




"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      KandinskyInpaintPipeline
 lowercase__				      =      ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
 lowercase__				      =      [
     'prompt',
     'negative_prompt',
     'image_embeds',
     'negative_image_embeds',
     'image',
     'mask_image',
 ]
 lowercase__				      =      [
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'negative_prompt',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 lowercase__				      =      False


 @property
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return 32


 @property
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   return 32


 @property
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   return self.time_input_dim


 @property
 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return self.time_input_dim * 4


 @property
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return 100


 @property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
   return tokenizer


 @property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   torch.manual_seed(0	)
   _snake_case							:	Optional[int]			=						MCLIPConfig(
       numDims=self.cross_attention_dim,   transformerDimensions=self.text_embedder_hidden_size,   hidden_size=self.text_embedder_hidden_size,   intermediate_size=37,   num_attention_heads=4,   num_hidden_layers=5,   vocab_size=1_005,   )

   _snake_case							:	int			=						MultilingualCLIP(a_	)
   _snake_case							:	Optional[int]			=						text_encoder.eval()

   return text_encoder


 @property
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   torch.manual_seed(0	)

   _snake_case							:	int			=						{
       """in_channels""": 9,
       # Out channels is double in channels because predicts mean and variance
       """out_channels""": 8,
       """addition_embed_type""": """text_image""",
       """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
       """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
       """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
       """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
       """layers_per_block""": 1,
       """encoder_hid_dim""": self.text_embedder_hidden_size,
       """encoder_hid_dim_type""": """text_image_proj""",
       """cross_attention_dim""": self.cross_attention_dim,
       """attention_head_dim""": 4,
       """resnet_time_scale_shift""": """scale_shift""",
       """class_embed_type""": None,
   }

   _snake_case							:	str			=						UNetaDConditionModel(**a_	)
   return model


 @property
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }


 @property
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   torch.manual_seed(0	)
   _snake_case							:	Optional[Any]			=						VQModel(**self.dummy_movq_kwargs	)
   return model


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.dummy_text_encoder
   _snake_case							:	Optional[int]			=						self.dummy_tokenizer
   _snake_case							:	List[Any]			=						self.dummy_unet
   _snake_case							:	Tuple			=						self.dummy_movq

   _snake_case							:	List[Any]			=						DDIMScheduler(
       num_train_timesteps=1_000,   beta_schedule="""linear""",   beta_start=0.00_085,   beta_end=0.012,   clip_sample=a_,   set_alpha_to_one=a_,   steps_offset=1,   prediction_type="""epsilon""",   thresholding=a_,   )

   _snake_case							:	int			=						{
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }

   return components


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	Tuple,   a_:	Any=0	):




   '''simple docstring'''



   _snake_case							:	int			=						floats_tensor((1, self.cross_attention_dim),   rng=random.Random(a_	)	).to(a_	)
   _snake_case							:	Tuple			=						floats_tensor((1, self.cross_attention_dim),   rng=random.Random(seed + 1	)	).to(a_	)
   # create init_image
   _snake_case							:	Any			=						floats_tensor((1, 3, 64, 64),   rng=random.Random(a_	)	).to(a_	)
   _snake_case							:	int			=						image.cpu().permute(0,   2,   3,   1	)[0]
   _snake_case							:	int			=						Image.fromarray(np.uinta(a_	)	).convert("""RGB"""	).resize((256, 256)	)
   # create mask
   _snake_case							:	List[Any]			=						np.ones((64, 64),   dtype=np.floataa	)
   _snake_case							:	Union[str, Any]			=						0

   if str(a_	).startswith("""mps"""	):
     _snake_case							:	Optional[Any]			=						torch.manual_seed(a_	)
   else:
     _snake_case							:	Tuple			=						torch.Generator(device=a_	).manual_seed(a_	)
   _snake_case							:	List[Any]			=						{
       """prompt""": """horse""",
       """image""": init_image,
       """mask_image""": mask,
       """image_embeds""": image_embeds,
       """negative_image_embeds""": negative_image_embeds,
       """generator""": generator,
       """height""": 64,
       """width""": 64,
       """num_inference_steps""": 2,
       """guidance_scale""": 4.0,
       """output_type""": """np""",
   }
   return inputs


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						"""cpu"""

   _snake_case							:	Dict			=						self.get_dummy_components()

   _snake_case							:	Any			=						self.pipeline_class(**a_	)
   _snake_case							:	Any			=						pipe.to(a_	)

   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	Optional[Any]			=						pipe(**self.get_dummy_inputs(a_	)	)
   _snake_case							:	Optional[int]			=						output.images

   _snake_case							:	Any			=						pipe(
       **self.get_dummy_inputs(a_	),   return_dict=a_,   )[0]

   _snake_case							:	Tuple			=						image[0, -3:, -3:, -1]
   _snake_case							:	Union[str, Any]			=						image_from_tuple[0, -3:, -3:, -1]

   print(f"image.shape {image.shape}"	)

   assert image.shape == (1, 64, 64, 3)

   _snake_case							:	Any			=						np.array(
       [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

   assert (
       np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
   ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
   ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







