






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

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

							if "tiny" in model_name:
														snake_case_           =				96
							elif "small" in model_name:
														snake_case_           =				96
							elif "base" in model_name:
														snake_case_           =				1_28
							elif "large" in model_name:
														snake_case_           =				1_92
							elif "xlarge" in model_name:
														snake_case_           =				2_56
							elif "huge" in model_name:
														snake_case_           =				3_52

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

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

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

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

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

							return name





def 		__UpperCAmelCase     (	a_				,	a_				,	a_=False):
							# fmt: off
							snake_case_           =				{
							    '''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

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

							# rename keys
							for key in state_dict.copy().keys():
														snake_case_           =				state_dict.pop(A_)
														snake_case_           =				val

							snake_case_           =				get_focalnet_config(A_)
							snake_case_           =				FocalNetForImageClassification(A_)
							model.eval()

							# load state dict
							model.load_state_dict(A_)

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

							snake_case_           =				BitImageProcessor(
							    do_resize=A_				,	size={'shortest_edge': 2_56}				,	resample=PILImageResampling.BILINEAR				,	do_center_crop=A_				,	crop_size=2_24				,	do_normalize=A_				,	image_mean=A_				,	image_std=A_				,	)
							snake_case_           =				Image.open(requests.get(A_				,	stream=A_).raw)
							snake_case_           =				processor(images=A_				,	return_tensors='pt')

							snake_case_           =				transforms.Compose(
							    [
							        transforms.Resize(2_56),
							        transforms.CenterCrop(2_24),
							        transforms.ToTensor(),
							        transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,	std=[0.2_29, 0.2_24, 0.2_25]),
							    ])

							snake_case_           =				image_transforms(A_).unsqueeze(0)

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

							snake_case_           =				model(**A_)

							snake_case_           =				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":
														snake_case_           =				torch.tensor([0.21_66, -0.43_68, 0.21_91])
							elif model_name == "focalnet-tiny-lrf":
														snake_case_           =				torch.tensor([1.16_69, 0.01_25, -0.16_95])
							elif model_name == "focalnet-small":
														snake_case_           =				torch.tensor([0.49_17, -0.04_30, 0.13_41])
							elif model_name == "focalnet-small-lrf":
														snake_case_           =				torch.tensor([-0.25_88, -0.53_42, -0.23_31])
							elif model_name == "focalnet-base":
														snake_case_           =				torch.tensor([-0.16_55, -0.40_90, -0.17_30])
							elif model_name == "focalnet-base-lrf":
														snake_case_           =				torch.tensor([0.53_06, -0.04_83, -0.39_28])
							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__":
	lowercase    				=   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    				=   parser.parse_args()
	convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42
					lowercase__												=			42







					def __init__(			self     :							int  ,lowercase_     :							UNetaDModel  ,lowercase_     :							ScoreSdeVeScheduler						):
						super().__init__()
						self.register_modules(unet=lowercase_  ,scheduler=lowercase_						)







					@torch.no_grad()
					def __call__(			self     :							List[str]  ,lowercase_     :							int = 1  ,lowercase_     :							int = 2_0_0_0  ,lowercase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowercase_     :							Optional[str] = "pil"  ,lowercase_     :							bool = True  ,**lowercase_     :							Dict  ,):
						lowerCAmelCase__      :		str    							=      self.unet.config.sample_size
						lowerCAmelCase__      :		int    							=      (batch_size, 3, img_size, img_size)

						lowerCAmelCase__      :		List[Any]    							=      self.unet

						lowerCAmelCase__      :		Tuple    							=      randn_tensor(lowercase_  ,generator=lowercase_						) * self.scheduler.init_noise_sigma
						lowerCAmelCase__      :		List[Any]    							=      sample.to(self.device						)

						self.scheduler.set_timesteps(lowercase_						)
						self.scheduler.set_sigmas(lowercase_						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
							lowerCAmelCase__      :		Tuple    							=      self.scheduler.sigmas[i] * torch.ones(shape[0]  ,device=self.device						)

							# correction step
							for _ in range(self.scheduler.config.correct_steps						):
								lowerCAmelCase__      :		str    							=      self.unet(lowercase_  ,lowercase_						).sample
								lowerCAmelCase__      :		List[str]    							=      self.scheduler.step_correct(lowercase_  ,lowercase_  ,generator=lowercase_						).prev_sample

							# prediction step
							lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,lowercase_						).sample
							lowerCAmelCase__      :		Optional[int]    							=      self.scheduler.step_pred(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)

							lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      output.prev_sample, output.prev_sample_mean

						lowerCAmelCase__      :		List[str]    							=      sample_mean.clamp(0  ,1						)
						lowerCAmelCase__      :		Union[str, Any]    							=      sample.cpu().permute(0  ,2  ,3  ,1						).numpy()
						if output_type == "pil":
							lowerCAmelCase__      :		Union[str, Any]    							=      self.numpy_to_pil(lowercase_						)

						if not return_dict:
							return (sample,)

						return ImagePipelineOutput(images=lowercase_						)




