

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def        snake_case__		(    lowerCamelCase_		):
							if "cls_token" in name:
														A      :					str      		=					name.replace('''cls_token'''     ,				'''vit.embeddings.cls_token'''		)
							if "mask_token" in name:
														A      :					Tuple      		=					name.replace('''mask_token'''     ,				'''decoder.mask_token'''		)
							if "decoder_pos_embed" in name:
														A      :					List[str]      		=					name.replace('''decoder_pos_embed'''     ,				'''decoder.decoder_pos_embed'''		)
							if "pos_embed" in name and "decoder" not in name:
														A      :					str      		=					name.replace('''pos_embed'''     ,				'''vit.embeddings.position_embeddings'''		)
							if "patch_embed.proj" in name:
														A      :					Union[str, Any]      		=					name.replace('''patch_embed.proj'''     ,				'''vit.embeddings.patch_embeddings.projection'''		)
							if "patch_embed.norm" in name:
														A      :					Tuple      		=					name.replace('''patch_embed.norm'''     ,				'''vit.embeddings.norm'''		)
							if "decoder_blocks" in name:
														A      :					int      		=					name.replace('''decoder_blocks'''     ,				'''decoder.decoder_layers'''		)
							if "blocks" in name:
														A      :					List[str]      		=					name.replace('''blocks'''     ,				'''vit.encoder.layer'''		)
							if "attn.proj" in name:
														A      :					str      		=					name.replace('''attn.proj'''     ,				'''attention.output.dense'''		)
							if "attn" in name:
														A      :					List[Any]      		=					name.replace('''attn'''     ,				'''attention.self'''		)
							if "norm1" in name:
														A      :					Union[str, Any]      		=					name.replace('''norm1'''     ,				'''layernorm_before'''		)
							if "norm2" in name:
														A      :					Optional[int]      		=					name.replace('''norm2'''     ,				'''layernorm_after'''		)
							if "mlp.fc1" in name:
														A      :					int      		=					name.replace('''mlp.fc1'''     ,				'''intermediate.dense'''		)
							if "mlp.fc2" in name:
														A      :					List[Any]      		=					name.replace('''mlp.fc2'''     ,				'''output.dense'''		)
							if "decoder_embed" in name:
														A      :					int      		=					name.replace('''decoder_embed'''     ,				'''decoder.decoder_embed'''		)
							if "decoder_norm" in name:
														A      :					List[Any]      		=					name.replace('''decoder_norm'''     ,				'''decoder.decoder_norm'''		)
							if "decoder_pred" in name:
														A      :					str      		=					name.replace('''decoder_pred'''     ,				'''decoder.decoder_pred'''		)
							if "norm.weight" in name and "decoder" not in name:
														A      :					int      		=					name.replace('''norm.weight'''     ,				'''vit.layernorm.weight'''		)
							if "norm.bias" in name and "decoder" not in name:
														A      :					List[Any]      		=					name.replace('''norm.bias'''     ,				'''vit.layernorm.bias'''		)

							return name






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							for key in orig_state_dict.copy().keys():
														A      :					List[str]      		=					orig_state_dict.pop(lowercase_		)

														if "qkv" in key:
																					A      :					Tuple      		=					key.split('''.'''		)
																					A      :					Tuple      		=					int(key_split[1]		)
																					if "decoder_blocks" in key:
																												A      :					Union[str, Any]      		=					config.decoder_hidden_size
																												A      :					int      		=					'''decoder.decoder_layers.'''
																												if "weight" in key:
																																			A      :					Any      		=					val[:dim, :]
																																			A      :					Tuple      		=					val[dim : dim * 2, :]
																																			A      :					Any      		=					val[-dim:, :]
																												elif "bias" in key:
																																			A      :					Optional[int]      		=					val[:dim]
																																			A      :					Optional[int]      		=					val[dim : dim * 2]
																																			A      :					Dict      		=					val[-dim:]
																					else:
																												A      :					Optional[Any]      		=					config.hidden_size
																												A      :					Optional[int]      		=					'''vit.encoder.layer.'''
																												if "weight" in key:
																																			A      :					Optional[Any]      		=					val[:dim, :]
																																			A      :					Optional[Any]      		=					val[dim : dim * 2, :]
																																			A      :					Optional[Any]      		=					val[-dim:, :]
																												elif "bias" in key:
																																			A      :					Any      		=					val[:dim]
																																			A      :					Dict      		=					val[dim : dim * 2]
																																			A      :					Tuple      		=					val[-dim:]

