



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a__   :					str 						=      {
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 10_00,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

a__   :					Dict 						=      {
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 10_00,
    "block_out_channels": [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

a__   :					str 						=      {
    "sample_size": 2_56,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

a__   :					Tuple 						=      {
    "num_train_timesteps": 40,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

a__   :					List[Any] 						=      {
    "num_train_timesteps": 2_01,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

a__   :					Dict 						=      {
    "num_train_timesteps": 1_51,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

def 	_lowerCAmelCase (    A__						):
      if isinstance(A__    ,	A__						):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError('boolean value expected'						)

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__=False						):
      lowercase__  =	checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      lowercase__  =	checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      lowercase__  =	checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      lowercase__  =	checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      lowercase__  =	checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      lowercase__  =	checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      lowercase__  =	checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      lowercase__  =	checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      lowercase__  =	checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      lowercase__  =	checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            lowercase__  =	checkpoint[F'''{old_prefix}.skip_connection.weight''']
            lowercase__  =	checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__=None						):
      lowercase__,						lowercase__,						lowercase__  =	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3    ,	dim=0						)
      lowercase__,						lowercase__,						lowercase__  =	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3    ,	dim=0						)

      lowercase__  =	checkpoint[F'''{old_prefix}.norm.weight''']
      lowercase__  =	checkpoint[F'''{old_prefix}.norm.bias''']

      lowercase__  =	weight_q.squeeze(-1						).squeeze(-1						)
      lowercase__  =	bias_q.squeeze(-1						).squeeze(-1						)
      lowercase__  =	weight_k.squeeze(-1						).squeeze(-1						)
      lowercase__  =	bias_k.squeeze(-1						).squeeze(-1						)
      lowercase__  =	weight_v.squeeze(-1						).squeeze(-1						)
      lowercase__  =	bias_v.squeeze(-1						).squeeze(-1						)

      lowercase__  =	(
          checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1						).squeeze(-1						)
      )
      lowercase__  =	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1						).squeeze(-1						)

      return new_checkpoint

def 	_lowerCAmelCase (    A__    ,	A__						):
      lowercase__  =	torch.load(A__    ,	map_location='cpu'						)
      lowercase__  =	{}

      lowercase__  =	checkpoint['time_embed.0.weight']
      lowercase__  =	checkpoint['time_embed.0.bias']
      lowercase__  =	checkpoint['time_embed.2.weight']
      lowercase__  =	checkpoint['time_embed.2.bias']

      if unet_config["num_class_embeds"] is not None:
            lowercase__  =	checkpoint['label_emb.weight']

      lowercase__  =	checkpoint['input_blocks.0.0.weight']
      lowercase__  =	checkpoint['input_blocks.0.0.bias']

      lowercase__  =	unet_config['down_block_types']
      lowercase__  =	unet_config['layers_per_block']
      lowercase__  =	unet_config['attention_head_dim']
      lowercase__  =	unet_config['block_out_channels']
      lowercase__  =	1
      lowercase__  =	channels_list[0]

      for i, layer_type in enumerate(A__						):
            lowercase__  =	channels_list[i]
            lowercase__  =	current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(A__						):
                        lowercase__  =	F'''down_blocks.{i}.resnets.{j}'''
                        lowercase__  =	F'''input_blocks.{current_layer}.0'''
                        lowercase__  =	True if j == 0 and downsample_block_has_skip else False
                        lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__    ,	has_skip=A__						)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(A__						):
                        lowercase__  =	F'''down_blocks.{i}.resnets.{j}'''
                        lowercase__  =	F'''input_blocks.{current_layer}.0'''
                        lowercase__  =	True if j == 0 and downsample_block_has_skip else False
                        lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__    ,	has_skip=A__						)
                        lowercase__  =	F'''down_blocks.{i}.attentions.{j}'''
                        lowercase__  =	F'''input_blocks.{current_layer}.1'''
                        lowercase__  =	convert_attention(
                            A__    ,	A__    ,	A__    ,	A__    ,	A__						)
                        current_layer += 1

            if i != len(A__						) - 1:
                  lowercase__  =	F'''down_blocks.{i}.downsamplers.0'''
                  lowercase__  =	F'''input_blocks.{current_layer}.0'''
                  lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__						)
                  current_layer += 1

