






'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def     __lowercase  (_lowercase     )   ->     Any:


     """simple docstring"""







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

     return name
def     __lowercase  (_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""







     for key in orig_state_dict.copy().keys():
          __lowerCamelCase						:       Tuple				   =				orig_state_dict.pop(lowerCAmelCase__     )

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

          else:
               __lowerCamelCase						:       Optional[int]				   =				val

     return orig_state_dict
def     __lowercase  (_lowercase,						_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				ViTMAEConfig()
     if "large" in checkpoint_url:
          __lowerCamelCase						:       Any				   =				1_024
          __lowerCamelCase						:       Any				   =				4_096
          __lowerCamelCase						:       List[str]				   =				24
          __lowerCamelCase						:       List[str]				   =				16
     elif "huge" in checkpoint_url:
          __lowerCamelCase						:       str				   =				14
          __lowerCamelCase						:       List[str]				   =				1_280
          __lowerCamelCase						:       int				   =				5_120
          __lowerCamelCase						:       Optional[int]				   =				32
          __lowerCamelCase						:       List[str]				   =				16

     __lowerCamelCase						:       Optional[int]				   =				ViTMAEForPreTraining(lowerCAmelCase__     )

     __lowerCamelCase						:       List[str]				   =				torch.hub.load_state_dict_from_url(lowerCAmelCase__,						map_location="""cpu"""     )['model']

     __lowerCamelCase						:       Union[str, Any]				   =				ViTMAEImageProcessor(size=config.image_size     )

     __lowerCamelCase						:       int				   =				convert_state_dict(lowerCAmelCase__,						lowerCAmelCase__     )

     model.load_state_dict(lowerCAmelCase__     )
     model.eval()

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

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

     # forward pass
     torch.manual_seed(2     )
     __lowerCamelCase						:       Any				   =				model(**lowerCAmelCase__     )
     __lowerCamelCase						:       Union[str, Any]				   =				outputs.logits

     if "large" in checkpoint_url:
          __lowerCamelCase						:       Any				   =				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:
          __lowerCamelCase						:       Dict				   =				torch.tensor(
              [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]     )
     else:
          __lowerCamelCase						:       int				   =				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],						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__":
   UpperCAmelCase__						:Any          =			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__						:Optional[Any]          =			parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







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




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""					)
            __lowerCamelCase						:       Tuple				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       Any				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       List[Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       Tuple				   =				output.images

            __lowerCamelCase						:       int				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       List[str]				   =				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       int				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       List[Any]				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Union[str, Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       int				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       List[str]				   =				output.images

            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       Optional[int]				   =				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-1




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       List[str]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_dpmpp_2m"""					)

            __lowerCamelCase						:       int				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Tuple				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=7.5	,    num_inference_steps=15	,    output_type="""np"""	,    use_karras_sigmas=A__	,    )

            __lowerCamelCase						:       int				   =				output.images

            __lowerCamelCase						:       Any				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       int				   =				np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2



