A							:       str         =  parser.parse_args()
				convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import re






def _lowerCamelCase  (		snake_case  ):

							if len(re.findall('[ATCG]'     , snake_case  )  ) != len(snake_case  ):
														raise ValueError('Invalid Strand'  )

							return dna.translate(dna.maketrans('ATCG'     , 'TAGC'  )  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






