

import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)



def A__  (			lowercase:     List[Any],		lowercase:     Optional[int]					)				->		Optional[Any]:
       A						:     List[str]         =checkpoint

       A						:     int         ={}

       A						:     str         =vae_state_dict['encoder.conv_in.weight']
       A						:     Any         =vae_state_dict['encoder.conv_in.bias']
       A						:     str         =vae_state_dict['encoder.conv_out.weight']
       A						:     Optional[int]         =vae_state_dict['encoder.conv_out.bias']
       A						:     str         =vae_state_dict['encoder.norm_out.weight']
       A						:     List[Any]         =vae_state_dict['encoder.norm_out.bias']

       A						:     List[str]         =vae_state_dict['decoder.conv_in.weight']
       A						:     Dict         =vae_state_dict['decoder.conv_in.bias']
       A						:     Optional[int]         =vae_state_dict['decoder.conv_out.weight']
       A						:     Tuple         =vae_state_dict['decoder.conv_out.bias']
       A						:     Any         =vae_state_dict['decoder.norm_out.weight']
       A						:     Optional[Any]         =vae_state_dict['decoder.norm_out.bias']

       A						:     Union[str, Any]         =vae_state_dict['quant_conv.weight']
       A						:     str         =vae_state_dict['quant_conv.bias']
       A						:     Tuple         =vae_state_dict['post_quant_conv.weight']
       A						:     Optional[Any]         =vae_state_dict['post_quant_conv.bias']

       # Retrieves the keys for the encoder down blocks only
       A						:     List[str]         =len({'.'.join(layer.split('.'					)[:3]					) for layer in vae_state_dict if 'encoder.down' in layer}					)
       A						:     List[str]         ={
           layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(lowercase					)
       }

       # Retrieves the keys for the decoder up blocks only
       A						:     List[Any]         =len({'.'.join(layer.split('.'					)[:3]					) for layer in vae_state_dict if 'decoder.up' in layer}					)
       A						:     Any         ={
           layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(lowercase					)
       }

       for i in range(lowercase					):
              A						:     List[str]         =[key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

              if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
                     A						:     Union[str, Any]         =vae_state_dict.pop(
                         F'encoder.down.{i}.downsample.conv.weight'					)
                     A						:     Dict         =vae_state_dict.pop(
                         F'encoder.down.{i}.downsample.conv.bias'					)

              A						:     List[str]         =renew_vae_resnet_paths(lowercase					)
              A						:     Union[str, Any]         ={'old': F'down.{i}.block', 'new': F'down_blocks.{i}.resnets'}
              assign_to_checkpoint(lowercase,		lowercase,		lowercase,		additional_replacements=[meta_path],		config=lowercase					)

       A						:     str         =[key for key in vae_state_dict if 'encoder.mid.block' in key]
       A						:     int         =2
       for i in range(1,		num_mid_res_blocks + 1					):
              A						:     Optional[Any]         =[key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

              A						:     List[Any]         =renew_vae_resnet_paths(lowercase					)
              A						:     List[str]         ={'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
              assign_to_checkpoint(lowercase,		lowercase,		lowercase,		additional_replacements=[meta_path],		config=lowercase					)

       A						:     int         =[key for key in vae_state_dict if 'encoder.mid.attn' in key]
       A						:     List[Any]         =renew_vae_attention_paths(lowercase					)
       A						:     List[str]         ={'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
       assign_to_checkpoint(lowercase,		lowercase,		lowercase,		additional_replacements=[meta_path],		config=lowercase					)
       conv_attn_to_linear(lowercase					)

       for i in range(lowercase					):
              A						:     List[str]         =num_up_blocks - 1 - i
              A						:     Any         =[
                  key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
              ]

              if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
                     A						:     Union[str, Any]         =vae_state_dict[
                         F'decoder.up.{block_id}.upsample.conv.weight'
                     ]
                     A						:     int         =vae_state_dict[
                         F'decoder.up.{block_id}.upsample.conv.bias'
                     ]

              A						:     Tuple         =renew_vae_resnet_paths(lowercase					)
              A						:     str         ={'old': F'up.{block_id}.block', 'new': F'up_blocks.{i}.resnets'}
              assign_to_checkpoint(lowercase,		lowercase,		lowercase,		additional_replacements=[meta_path],		config=lowercase					)

       A						:     int         =[key for key in vae_state_dict if 'decoder.mid.block' in key]
       A						:     List[Any]         =2
       for i in range(1,		num_mid_res_blocks + 1					):
              A						:     Union[str, Any]         =[key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