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







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""	)

   _snake_case							:	Union[str, Any]			=						load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
   _snake_case							:	str			=						np.ones((768, 768),   dtype=np.floataa	)
   _snake_case							:	List[str]			=						0

   _snake_case							:	Optional[int]			=						"""a hat"""

   _snake_case							:	Union[str, Any]			=						KandinskyPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-prior""",   torch_dtype=torch.floataa	)
   pipe_prior.to(a_	)

   _snake_case							:	Any			=						KandinskyInpaintPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-inpaint""",   torch_dtype=torch.floataa	)
   _snake_case							:	int			=						pipeline.to(a_	)
   pipeline.set_progress_bar_config(disable=a_	)

   _snake_case							:	Dict			=						torch.Generator(device="""cpu"""	).manual_seed(0	)
   _snake_case	,      _snake_case							:	Optional[Any]			=						pipe_prior(
       a_,   generator=a_,   num_inference_steps=5,   negative_prompt="""""",   ).to_tuple()

   _snake_case							:	int			=						pipeline(
       a_,   image=a_,   mask_image=a_,   image_embeds=a_,   negative_image_embeds=a_,   generator=a_,   num_inference_steps=100,   height=768,   width=768,   output_type="""np""",   )

   _snake_case							:	Union[str, Any]			=						output.images[0]

   assert image.shape == (768, 768, 3)

   assert_mean_pixel_difference(a_,   a_	)








"""simple docstring"""


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Union[str, Any]=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       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      List[Any]=0					):
  """simple docstring"""

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

    _snake_case							:	Tuple			=						new_item.replace("""out_layers.0"""	,     """norm2"""					)
    _snake_case							:	Dict			=						new_item.replace("""out_layers.3"""	,     """conv2"""					)

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

    _snake_case							:	str			=						shave_segments(snake_case__	,     n_shave_prefix_segments=snake_case__					)

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

  return mapping


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      Dict=0					):
  """simple docstring"""

  _snake_case							:	Dict			=						[]
  for old_item in old_list:
    _snake_case							:	Dict			=						old_item

    _snake_case							:	int			=						new_item.replace("""norm.weight"""	,     """group_norm.weight"""					)
    _snake_case							:	str			=						new_item.replace("""norm.bias"""	,     """group_norm.bias"""					)

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

    _snake_case							:	Optional[Any]			=						shave_segments(snake_case__	,     n_shave_prefix_segments=snake_case__					)

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

  return mapping


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Union[str, Any]	,     snake_case__							:      List[str]	,     snake_case__							:      str=None	,     snake_case__							:      str=None	,     snake_case__							:      List[str]=None					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					), "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():
      _snake_case							:	Union[str, Any]			=						old_checkpoint[path]
      _snake_case							:	Optional[int]			=						old_tensor.shape[0] // 3

      _snake_case							:	List[Any]			=						(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

      _snake_case							:	Union[str, Any]			=						old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

      _snake_case							:	Union[str, Any]			=						query.reshape(snake_case__					)
      _snake_case							:	Tuple			=						key.reshape(snake_case__					)
      _snake_case							:	Any			=						value.reshape(snake_case__					)

  for path in paths:
    _snake_case							:	List[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
    _snake_case							:	Union[str, Any]			=						new_path.replace("""middle_block.0"""	,     """mid_block.resnets.0"""					)
    _snake_case							:	str			=						new_path.replace("""middle_block.1"""	,     """mid_block.attentions.0"""					)
    _snake_case							:	Any			=						new_path.replace("""middle_block.2"""	,     """mid_block.resnets.1"""					)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _snake_case							:	int			=						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:
      _snake_case							:	Dict			=						old_checkpoint[path["""old"""]][:, :, 0]
    else:
      _snake_case							:	Optional[Any]			=						old_checkpoint[path["""old"""]]


def       UpperCAmelCase__ (snake_case__							:      Any	,     snake_case__							:      List[str]					):
  """simple docstring"""

  _snake_case							:	int			=						{}

  _snake_case							:	Tuple			=						checkpoint["""time_embed.0.weight"""]
  _snake_case							:	List[str]			=						checkpoint["""time_embed.0.bias"""]
  _snake_case							:	List[str]			=						checkpoint["""time_embed.2.weight"""]
  _snake_case							:	Tuple			=						checkpoint["""time_embed.2.bias"""]

  _snake_case							:	Dict			=						checkpoint["""input_blocks.0.0.weight"""]
  _snake_case							:	List[Any]			=						checkpoint["""input_blocks.0.0.bias"""]

  _snake_case							:	List[Any]			=						checkpoint["""out.0.weight"""]
  _snake_case							:	Any			=						checkpoint["""out.0.bias"""]
  _snake_case							:	Any			=						checkpoint["""out.2.weight"""]
  _snake_case							:	List[str]			=						checkpoint["""out.2.bias"""]

  # Retrieves the keys for the input blocks only
  _snake_case							:	List[str]			=						len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """input_blocks""" in layer}					)
  _snake_case							:	Any			=						{
      layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
      for layer_id in range(snake_case__					)
  }