            lowercase__  =	current_channels

      # hardcoded the mid-block for now
      lowercase__  =	'mid_block.resnets.0'
      lowercase__  =	'middle_block.0'
      lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__						)
      lowercase__  =	'mid_block.attentions.0'
      lowercase__  =	'middle_block.1'
      lowercase__  =	convert_attention(A__    ,	A__    ,	A__    ,	A__    ,	A__						)
      lowercase__  =	'mid_block.resnets.1'
      lowercase__  =	'middle_block.2'
      lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__						)

      lowercase__  =	0
      lowercase__  =	unet_config['up_block_types']

      for i, layer_type in enumerate(A__						):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1						):
                        lowercase__  =	F'''up_blocks.{i}.resnets.{j}'''
                        lowercase__  =	F'''output_blocks.{current_layer}.0'''
                        lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__    ,	has_skip=A__						)
                        current_layer += 1

                  if i != len(A__						) - 1:
                        lowercase__  =	F'''up_blocks.{i}.upsamplers.0'''
                        lowercase__  =	F'''output_blocks.{current_layer-1}.1'''
                        lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__						)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1						):
                        lowercase__  =	F'''up_blocks.{i}.resnets.{j}'''
                        lowercase__  =	F'''output_blocks.{current_layer}.0'''
                        lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__    ,	has_skip=A__						)
                        lowercase__  =	F'''up_blocks.{i}.attentions.{j}'''
                        lowercase__  =	F'''output_blocks.{current_layer}.1'''
                        lowercase__  =	convert_attention(
                            A__    ,	A__    ,	A__    ,	A__    ,	A__						)
                        current_layer += 1

                  if i != len(A__						) - 1:
                        lowercase__  =	F'''up_blocks.{i}.upsamplers.0'''
                        lowercase__  =	F'''output_blocks.{current_layer-1}.2'''
                        lowercase__  =	convert_resnet(A__    ,	A__    ,	A__    ,	A__						)

      lowercase__  =	checkpoint['out.0.weight']
      lowercase__  =	checkpoint['out.0.bias']
      lowercase__  =	checkpoint['out.2.weight']
      lowercase__  =	checkpoint['out.2.bias']

      return new_checkpoint


if __name__ == "__main__":
       a__   :					Optional[Any] 						=      argparse.ArgumentParser()

       parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
       parser.add_argument(
           "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
       )
       parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

       a__   :					Tuple 						=      parser.parse_args()
       a__   :					Tuple 						=      strabool(args.class_cond)

       a__   :					List[str] 						=      os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              a__   :					Union[str, Any] 						=      IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a__   :					Dict 						=      LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              a__   :					Dict 						=      TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              a__   :					Union[str, Any] 						=      None

       a__   :					str 						=      con_pt_to_diffuser(args.unet_path, unet_config)

       a__   :					List[str] 						=      UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              a__   :					str 						=      CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              a__   :					Dict 						=      CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a__   :					Tuple 						=      CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       a__   :					Tuple 						=      CMStochasticIterativeScheduler(**scheduler_config)

       a__   :					List[str] 						=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	str												=     IFImgaImgSuperResolutionPipeline
   A     :	Dict												=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A     :	Any												=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}						)
   A     :	str												=     PipelineTesterMixin.required_optional_params - {"latents"}


   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         return self._get_superresolution_dummy_components()


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]=0)						->					str:




         """simple docstring"""

         if str(lowerCAmelCase).startswith('mps'):
               lowercase__  =	torch.manual_seed(lowerCAmelCase)
         else:
               lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)

         lowercase__  =	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
         lowercase__  =	floats_tensor((1, 3, 16, 16)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)

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

         return inputs


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()      ,					reason='XFormers attention is only available with CUDA and `xformers` installed'      ,					)
   def       UpperCAmelCase		(    self	:      str)						->					Dict:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


   def       UpperCAmelCase		(    self	:      List[Any])						->					Dict:




         """simple docstring"""

         self._test_save_load_optional_components()


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




         """simple docstring"""

         super().test_save_load_floataa(expected_max_diff=1E-1)


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




         """simple docstring"""

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Tuple:




         """simple docstring"""

         self._test_save_load_local()



   def       UpperCAmelCase		(    self	:      List[str])						->					Tuple:




         """simple docstring"""

         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2      ,					)
