



'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

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


enable_full_determinism()






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




  __lowercase							:      Dict				=      ConsistencyModelPipeline
  __lowercase							:      Any				=      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  __lowercase							:      List[str]				=      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

  # Override required_optional_params to remove num_images_per_prompt
  __lowercase							:      List[Any]				=      frozenset(
      [
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''output_type''',
          '''return_dict''',
          '''callback''',
          '''callback_steps''',
      ]     )




  @property
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      lowerCAmelCase__ :  str							      =					UNetaDModel.from_pretrained(
          """diffusers/consistency-models-test"""  ,subfolder="""test_unet"""  ,)
      return unet




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Optional[Any]							      =					UNetaDModel.from_pretrained(
          """diffusers/consistency-models-test"""  ,subfolder="""test_unet_class_cond"""  ,)
      return unet




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=False      )    ->		int:
      if class_cond:
          lowerCAmelCase__ :  Optional[Any]							      =					self.dummy_cond_unet
      else:
          lowerCAmelCase__ :  List[Any]							      =					self.dummy_uncond_unet

      # Default to CM multistep sampler
      lowerCAmelCase__ :  str							      =					CMStochasticIterativeScheduler(
          num_train_timesteps=40  ,sigma_min=0.0_0_2  ,sigma_max=8_0.0  ,)

      lowerCAmelCase__ :  Dict							      =					{
          """unet""": unet,
          """scheduler""": scheduler,
      }

      return components




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=0      )    ->		Optional[Any]:
      if str(__UpperCAmelCase      ).startswith("""mps"""      ):
          lowerCAmelCase__ :  str							      =					torch.manual_seed(__UpperCAmelCase      )
      else:
          lowerCAmelCase__ :  Union[str, Any]							      =					torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[int]							      =					{
          """batch_size""": 1,
          """num_inference_steps""": None,
          """timesteps""": [22, 0],
          """generator""": generator,
          """output_type""": """np""",
      }

      return inputs




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      lowerCAmelCase__ :  List[Any]							      =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase__ :  Tuple							      =					self.get_dummy_components()
      lowerCAmelCase__ :  Tuple							      =					ConsistencyModelPipeline(**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					pipe.to(__UpperCAmelCase      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Tuple							      =					self.get_dummy_inputs(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 32, 32, 3)

      lowerCAmelCase__ :  Tuple							      =					image[0, -3:, -3:, -1]
      lowerCAmelCase__ :  List[Any]							      =					np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  int							      =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase__ :  str							      =					self.get_dummy_components(class_cond=__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					ConsistencyModelPipeline(**__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					pipe.to(__UpperCAmelCase      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  int							      =					self.get_dummy_inputs(__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					0
      lowerCAmelCase__ :  Optional[Any]							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 32, 32, 3)

      lowerCAmelCase__ :  List[Any]							      =					image[0, -3:, -3:, -1]
      lowerCAmelCase__ :  int							      =					np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      lowerCAmelCase__ :  str							      =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase__ :  Any							      =					self.get_dummy_components()
      lowerCAmelCase__ :  Dict							      =					ConsistencyModelPipeline(**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					pipe.to(__UpperCAmelCase      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[int]							      =					self.get_dummy_inputs(__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					1
      lowerCAmelCase__ :  Optional[int]							      =					None
      lowerCAmelCase__ :  Union[str, Any]							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 32, 32, 3)

      lowerCAmelCase__ :  Optional[int]							      =					image[0, -3:, -3:, -1]
      lowerCAmelCase__ :  Optional[Any]							      =					np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3






  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  int							      =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase__ :  Any							      =					self.get_dummy_components(class_cond=__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[Any]							      =					ConsistencyModelPipeline(**__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					pipe.to(__UpperCAmelCase      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					self.get_dummy_inputs(__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					1
      lowerCAmelCase__ :  List[str]							      =					None
      lowerCAmelCase__ :  Optional[Any]							      =					0
      lowerCAmelCase__ :  Optional[Any]							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 32, 32, 3)

      lowerCAmelCase__ :  Union[str, Any]							      =					image[0, -3:, -3:, -1]
      lowerCAmelCase__ :  Union[str, Any]							      =					np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