  # Retrieves the keys for the middle blocks only
  _snake_case							:	Optional[int]			=						len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
  _snake_case							:	Optional[int]			=						{
      layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
      for layer_id in range(snake_case__					)
  }

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

  for i in range(1	,     snake_case__					):
    _snake_case							:	Union[str, Any]			=						(i - 1) // (config["""num_res_blocks"""] + 1)
    _snake_case							:	int			=						(i - 1) % (config["""num_res_blocks"""] + 1)

    _snake_case							:	List[str]			=						[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
    _snake_case							:	str			=						[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:
      _snake_case							:	Union[str, Any]			=						checkpoint[
          F"input_blocks.{i}.0.op.weight"
      ]
      _snake_case							:	Dict			=						checkpoint[
          F"input_blocks.{i}.0.op.bias"
      ]
      continue

    _snake_case							:	Optional[int]			=						renew_resnet_paths(snake_case__					)
    _snake_case							:	int			=						{"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
    _snake_case							:	Tuple			=						{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
    assign_to_checkpoint(
        snake_case__	,     snake_case__	,     snake_case__	,     additional_replacements=[meta_path, resnet_op]	,     config=snake_case__					)

    if len(snake_case__					):
      _snake_case							:	str			=						renew_attention_paths(snake_case__					)
      _snake_case							:	List[str]			=						{
          """old""": F"input_blocks.{i}.1",
          """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
      }
      _snake_case							:	Optional[int]			=						{
          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(
          snake_case__	,     snake_case__	,     snake_case__	,     additional_replacements=[meta_path]	,     attention_paths_to_split=snake_case__	,     config=snake_case__	,     )

  _snake_case							:	int			=						middle_blocks[0]
  _snake_case							:	List[str]			=						middle_blocks[1]
  _snake_case							:	Any			=						middle_blocks[2]

  _snake_case							:	Dict			=						renew_resnet_paths(snake_case__					)
  assign_to_checkpoint(snake_case__	,     snake_case__	,     snake_case__	,     config=snake_case__					)

  _snake_case							:	Any			=						renew_resnet_paths(snake_case__					)
  assign_to_checkpoint(snake_case__	,     snake_case__	,     snake_case__	,     config=snake_case__					)

  _snake_case							:	Dict			=						renew_attention_paths(snake_case__					)
  _snake_case							:	Tuple			=						{
      """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(
      snake_case__	,     snake_case__	,     snake_case__	,     attention_paths_to_split=snake_case__	,     config=snake_case__					)

  for i in range(snake_case__					):
    _snake_case							:	Optional[Any]			=						i // (config["""num_res_blocks"""] + 1)
    _snake_case							:	Dict			=						i % (config["""num_res_blocks"""] + 1)
    _snake_case							:	List[str]			=						[shave_segments(snake_case__	,     2					) for name in output_blocks[i]]
    _snake_case							:	Any			=						{}

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

    if len(snake_case__					) > 1:
      _snake_case							:	Dict			=						[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
      _snake_case							:	List[str]			=						[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

      _snake_case							:	List[Any]			=						renew_resnet_paths(snake_case__					)
      _snake_case							:	int			=						renew_resnet_paths(snake_case__					)

      _snake_case							:	Optional[Any]			=						{"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
      assign_to_checkpoint(snake_case__	,     snake_case__	,     snake_case__	,     additional_replacements=[meta_path]	,     config=snake_case__					)

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        _snake_case							:	str			=						list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
        _snake_case							:	Any			=						checkpoint[
            F"output_blocks.{i}.{index}.conv.weight"
        ]
        _snake_case							:	Optional[int]			=						checkpoint[
            F"output_blocks.{i}.{index}.conv.bias"
        ]

        # Clear attentions as they have been attributed above.
        if len(snake_case__					) == 2:
          _snake_case							:	Any			=						[]

      if len(snake_case__					):
        _snake_case							:	str			=						renew_attention_paths(snake_case__					)
        _snake_case							:	str			=						{
            """old""": F"output_blocks.{i}.1",
            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
        }
        _snake_case							:	int			=						{
            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(
            snake_case__	,     snake_case__	,     snake_case__	,     additional_replacements=[meta_path]	,     attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,     config=snake_case__	,     )
    else:
      _snake_case							:	Optional[Any]			=						renew_resnet_paths(snake_case__	,     n_shave_prefix_segments=1					)
      for path in resnet_0_paths:
        _snake_case							:	Optional[Any]			=						""".""".join(["""output_blocks""", str(snake_case__					), path["""old"""]]					)
        _snake_case							:	Optional[int]			=						""".""".join(["""up_blocks""", str(snake_case__					), """resnets""", str(snake_case__					), path["""new"""]]					)

        _snake_case							:	Any			=						checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     A_      			=       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.''')

     A_      			=       parser.parse_args()

     A_      			=       torch.load(args.checkpoint_path)

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

     A_      			=       convert_ldm_checkpoint(checkpoint, config)

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

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

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

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


