






'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			str:

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

   return name



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

   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      __a				     =    orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

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

      else:
         __a				     =    val

   return orig_state_dict



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			Tuple:

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

   __a				     =    ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__    )

   __a				     =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__,					map_location='cpu'    )['model']

   __a				     =    ViTMAEImageProcessor(size=config.image_size    )

   __a				     =    convert_state_dict(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )

   model.load_state_dict(SCREAMING_SNAKE_CASE__    )
   model.eval()

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

   __a				     =    Image.open(requests.get(SCREAMING_SNAKE_CASE__,					stream=SCREAMING_SNAKE_CASE__    ).raw    )
   __a				     =    ViTMAEImageProcessor(size=config.image_size    )
   __a				     =    image_processor(images=SCREAMING_SNAKE_CASE__,					return_tensors='pt'    )

   # forward pass
   torch.manual_seed(2    )
   __a				     =    model(**SCREAMING_SNAKE_CASE__    )
   __a				     =    outputs.logits

   if "large" in checkpoint_url:
      __a				     =    torch.tensor(
          [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]    )
   elif "huge" in checkpoint_url:
      __a				     =    torch.tensor(
          [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]    )
   else:
      __a				     =    torch.tensor(
          [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]    )

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

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

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


if __name__ == "__main__":
 __UpperCamelCase      :       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."""
 )

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







'''simple docstring'''



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       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=IFPipeline
      __a  					=TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
      __a  					=TEXT_TO_IMAGE_BATCH_PARAMS
      __a  					=PipelineTesterMixin.required_optional_params - {"latents"}




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         return self._get_dummy_components()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->Tuple:



         '''simple docstring'''
         if str(lowerCamelCase      ).startswith('mps'      ):
            __a				     =    torch.manual_seed(lowerCamelCase      )
         else:
            __a				     =    torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

         __a				     =    {
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         self._test_save_load_optional_components()




      @unittest.skipIf(torch_device != 'cuda' ,   reason='float16 requires CUDA'      )
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # 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  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self._test_save_load_local()




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         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  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )







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




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         # if

         __a				     =    IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa      )

         __a				     =    IFSuperResolutionPipeline.from_pretrained(
             'DeepFloyd/IF-II-L-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa ,   text_encoder=lowerCamelCase ,   tokenizer=lowerCamelCase      )

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('cuda'      )

         __a     ,			__a				     =    pipe_a.encode_prompt('anime turtle' ,   device='cuda'      )

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

         __a				     =    None
         __a				     =    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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         __a				     =    IFImgaImgPipeline(**pipe_a.components      )
         __a				     =    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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         __a				     =    IFInpaintingPipeline(**pipe_a.components      )
         __a				     =    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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

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

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

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

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    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(lowerCamelCase ,   lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

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

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    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(lowerCamelCase ,   lowerCamelCase      )







def    __UpperCAmelCase	(				)  ->			Dict:

   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()