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




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=0  ,__UpperCAmelCase=False  ,__UpperCAmelCase="cpu"  ,__UpperCAmelCase=torch.floataa  ,__UpperCAmelCase=(1, 3, 64, 64)      )    ->		Optional[int]:
      lowerCAmelCase__ :  Dict							      =					torch.manual_seed(__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					{
          """num_inference_steps""": None,
          """timesteps""": [22, 0],
          """class_labels""": 0,
          """generator""": generator,
          """output_type""": """np""",
      }

      if get_fixed_latents:
          lowerCAmelCase__ :  Optional[Any]							      =					self.get_fixed_latents(seed=__UpperCAmelCase  ,device=__UpperCAmelCase  ,dtype=__UpperCAmelCase  ,shape=__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					latents

      return inputs




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=0  ,__UpperCAmelCase="cpu"  ,__UpperCAmelCase=torch.floataa  ,__UpperCAmelCase=(1, 3, 64, 64)      )    ->		Any:
      if type(__UpperCAmelCase      ) == str:
          lowerCAmelCase__ :  List[str]							      =					torch.device(__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					randn_tensor(__UpperCAmelCase  ,generator=__UpperCAmelCase  ,device=__UpperCAmelCase  ,dtype=__UpperCAmelCase      )
      return latents




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  Optional[Any]							      =					UNetaDModel.from_pretrained("""diffusers/consistency_models"""  ,subfolder="""diffusers_cd_imagenet64_l2"""      )
      lowerCAmelCase__ :  Tuple							      =					CMStochasticIterativeScheduler(
          num_train_timesteps=40  ,sigma_min=0.0_0_2  ,sigma_max=8_0.0  ,)
      lowerCAmelCase__ :  str							      =					ConsistencyModelPipeline(unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase      )
      pipe.to(torch_device=__UpperCAmelCase      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					self.get_inputs()
      lowerCAmelCase__ :  Tuple							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 64, 64, 3)

      lowerCAmelCase__ :  List[str]							      =					image[0, -3:, -3:, -1]

      lowerCAmelCase__ :  int							      =					np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4]      )

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




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  List[str]							      =					UNetaDModel.from_pretrained("""diffusers/consistency_models"""  ,subfolder="""diffusers_cd_imagenet64_l2"""      )
      lowerCAmelCase__ :  str							      =					CMStochasticIterativeScheduler(
          num_train_timesteps=40  ,sigma_min=0.0_0_2  ,sigma_max=8_0.0  ,)
      lowerCAmelCase__ :  List[Any]							      =					ConsistencyModelPipeline(unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase      )
      pipe.to(torch_device=__UpperCAmelCase      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[int]							      =					self.get_inputs()
      lowerCAmelCase__ :  Any							      =					1
      lowerCAmelCase__ :  Dict							      =					None
      lowerCAmelCase__ :  Dict							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 64, 64, 3)

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

      lowerCAmelCase__ :  Optional[Any]							      =					np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7]      )

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




  @require_torch_a
  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      lowerCAmelCase__ :  Any							      =					UNetaDModel.from_pretrained("""diffusers/consistency_models"""  ,subfolder="""diffusers_cd_imagenet64_l2"""      )
      lowerCAmelCase__ :  Tuple							      =					CMStochasticIterativeScheduler(
          num_train_timesteps=40  ,sigma_min=0.0_0_2  ,sigma_max=8_0.0  ,)
      lowerCAmelCase__ :  Union[str, Any]							      =					ConsistencyModelPipeline(unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase      )
      pipe.to(torch_device=__UpperCAmelCase  ,torch_dtype=torch.floataa      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  int							      =					self.get_inputs(get_fixed_latents=__UpperCAmelCase  ,device=__UpperCAmelCase      )
      # Ensure usage of flash attention in torch 2.0
      with sdp_kernel(enable_flash=__UpperCAmelCase  ,enable_math=__UpperCAmelCase  ,enable_mem_efficient=__UpperCAmelCase      ):
          lowerCAmelCase__ :  Optional[Any]							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 64, 64, 3)

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

      lowerCAmelCase__ :  Optional[int]							      =					np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3






  @require_torch_a
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  Optional[int]							      =					UNetaDModel.from_pretrained("""diffusers/consistency_models"""  ,subfolder="""diffusers_cd_imagenet64_l2"""      )
      lowerCAmelCase__ :  List[Any]							      =					CMStochasticIterativeScheduler(
          num_train_timesteps=40  ,sigma_min=0.0_0_2  ,sigma_max=8_0.0  ,)
      lowerCAmelCase__ :  Optional[int]							      =					ConsistencyModelPipeline(unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase      )
      pipe.to(torch_device=__UpperCAmelCase  ,torch_dtype=torch.floataa      )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					self.get_inputs(get_fixed_latents=__UpperCAmelCase  ,device=__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					1
      lowerCAmelCase__ :  List[Any]							      =					None
      # Ensure usage of flash attention in torch 2.0
      with sdp_kernel(enable_flash=__UpperCAmelCase  ,enable_math=__UpperCAmelCase  ,enable_mem_efficient=__UpperCAmelCase      ):
          lowerCAmelCase__ :  Any							      =					pipe(**__UpperCAmelCase      ).images
      assert image.shape == (1, 64, 64, 3)

