


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						if "cls_token" in name:
												__snake_case				=		name.replace("cls_token"      ,	"vit.embeddings.cls_token" )
						if "mask_token" in name:
												__snake_case				=		name.replace("mask_token"      ,	"decoder.mask_token" )
						if "decoder_pos_embed" in name:
												__snake_case				=		name.replace("decoder_pos_embed"      ,	"decoder.decoder_pos_embed" )
						if "pos_embed" in name and "decoder" not in name:
												__snake_case				=		name.replace("pos_embed"      ,	"vit.embeddings.position_embeddings" )
						if "patch_embed.proj" in name:
												__snake_case				=		name.replace("patch_embed.proj"      ,	"vit.embeddings.patch_embeddings.projection" )
						if "patch_embed.norm" in name:
												__snake_case				=		name.replace("patch_embed.norm"      ,	"vit.embeddings.norm" )
						if "decoder_blocks" in name:
												__snake_case				=		name.replace("decoder_blocks"      ,	"decoder.decoder_layers" )
						if "blocks" in name:
												__snake_case				=		name.replace("blocks"      ,	"vit.encoder.layer" )
						if "attn.proj" in name:
												__snake_case				=		name.replace("attn.proj"      ,	"attention.output.dense" )
						if "attn" in name:
												__snake_case				=		name.replace("attn"      ,	"attention.self" )
						if "norm1" in name:
												__snake_case				=		name.replace("norm1"      ,	"layernorm_before" )
						if "norm2" in name:
												__snake_case				=		name.replace("norm2"      ,	"layernorm_after" )
						if "mlp.fc1" in name:
												__snake_case				=		name.replace("mlp.fc1"      ,	"intermediate.dense" )
						if "mlp.fc2" in name:
												__snake_case				=		name.replace("mlp.fc2"      ,	"output.dense" )
						if "decoder_embed" in name:
												__snake_case				=		name.replace("decoder_embed"      ,	"decoder.decoder_embed" )
						if "decoder_norm" in name:
												__snake_case				=		name.replace("decoder_norm"      ,	"decoder.decoder_norm" )
						if "decoder_pred" in name:
												__snake_case				=		name.replace("decoder_pred"      ,	"decoder.decoder_pred" )
						if "norm.weight" in name and "decoder" not in name:
												__snake_case				=		name.replace("norm.weight"      ,	"vit.layernorm.weight" )
						if "norm.bias" in name and "decoder" not in name:
												__snake_case				=		name.replace("norm.bias"      ,	"vit.layernorm.bias" )

						return name





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												__snake_case				=		orig_state_dict.pop(_lowerCAmelCase )

												if "qkv" in key:
																		__snake_case				=		key.split("." )
																		__snake_case				=		int(key_split[1] )
																		if "decoder_blocks" in key:
																								__snake_case				=		config.decoder_hidden_size
																								__snake_case				=		"decoder.decoder_layers."
																								if "weight" in key:
																														__snake_case				=		val[:dim, :]
																														__snake_case				=		val[dim : dim * 2, :]
																														__snake_case				=		val[-dim:, :]
																								elif "bias" in key:
																														__snake_case				=		val[:dim]
																														__snake_case				=		val[dim : dim * 2]
																														__snake_case				=		val[-dim:]
																		else:
																								__snake_case				=		config.hidden_size
																								__snake_case				=		"vit.encoder.layer."
																								if "weight" in key:
																														__snake_case				=		val[:dim, :]
																														__snake_case				=		val[dim : dim * 2, :]
																														__snake_case				=		val[-dim:, :]
																								elif "bias" in key:
																														__snake_case				=		val[:dim]
																														__snake_case				=		val[dim : dim * 2]
																														__snake_case				=		val[-dim:]

												else:
																		__snake_case				=		val

						return orig_state_dict





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						__snake_case				=		ViTMAEConfig()
						if "large" in checkpoint_url:
												__snake_case				=		1024
												__snake_case				=		4096
												__snake_case				=		24
												__snake_case				=		16
						elif "huge" in checkpoint_url:
												__snake_case				=		14
												__snake_case				=		1280
												__snake_case				=		5120
												__snake_case				=		32
												__snake_case				=		16

						__snake_case				=		ViTMAEForPreTraining(_lowerCAmelCase )

						__snake_case				=		torch.hub.load_state_dict_from_url(_lowerCAmelCase      ,	map_location="cpu" )["model"]

						__snake_case				=		ViTMAEImageProcessor(size=config.image_size )

						__snake_case				=		convert_state_dict(_lowerCAmelCase      ,	_lowerCAmelCase )

						model.load_state_dict(_lowerCAmelCase )
						model.eval()

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

						__snake_case				=		Image.open(requests.get(_lowerCAmelCase      ,	stream=_lowerCAmelCase ).raw )
						__snake_case				=		ViTMAEImageProcessor(size=config.image_size )
						__snake_case				=		image_processor(images=_lowerCAmelCase      ,	return_tensors="pt" )

						# forward pass
						torch.manual_seed(2 )
						__snake_case				=		model(**_lowerCAmelCase )
						__snake_case				=		outputs.logits

						if "large" in checkpoint_url:
												__snake_case				=		torch.tensor(
												    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]] )
						elif "huge" in checkpoint_url:
												__snake_case				=		torch.tensor(
												    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]] )
						else:
												__snake_case				=		torch.tensor(
												    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]] )

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

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

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


if __name__ == "__main__":
	A							:					str     						=     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.'
	)

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









import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A							:					int     						=     logging.get_logger(__name__)  # pylint: disable=invalid-name






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

											# uncondition for scaling
											__snake_case				=		nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