														else:
																					A      :					str      		=					val

							return orig_state_dict






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					ViTMAEConfig()
							if "large" in checkpoint_url:
														A      :					List[str]      		=					1024
														A      :					Optional[Any]      		=					4096
														A      :					Optional[int]      		=					24
														A      :					str      		=					16
							elif "huge" in checkpoint_url:
														A      :					List[str]      		=					14
														A      :					Tuple      		=					1280
														A      :					Optional[Any]      		=					5120
														A      :					Any      		=					32
														A      :					str      		=					16

							A      :					List[str]      		=					ViTMAEForPreTraining(lowercase_		)

							A      :					List[Any]      		=					torch.hub.load_state_dict_from_url(lowercase_     ,				map_location='''cpu'''		)['''model''']

							A      :					Optional[Any]      		=					ViTMAEImageProcessor(size=config.image_size		)

							A      :					str      		=					convert_state_dict(lowercase_     ,				lowercase_		)

							model.load_state_dict(lowercase_		)
							model.eval()

							A      :					str      		=					'''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

							A      :					Any      		=					Image.open(requests.get(lowercase_     ,				stream=lowercase_		).raw		)
							A      :					Dict      		=					ViTMAEImageProcessor(size=config.image_size		)
							A      :					Union[str, Any]      		=					image_processor(images=lowercase_     ,				return_tensors='''pt'''		)

							# forward pass
							torch.manual_seed(2		)
							A      :					Tuple      		=					model(**lowercase_		)
							A      :					str      		=					outputs.logits

							if "large" in checkpoint_url:
														A      :					Any      		=					torch.tensor(
														    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]		)
							elif "huge" in checkpoint_url:
														A      :					Union[str, Any]      		=					torch.tensor(
														    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]		)
							else:
														A      :					Tuple      		=					torch.tensor(
														    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]		)

							# verify logits
							assert torch.allclose(logits[0, :3, :3]     ,				lowercase_     ,				atol=1E-4		)

							print(F'Saving model to {pytorch_dump_folder_path}'		)
							model.save_pretrained(lowercase_		)

							print(F'Saving image processor to {pytorch_dump_folder_path}'		)
							image_processor.save_pretrained(lowercase_		)


if __name__ == "__main__":
		lowercase			:				Dict   =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint_url",
		    default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
		    type=str,
		    help="URL of the checkpoint you\'d like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)

		lowercase			:				Optional[int]   =     parser.parse_args()
		convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













import inspect
from typing import List, Optional, Tuple, Union

import torch

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







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()
             self.register_modules(vqvae=a__   ,				unet=a__   ,				scheduler=a__)



      @torch.no_grad()
      def __call__(		self   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = "pil"   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,				generator=a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		latents.to(self.device)

             # scale the initial noise by the standard deviation required by the scheduler
             _lowerCamelCase				:  Tuple						=		latents * self.scheduler.init_noise_sigma

             self.scheduler.set_timesteps(a__)

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _lowerCamelCase				:  Optional[int]						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

             _lowerCamelCase				:  Union[str, Any]						=		{}
             if accepts_eta:
                    _lowerCamelCase				:  List[Any]						=		eta

             for t in self.progress_bar(self.scheduler.timesteps):
                    _lowerCamelCase				:  List[Any]						=		self.scheduler.scale_model_input(a__   ,				a__)
                    # predict the noise residual
                    _lowerCamelCase				:  Optional[int]						=		self.unet(a__   ,				a__).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase				:  List[str]						=		self.scheduler.step(a__   ,				a__   ,				a__   ,				**a__).prev_sample

             # decode the image latents with the VAE
             _lowerCamelCase				:  Optional[int]						=		self.vqvae.decode(a__).sample

             _lowerCamelCase				:  Dict						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  List[str]						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  List[Any]						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)