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

      lowerCAmelCase__ :  Dict							      =					np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=1			):



    """simple docstring"""





    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
    else:
        return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  Optional[Any]							      =					old_item.replace("""in_layers.0"""							,    """norm1"""			)
        lowerCAmelCase__ :  Optional[int]							      =					new_item.replace("""in_layers.2"""							,    """conv1"""			)

        lowerCAmelCase__ :  Dict							      =					new_item.replace("""out_layers.0"""							,    """norm2"""			)
        lowerCAmelCase__ :  str							      =					new_item.replace("""out_layers.3"""							,    """conv2"""			)

        lowerCAmelCase__ :  str							      =					new_item.replace("""emb_layers.1"""							,    """time_emb_proj"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""skip_connection"""							,    """conv_shortcut"""			)

        lowerCAmelCase__ :  Union[str, Any]							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

        mapping.append({"""old""": old_item, """new""": new_item}			)

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  List[str]							      =					old_item

        lowerCAmelCase__ :  int							      =					new_item.replace("""norm.weight"""							,    """group_norm.weight"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""norm.bias"""							,    """group_norm.bias"""			)

        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""proj_out.weight"""							,    """proj_attn.weight"""			)
        lowerCAmelCase__ :  int							      =					new_item.replace("""proj_out.bias"""							,    """proj_attn.bias"""			)

        lowerCAmelCase__ :  str							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

        mapping.append({"""old""": old_item, """new""": new_item}			)

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=None			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            lowerCAmelCase__ :  Any							      =					old_checkpoint[path]
            lowerCAmelCase__ :  int							      =					old_tensor.shape[0] // 3

            lowerCAmelCase__ :  int							      =					(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

            lowerCAmelCase__ :  Tuple							      =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

            lowerCAmelCase__ :  List[Any]							      =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
            lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					old_tensor.split(channels // num_heads							,    dim=1			)

            lowerCAmelCase__ :  int							      =					query.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Union[str, Any]							      =					key.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Optional[int]							      =					value.reshape(UpperCamelCase			)

    for path in paths:
        lowerCAmelCase__ :  Any							      =					path["""new"""]

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.0"""							,    """mid_block.resnets.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.1"""							,    """mid_block.attentions.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.2"""							,    """mid_block.resnets.1"""			)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                lowerCAmelCase__ :  Any							      =					new_path.replace(replacement["""old"""]							,    replacement["""new"""]			)

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            lowerCAmelCase__ :  List[Any]							      =					old_checkpoint[path["""old"""]][:, :, 0]
        else:
            lowerCAmelCase__ :  Dict							      =					old_checkpoint[path["""old"""]]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					{}

    lowerCAmelCase__ :  str							      =					checkpoint["""time_embed.0.weight"""]
    lowerCAmelCase__ :  List[Any]							      =					checkpoint["""time_embed.0.bias"""]
    lowerCAmelCase__ :  int							      =					checkpoint["""time_embed.2.weight"""]
    lowerCAmelCase__ :  List[str]							      =					checkpoint["""time_embed.2.bias"""]

    lowerCAmelCase__ :  str							      =					checkpoint["""input_blocks.0.0.weight"""]
    lowerCAmelCase__ :  Any							      =					checkpoint["""input_blocks.0.0.bias"""]

    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.weight"""]
    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.bias"""]
    lowerCAmelCase__ :  str							      =					checkpoint["""out.2.weight"""]
    lowerCAmelCase__ :  Tuple							      =					checkpoint["""out.2.bias"""]

    # Retrieves the keys for the input blocks only
    lowerCAmelCase__ :  Optional[Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """input_blocks""" in layer}			)
    lowerCAmelCase__ :  Optional[Any]							      =					{
        layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    # Retrieves the keys for the middle blocks only
    lowerCAmelCase__ :  Union[str, Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """middle_block""" in layer}			)
    lowerCAmelCase__ :  Union[str, Any]							      =					{
        layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    # Retrieves the keys for the output blocks only
    lowerCAmelCase__ :  List[str]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """output_blocks""" in layer}			)
    lowerCAmelCase__ :  List[Any]							      =					{
        layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    for i in range(1							,    UpperCamelCase			):
        lowerCAmelCase__ :  Dict							      =					(i - 1) // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  Tuple							      =					(i - 1) % (config["""num_res_blocks"""] + 1)

        lowerCAmelCase__ :  Optional[int]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        lowerCAmelCase__ :  Optional[Any]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

