
'''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_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					str													=	[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
						lowercase_     :					str													=	True if '''large''' in model_name or '''huge''' in model_name else False
						lowercase_     :					Union[str, Any]													=	True if '''large''' in model_name or '''huge''' in model_name else False
						lowercase_     :					Optional[Any]													=	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:
																		lowercase_     :					List[str]													=	[3, 3, 3, 3]
																		lowercase_     :					Tuple													=	[5, 5, 5, 5]
												elif "fl4" in model_name:
																		lowercase_     :					str													=	[4, 4, 4, 4]
																		lowercase_     :					Optional[Any]													=	[3, 3, 3, 3]

						if "tiny" in model_name or "small" in model_name or "base" in model_name:
												lowercase_     :					Union[str, Any]													=	[3, 3, 3, 3]
												if "lrf" in model_name:
																		lowercase_     :					Optional[int]													=	[3, 3, 3, 3]
												else:
																		lowercase_     :					Optional[int]													=	[2, 2, 2, 2]

						if "tiny" in model_name:
												lowercase_     :					Union[str, Any]													=	96
						elif "small" in model_name:
												lowercase_     :					Dict													=	96
						elif "base" in model_name:
												lowercase_     :					Optional[int]													=	128
						elif "large" in model_name:
												lowercase_     :					int													=	192
						elif "xlarge" in model_name:
												lowercase_     :					str													=	256
						elif "huge" in model_name:
												lowercase_     :					List[Any]													=	352

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

						lowercase_     :					Tuple													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''							)				,				'''r'''							)							)
						lowercase_     :					int													=	{int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
						lowercase_     :					Optional[Any]													=	{v: k for k, v in idalabel.items()}

						lowercase_     :					List[Any]													=	FocalNetConfig(
						    embed_dim=__SCREAMING_SNAKE_CASE				,				depths=__SCREAMING_SNAKE_CASE				,				focal_levels=__SCREAMING_SNAKE_CASE				,				focal_windows=__SCREAMING_SNAKE_CASE				,				use_conv_embed=__SCREAMING_SNAKE_CASE				,				idalabel=__SCREAMING_SNAKE_CASE				,				labelaid=__SCREAMING_SNAKE_CASE				,				use_post_layernorm=__SCREAMING_SNAKE_CASE				,				use_layerscale=__SCREAMING_SNAKE_CASE				,				)

						return config
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





						if "patch_embed.proj" in name:
												lowercase_     :					Optional[Any]													=	name.replace('''patch_embed.proj'''				,				'''embeddings.patch_embeddings.projection'''							)
						if "patch_embed.norm" in name:
												lowercase_     :					int													=	name.replace('''patch_embed.norm'''				,				'''embeddings.norm'''							)
						if "layers" in name:
												lowercase_     :					Optional[Any]													=	'''encoder.''' + name
						if "encoder.layers" in name:
												lowercase_     :					Tuple													=	name.replace('''encoder.layers'''				,				'''encoder.stages'''							)
						if "downsample.proj" in name:
												lowercase_     :					Optional[int]													=	name.replace('''downsample.proj'''				,				'''downsample.projection'''							)
						if "blocks" in name:
												lowercase_     :					List[Any]													=	name.replace('''blocks'''				,				'''layers'''							)
						if "modulation.f.weight" in name or "modulation.f.bias" in name:
												lowercase_     :					Optional[Any]													=	name.replace('''modulation.f'''				,				'''modulation.projection_in'''							)
						if "modulation.h.weight" in name or "modulation.h.bias" in name:
												lowercase_     :					List[Any]													=	name.replace('''modulation.h'''				,				'''modulation.projection_context'''							)
						if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
												lowercase_     :					Dict													=	name.replace('''modulation.proj'''				,				'''modulation.projection_out'''							)

						if name == "norm.weight":
												lowercase_     :					str													=	'''layernorm.weight'''
						if name == "norm.bias":
												lowercase_     :					List[str]													=	'''layernorm.bias'''

						if "head" in name:
												lowercase_     :					List[Any]													=	name.replace('''head'''				,				'''classifier'''							)
						else:
												lowercase_     :					Tuple													=	'''focalnet.''' + name

						return name
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			List[str]=False							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	{
						    '''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

						lowercase_     :					List[str]													=	model_name_to_url[model_name]
						print('''Checkpoint URL: '''				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					int													=	torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE				,				map_location='''cpu'''							)['''model''']

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

						lowercase_     :					Tuple													=	get_focalnet_config(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					int													=	FocalNetForImageClassification(__SCREAMING_SNAKE_CASE							)
						model.eval()

						# load state dict
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						# verify conversion
						lowercase_     :					List[str]													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

						lowercase_     :					Union[str, Any]													=	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]							),
						    ]							)

						lowercase_     :					Any													=	image_transforms(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)

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

						lowercase_     :					Tuple													=	model(**__SCREAMING_SNAKE_CASE							)

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

						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			:  Optional[int]   							=	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'''







from PIL import Image
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Image				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	(259 * (level + 255)) / (255 * (259 - level))

						def contrast(__SCREAMING_SNAKE_CASE :			int							) -> int:
												return int(128 + factor * (c - 128)							)

						return img.point(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change contrast to 170
														_lowercase			:  Union[str, Any]   							=	change_contrast(img, 1_7_0)
														cont_img.save("image_data/lena_high_contrast.png", format="png")



