



"""simple docstring"""






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   snake_case__     (	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[int]				=[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
			lowerCamelCase__	:	Union[str, Any]				=True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase__	:	Optional[Any]				=True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase__	:	Optional[int]				=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__	:	Union[str, Any]				=[3, 3, 3, 3]
									lowerCamelCase__	:	Dict				=[5, 5, 5, 5]
						elif "fl4" in model_name:
									lowerCamelCase__	:	List[str]				=[4, 4, 4, 4]
									lowerCamelCase__	:	int				=[3, 3, 3, 3]

			if "tiny" in model_name or "small" in model_name or "base" in model_name:
						lowerCamelCase__	:	List[str]				=[3, 3, 3, 3]
						if "lrf" in model_name:
									lowerCamelCase__	:	Dict				=[3, 3, 3, 3]
						else:
									lowerCamelCase__	:	Optional[int]				=[2, 2, 2, 2]

			if "tiny" in model_name:
						lowerCamelCase__	:	Dict				=96
			elif "small" in model_name:
						lowerCamelCase__	:	Any				=96
			elif "base" in model_name:
						lowerCamelCase__	:	str				=128
			elif "large" in model_name:
						lowerCamelCase__	:	Tuple				=192
			elif "xlarge" in model_name:
						lowerCamelCase__	:	Optional[Any]				=256
			elif "huge" in model_name:
						lowerCamelCase__	:	Optional[Any]				=352

			# set label information
			lowerCamelCase__	:	List[Any]				='''huggingface/label-files'''
			if "large" in model_name or "huge" in model_name:
						lowerCamelCase__	:	int				='''imagenet-22k-id2label.json'''
			else:
						lowerCamelCase__	:	Optional[int]				='''imagenet-1k-id2label.json'''

			lowerCamelCase__	:	List[Any]				=json.load(open(hf_hub_download(__lowerCamelCase							,	__lowerCamelCase							,	repo_type='''dataset'''			)							,	'''r'''			)			)
			lowerCamelCase__	:	Dict				={int(__lowerCamelCase			): v for k, v in idalabel.items()}
			lowerCamelCase__	:	int				={v: k for k, v in idalabel.items()}

			lowerCamelCase__	:	int				=FocalNetConfig(
			    embed_dim=__lowerCamelCase							,	depths=__lowerCamelCase							,	focal_levels=__lowerCamelCase							,	focal_windows=__lowerCamelCase							,	use_conv_embed=__lowerCamelCase							,	idalabel=__lowerCamelCase							,	labelaid=__lowerCamelCase							,	use_post_layernorm=__lowerCamelCase							,	use_layerscale=__lowerCamelCase							,	)

			return config






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			if "patch_embed.proj" in name:
						lowerCamelCase__	:	Union[str, Any]				=name.replace('''patch_embed.proj'''							,	'''embeddings.patch_embeddings.projection'''			)
			if "patch_embed.norm" in name:
						lowerCamelCase__	:	Tuple				=name.replace('''patch_embed.norm'''							,	'''embeddings.norm'''			)
			if "layers" in name:
						lowerCamelCase__	:	str				='''encoder.''' + name
			if "encoder.layers" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''encoder.layers'''							,	'''encoder.stages'''			)
			if "downsample.proj" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''downsample.proj'''							,	'''downsample.projection'''			)
			if "blocks" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''blocks'''							,	'''layers'''			)
			if "modulation.f.weight" in name or "modulation.f.bias" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''modulation.f'''							,	'''modulation.projection_in'''			)
			if "modulation.h.weight" in name or "modulation.h.bias" in name:
						lowerCamelCase__	:	Union[str, Any]				=name.replace('''modulation.h'''							,	'''modulation.projection_context'''			)
			if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
						lowerCamelCase__	:	str				=name.replace('''modulation.proj'''							,	'''modulation.projection_out'''			)

			if name == "norm.weight":
						lowerCamelCase__	:	int				='''layernorm.weight'''
			if name == "norm.bias":
						lowerCamelCase__	:	int				='''layernorm.bias'''

			if "head" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''head'''							,	'''classifier'''			)
			else:
						lowerCamelCase__	:	Union[str, Any]				='''focalnet.''' + name

			return name






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					Optional[Any]=False			):
			"""simple docstring"""
			lowerCamelCase__	:	Dict				={
			    '''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__	:	Optional[Any]				=model_name_to_url[model_name]
			print('''Checkpoint URL: '''							,	__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=torch.hub.load_state_dict_from_url(__lowerCamelCase							,	map_location='''cpu'''			)['''model''']

			# rename keys
			for key in state_dict.copy().keys():
						lowerCamelCase__	:	List[Any]				=state_dict.pop(__lowerCamelCase			)
						lowerCamelCase__	:	Any				=val

			lowerCamelCase__	:	Tuple				=get_focalnet_config(__lowerCamelCase			)
			lowerCamelCase__	:	Optional[Any]				=FocalNetForImageClassification(__lowerCamelCase			)
			model.eval()

			# load state dict
			model.load_state_dict(__lowerCamelCase			)

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

			lowerCamelCase__	:	Dict				=BitImageProcessor(
			    do_resize=__lowerCamelCase							,	size={'''shortest_edge''': 256}							,	resample=PILImageResampling.BILINEAR							,	do_center_crop=__lowerCamelCase							,	crop_size=224							,	do_normalize=__lowerCamelCase							,	image_mean=__lowerCamelCase							,	image_std=__lowerCamelCase							,	)
			lowerCamelCase__	:	Any				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)
			lowerCamelCase__	:	Optional[int]				=processor(images=__lowerCamelCase							,	return_tensors='''pt'''			)

			lowerCamelCase__	:	List[str]				=transforms.Compose(
			    [
			        transforms.Resize(256			),
			        transforms.CenterCrop(224			),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]							,	std=[0.2_29, 0.2_24, 0.2_25]			),
			    ]			)

			lowerCamelCase__	:	Dict				=image_transforms(__lowerCamelCase			).unsqueeze(0			)

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

			lowerCamelCase__	:	Any				=model(**__lowerCamelCase			)

			lowerCamelCase__	:	List[str]				=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__	:	Tuple				=torch.tensor([0.21_66, -0.43_68, 0.21_91]			)
			elif model_name == "focalnet-tiny-lrf":
						lowerCamelCase__	:	Union[str, Any]				=torch.tensor([1.16_69, 0.01_25, -0.16_95]			)
			elif model_name == "focalnet-small":
						lowerCamelCase__	:	Optional[Any]				=torch.tensor([0.49_17, -0.04_30, 0.13_41]			)
			elif model_name == "focalnet-small-lrf":
						lowerCamelCase__	:	Any				=torch.tensor([-0.25_88, -0.53_42, -0.23_31]			)
			elif model_name == "focalnet-base":
						lowerCamelCase__	:	Any				=torch.tensor([-0.16_55, -0.40_90, -0.17_30]			)
			elif model_name == "focalnet-base-lrf":
						lowerCamelCase__	:	Tuple				=torch.tensor([0.53_06, -0.04_83, -0.39_28]			)
			assert torch.allclose(outputs.logits[0, :3]							,	__lowerCamelCase							,	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(__lowerCamelCase			)
						processor.save_pretrained(__lowerCamelCase			)

			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__":
		_lowercase				:				str   		=		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.",
		)

		_lowercase				:				Any   		=		parser.parse_args()
		convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			return " ".join(
			    ''''''.join(word[::-1]			) if len(__lowerCamelCase			) > 4 else word for word in sentence.split()			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words("Hey wollef sroirraw"))