        if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
            lowerCAmelCase__ :  Optional[int]							      =					checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            lowerCAmelCase__ :  Tuple							      =					checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

        lowerCAmelCase__ :  Optional[Any]							      =					renew_resnet_paths(UpperCamelCase			)
        lowerCAmelCase__ :  Dict							      =					{"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        lowerCAmelCase__ :  Optional[Any]							      =					{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
        assign_to_checkpoint(
            UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path, resnet_op]							,    config=UpperCamelCase			)

        if len(UpperCamelCase			):
            lowerCAmelCase__ :  Optional[Any]							      =					renew_attention_paths(UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					{
                """old""": f"""input_blocks.{i}.1""",
                """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            lowerCAmelCase__ :  List[str]							      =					{
                f"""input_blocks.{i}.1.qkv.bias""": {
                    """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                    """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                    """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                },
                f"""input_blocks.{i}.1.qkv.weight""": {
                    """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                    """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                    """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                },
            }
            assign_to_checkpoint(
                UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase							,    )

    lowerCAmelCase__ :  Dict							      =					middle_blocks[0]
    lowerCAmelCase__ :  Union[str, Any]							      =					middle_blocks[1]
    lowerCAmelCase__ :  Dict							      =					middle_blocks[2]

    lowerCAmelCase__ :  Any							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					renew_attention_paths(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					{
        """middle_block.1.qkv.bias""": {
            """key""": """mid_block.attentions.0.key.bias""",
            """query""": """mid_block.attentions.0.query.bias""",
            """value""": """mid_block.attentions.0.value.bias""",
        },
        """middle_block.1.qkv.weight""": {
            """key""": """mid_block.attentions.0.key.weight""",
            """query""": """mid_block.attentions.0.query.weight""",
            """value""": """mid_block.attentions.0.value.weight""",
        },
    }
    assign_to_checkpoint(
        UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase			)

    for i in range(UpperCamelCase			):
        lowerCAmelCase__ :  Tuple							      =					i // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  List[str]							      =					i % (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  int							      =					[shave_segments(UpperCamelCase							,    2			) for name in output_blocks[i]]
        lowerCAmelCase__ :  Union[str, Any]							      =					{}

        for layer in output_block_layers:
            lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					layer.split("""."""			)[0], shave_segments(UpperCamelCase							,    1			)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(UpperCamelCase			)
            else:
                lowerCAmelCase__ :  str							      =					[layer_name]

        if len(UpperCamelCase			) > 1:
            lowerCAmelCase__ :  str							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            lowerCAmelCase__ :  Dict							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            lowerCAmelCase__ :  Optional[int]							      =					renew_resnet_paths(UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase			)

            lowerCAmelCase__ :  Optional[int]							      =					{"""old""": f"""output_blocks.{i}.0""", """new""": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
            assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    config=UpperCamelCase			)

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                lowerCAmelCase__ :  List[Any]							      =					list(output_block_list.values()			).index(["""conv.weight""", """conv.bias"""]			)
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.weight"""
                ]
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.bias"""
                ]

                # Clear attentions as they have been attributed above.
                if len(UpperCamelCase			) == 2:
                    lowerCAmelCase__ :  Tuple							      =					[]

            if len(UpperCamelCase			):
                lowerCAmelCase__ :  Dict							      =					renew_attention_paths(UpperCamelCase			)
                lowerCAmelCase__ :  Tuple							      =					{
                    """old""": f"""output_blocks.{i}.1""",
                    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                lowerCAmelCase__ :  Tuple							      =					{
                    f"""output_blocks.{i}.1.qkv.bias""": {
                        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                    },
                    f"""output_blocks.{i}.1.qkv.weight""": {
                        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                    },
                }
                assign_to_checkpoint(
                    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions			) else None							,    config=UpperCamelCase							,    )
        else:
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase							,    n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                lowerCAmelCase__ :  Tuple							      =					""".""".join(["""output_blocks""", str(UpperCamelCase			), path["""old"""]]			)
                lowerCAmelCase__ :  List[Any]							      =					""".""".join(["""up_blocks""", str(UpperCamelCase			), """resnets""", str(UpperCamelCase			), path["""new"""]]			)

                lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )

 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 _lowerCAmelCase      						=	parser.parse_args()

 _lowerCAmelCase      						=	torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _lowerCAmelCase      						=	json.loads(f.read())

 _lowerCAmelCase      						=	convert_ldm_checkpoint(checkpoint, config)

 if "ldm" in config:
  del config["ldm"]

 _lowerCAmelCase      						=	UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase      						=	DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  _lowerCAmelCase      						=	VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  _lowerCAmelCase      						=	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)