              A						:     List[str]         =renew_vae_resnet_paths(lowercase					)
              A						:     Any         ={'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
              assign_to_checkpoint(lowercase,		lowercase,		lowercase,		additional_replacements=[meta_path],		config=lowercase					)

       A						:     Dict         =[key for key in vae_state_dict if 'decoder.mid.attn' in key]
       A						:     Union[str, Any]         =renew_vae_attention_paths(lowercase					)
       A						:     Union[str, Any]         ={'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
       assign_to_checkpoint(lowercase,		lowercase,		lowercase,		additional_replacements=[meta_path],		config=lowercase					)
       conv_attn_to_linear(lowercase					)
       return new_checkpoint



def A__  (			lowercase:     str,		lowercase:     str,		)				->		Optional[Any]:
       # Only support V1
       A						:     str         =requests.get(
           ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'					)
       A						:     Union[str, Any]         =io.BytesIO(r.content					)

       A						:     Union[str, Any]         =OmegaConf.load(lowercase					)
       A						:     str         =512
       A						:     Optional[int]         ='cuda' if torch.cuda.is_available() else 'cpu'
       if checkpoint_path.endswith('safetensors'					):
              from safetensors import safe_open

              A						:     int         ={}
              with safe_open(lowercase,		framework='pt',		device='cpu'					) as f:
                     for key in f.keys():
                            A						:     str         =f.get_tensor(lowercase					)
       else:
              A						:     Any         =torch.load(lowercase,		map_location=lowercase					)['state_dict']

       # Convert the VAE model.
       A						:     Optional[Any]         =create_vae_diffusers_config(lowercase,		image_size=lowercase					)
       A						:     Any         =custom_convert_ldm_vae_checkpoint(lowercase,		lowercase					)

       A						:     Optional[Any]         =AutoencoderKL(**lowercase					)
       vae.load_state_dict(lowercase					)
       vae.save_pretrained(lowercase					)


if __name__ == "__main__":
    _lowercase							:      Optional[int]      							=argparse.ArgumentParser()

    parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')

    _lowercase							:      int      							=parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				DDIMPipeline
  lowercase		:     int            =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowercase		:     Optional[Any]            =				PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  lowercase		:     Optional[Any]            =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  lowercase		:     Union[str, Any]            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         torch.manual_seed(0     )
         A						:     str         =UNetaDModel(
             block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,	up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,	)
         A						:     Optional[int]         =DDIMScheduler()
         A						:     Optional[Any]         ={'unet': unet, 'scheduler': scheduler}
         return components







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0     )    ->				Any:
         if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                A						:     List[Any]         =torch.manual_seed(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Union[str, Any]         =torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         ={
             'batch_size': 1,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         A						:     Union[str, Any]         ='cpu'

         A						:     Tuple         =self.get_dummy_components()
         A						:     Union[str, Any]         =self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
         pipe.to(SCREAMING_SNAKE_CASE__     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     str         =self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
         A						:     str         =pipe(**SCREAMING_SNAKE_CASE__     ).images
         A						:     Optional[Any]         =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape      ,	(1, 32, 32, 3)     )
         A						:     Optional[Any]         =np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]     )
         A						:     str         =np.abs(image_slice.flatten() - expected_slice     ).max()
         self.assertLessEqual(SCREAMING_SNAKE_CASE__      ,	1e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         super().test_save_load_local(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         super().test_save_load_optional_components(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Any         ='google/ddpm-cifar10-32'

         A						:     Optional[int]         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =DDIMScheduler()

         A						:     int         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddim.to(SCREAMING_SNAKE_CASE__     )
         ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =torch.manual_seed(0     )
         A						:     Optional[Any]         =ddim(generator=SCREAMING_SNAKE_CASE__      ,	eta=0.0      ,	output_type='numpy'     ).images

         A						:     str         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A						:     Tuple         =np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     Optional[int]         ='google/ddpm-ema-bedroom-256'

         A						:     str         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     str         =DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__     )

         A						:     Tuple         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddpm.to(SCREAMING_SNAKE_CASE__     )
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Any         =torch.manual_seed(0     )
         A						:     Optional[int]         =ddpm(generator=SCREAMING_SNAKE_CASE__      ,	output_type='numpy'     ).images

         A						:     List[Any]         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         A						:     Optional[int]         =np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]     )

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






