






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



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

				return name



def  a__ (__lowercase							:Optional[int]					,  __lowercase							:str   )		-> Any:
				for key in orig_state_dict.copy().keys():
								_A	:				Any   =      orig_state_dict.pop(snake_case__   )

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

								else:
												_A	:				str   =      val

				return orig_state_dict



def  a__ (__lowercase							:Union[str, Any]					,  __lowercase							:int   )		-> List[str]:
				_A	:				Any   =      ViTMAEConfig()
				if "large" in checkpoint_url:
								_A	:				Optional[int]   =      1024
								_A	:				str   =      4096
								_A	:				Any   =      24
								_A	:				Dict   =      16
				elif "huge" in checkpoint_url:
								_A	:				Optional[int]   =      14
								_A	:				Any   =      1280
								_A	:				Optional[Any]   =      5120
								_A	:				Optional[int]   =      32
								_A	:				str   =      16

				_A	:				Optional[Any]   =      ViTMAEForPreTraining(snake_case__   )

				_A	:				str   =      torch.hub.load_state_dict_from_url(snake_case__					,  map_location='''cpu'''   )["""model"""]

				_A	:				List[str]   =      ViTMAEImageProcessor(size=config.image_size   )

				_A	:				List[Any]   =      convert_state_dict(snake_case__					,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

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

				_A	:				List[Any]   =      Image.open(requests.get(snake_case__					,  stream=snake_case__   ).raw   )
				_A	:				List[Any]   =      ViTMAEImageProcessor(size=config.image_size   )
				_A	:				List[Any]   =      image_processor(images=snake_case__					,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				_A	:				Optional[Any]   =      model(**snake_case__   )
				_A	:				Tuple   =      outputs.logits

				if "large" in checkpoint_url:
								_A	:				List[str]   =      torch.tensor(
								    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]   )
				elif "huge" in checkpoint_url:
								_A	:				Optional[Any]   =      torch.tensor(
								    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]   )
				else:
								_A	:				Dict   =      torch.tensor(
								    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]   )

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

				print(f"""Saving model to {pytorch_dump_folder_path}"""   )
				model.save_pretrained(snake_case__   )

				print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
				image_processor.save_pretrained(snake_case__   )


if __name__ == "__main__":
	_UpperCamelCase			:	Optional[int]								=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.'
	)

	_UpperCamelCase			:	Union[str, Any]								=parser.parse_args()
	convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    IFPipeline
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_BATCH_PARAMS
     __SCREAMING_SNAKE_CASE     					=    PipelineTesterMixin.required_optional_params - {'''latents'''}


     def        __snake_case(  self    ):
       return self._get_dummy_components()


     def        __snake_case(  self							,					A_							,					A_=0    ):
       if str(A_    ).startswith("""mps"""    ):
         _UpperCAmelCase		:							Tuple							=     torch.manual_seed(A_    )
       else:
         _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device=A_    ).manual_seed(A_    )

       _UpperCAmelCase		:							Any							=     {
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }

       return inputs


     def        __snake_case(  self    ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != """cuda"""							,					reason="""float16 requires CUDA"""    )
     def        __snake_case(  self    ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1e-1    )


     def        __snake_case(  self    ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )


     def        __snake_case(  self    ):
       self._test_save_load_local()


     def        __snake_case(  self    ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2							,					)




     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()							,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""							,					)
     def        __snake_case(  self    ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


@slow
@require_torch_gpu
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):


     def        __snake_case(  self    ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


     def        __snake_case(  self    ):
       # if

       _UpperCAmelCase		:							List[str]							=     IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""							,					variant="""fp16"""							,					torch_dtype=torch.floataa    )

       _UpperCAmelCase		:							Dict							=     IFSuperResolutionPipeline.from_pretrained(
           """DeepFloyd/IF-II-L-v1.0"""							,					variant="""fp16"""							,					torch_dtype=torch.floataa							,					text_encoder=A_							,					tokenizer=A_    )

       # pre compute text embeddings and remove T5 to save memory

       pipe_a.text_encoder.to("""cuda"""    )

       _UpperCAmelCase,_UpperCAmelCase		:							Dict							=     pipe_a.encode_prompt("""anime turtle"""							,					device="""cuda"""    )

       del pipe_a.tokenizer
       del pipe_a.text_encoder
       gc.collect()

       _UpperCAmelCase		:							List[Any]							=     None
       _UpperCAmelCase		:							Any							=     None

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

       self._test_if(A_							,					A_							,					A_							,					A_    )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       _UpperCAmelCase		:							Any							=     IFImgaImgPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, Any]							=     IFImgaImgSuperResolutionPipeline(**pipe_a.components    )

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

       self._test_if_imgaimg(A_							,					A_							,					A_							,					A_    )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       _UpperCAmelCase		:							Optional[int]							=     IFInpaintingPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, Any]							=     IFInpaintingSuperResolutionPipeline(**pipe_a.components    )

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

       self._test_if_inpainting(A_							,					A_							,					A_							,					A_    )


     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )
       _UpperCAmelCase		:							List[str]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

       assert image.shape == (64, 64, 3)

       _UpperCAmelCase		:							Dict							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 13 * 10**9

       _UpperCAmelCase		:							str							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )

       # pipeline 2

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							List[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							List[Any]							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )


     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							Dict							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							List[str]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

       assert image.shape == (64, 64, 3)

       _UpperCAmelCase		:							str							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       _UpperCAmelCase		:							str							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )

       # pipeline 2

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							List[str]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							Union[str, Any]							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							Optional[int]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					original_image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Dict							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							int							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )




     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							List[Any]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Dict							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(1    )    ).to(A_    )

       _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )
       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					mask_image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Optional[Any]							=     output.images[0]

       assert image.shape == (64, 64, 3)

       _UpperCAmelCase		:							List[str]							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       _UpperCAmelCase		:							Dict							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )

       # pipeline 2

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							str							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Tuple							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(1    )    ).to(A_    )

       _UpperCAmelCase		:							Union[str, Any]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					mask_image=A_							,					original_image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Any							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Optional[int]							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							Tuple							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )




def    a__    (		):
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()





