


'''simple docstring'''



from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


_lowercase    :    Optional[int]			     =  logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase    :    List[str]			     =  '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Dict   ,     A     :    Optional[int]=8  ):




						'''simple docstring'''






						UpperCAmelCase         =			h // scale_factor**2
						if h % scale_factor**2 != 0:
												new_h += 1
						UpperCAmelCase         =			w // scale_factor**2
						if w % scale_factor**2 != 0:
												new_w += 1
						return new_h * scale_factor, new_w * scale_factor






class  UpperCamelCase__(     __a ):




				def __init__(	self    :				int   ,					lowerCAmelCase    :				MultilingualCLIP   ,					lowerCAmelCase    :				XLMRobertaTokenizer   ,					lowerCAmelCase    :				UNetaDConditionModel   ,					lowerCAmelCase    :				Union[DDIMScheduler, DDPMScheduler]   ,					lowerCAmelCase    :				VQModel   ,					)->  Dict:

										"""simple docstring"""




										super().__init__()

										self.register_modules(
										    text_encoder=snake_case__   ,					tokenizer=snake_case__   ,					unet=snake_case__   ,					scheduler=snake_case__   ,					movq=snake_case__   ,					)
										UpperCAmelCase         =			2 ** (len(self.movq.config.block_out_channels   ) - 1)




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				List[Any]   )->  List[str]:

										"""simple docstring"""




										if latents is None:
																UpperCAmelCase         =			randn_tensor(snake_case__   ,					generator=snake_case__   ,					device=snake_case__   ,					dtype=snake_case__   )
										else:
																if latents.shape != shape:
																						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""   )
																UpperCAmelCase         =			latents.to(snake_case__   )

										UpperCAmelCase         =			latents * scheduler.init_noise_sigma
										return latents




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Optional[int]=None   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			len(snake_case__   ) if isinstance(snake_case__   ,					snake_case__   ) else 1
										# get prompt text embeddings
										UpperCAmelCase         =			self.tokenizer(
										    snake_case__   ,					padding='''max_length'''   ,					truncation=snake_case__   ,					max_length=77   ,					return_attention_mask=snake_case__   ,					add_special_tokens=snake_case__   ,					return_tensors='''pt'''   ,					)

										UpperCAmelCase         =			text_inputs.input_ids
										UpperCAmelCase         =			self.tokenizer(snake_case__   ,					padding='''longest'''   ,					return_tensors='''pt'''   ).input_ids

										if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case__   ,					snake_case__   ):
																UpperCAmelCase         =			self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
																logger.warning(
																    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""   )

										UpperCAmelCase         =			text_input_ids.to(snake_case__   )
										UpperCAmelCase         =			text_inputs.attention_mask.to(snake_case__   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self.text_encoder(
										    input_ids=snake_case__   ,					attention_mask=snake_case__   )

										UpperCAmelCase         =			prompt_embeds.repeat_interleave(snake_case__   ,					dim=0   )
										UpperCAmelCase         =			text_encoder_hidden_states.repeat_interleave(snake_case__   ,					dim=0   )
										UpperCAmelCase         =			text_mask.repeat_interleave(snake_case__   ,					dim=0   )

										if do_classifier_free_guidance:
																UpperCAmelCase         =			42
																if negative_prompt is None:
																						UpperCAmelCase         =			[''''''] * batch_size
																elif type(snake_case__   ) is not type(snake_case__   ):
																						raise TypeError(
																						    F"""`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__   )} !="""
																						    F""" {type(snake_case__   )}."""   )
																elif isinstance(snake_case__   ,					snake_case__   ):
																						UpperCAmelCase         =			[negative_prompt]
																elif batch_size != len(snake_case__   ):
																						raise ValueError(
																						    F"""`negative_prompt`: {negative_prompt} has batch size {len(snake_case__   )}, but `prompt`:"""
																						    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																						    ''' the batch size of `prompt`.'''   )
																else:
																						UpperCAmelCase         =			negative_prompt

																UpperCAmelCase         =			self.tokenizer(
																    snake_case__   ,					padding='''max_length'''   ,					max_length=77   ,					truncation=snake_case__   ,					return_attention_mask=snake_case__   ,					add_special_tokens=snake_case__   ,					return_tensors='''pt'''   ,					)
																UpperCAmelCase         =			uncond_input.input_ids.to(snake_case__   )
																UpperCAmelCase         =			uncond_input.attention_mask.to(snake_case__   )

																UpperCAmelCase       ,       UpperCAmelCase         =			self.text_encoder(
																    input_ids=snake_case__   ,					attention_mask=snake_case__   )

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																UpperCAmelCase         =			negative_prompt_embeds.shape[1]
																UpperCAmelCase         =			negative_prompt_embeds.repeat(1   ,					snake_case__   )
																UpperCAmelCase         =			negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,					snake_case__   )

																UpperCAmelCase         =			uncond_text_encoder_hidden_states.shape[1]
																UpperCAmelCase         =			uncond_text_encoder_hidden_states.repeat(1   ,					snake_case__   ,					1   )
																UpperCAmelCase         =			uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt   ,					snake_case__   ,					-1   )
																UpperCAmelCase         =			uncond_text_mask.repeat_interleave(snake_case__   ,					dim=0   )

																# done duplicates

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																UpperCAmelCase         =			torch.cat([negative_prompt_embeds, prompt_embeds]   )
																UpperCAmelCase         =			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

																UpperCAmelCase         =			torch.cat([uncond_text_mask, text_mask]   )

										return prompt_embeds, text_encoder_hidden_states, text_mask




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				str=0   )->  Dict:

										"""simple docstring"""




										if is_accelerate_available():
																from accelerate import cpu_offload
										else:
																raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

										UpperCAmelCase         =			torch.device(F"""cuda:{gpu_id}"""   )

										UpperCAmelCase         =			[
										    self.unet,
										    self.text_encoder,
										    self.movq,
										]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(snake_case__   ,					snake_case__   )




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Optional[int]=0   )->  List[Any]:

										"""simple docstring"""




										if is_accelerate_available() and is_accelerate_version('''>='''   ,					'''0.17.0.dev0'''   ):
																from accelerate import cpu_offload_with_hook
										else:
																raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''   )

										UpperCAmelCase         =			torch.device(F"""cuda:{gpu_id}"""   )

										if self.device.type != "cpu":
																self.to('''cpu'''   ,					silence_dtype_warnings=snake_case__   )
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										UpperCAmelCase         =			None
										for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
																UpperCAmelCase       ,       UpperCAmelCase         =			cpu_offload_with_hook(snake_case__   ,					snake_case__   ,					prev_module_hook=snake_case__   )

										if self.safety_checker is not None:
																UpperCAmelCase       ,       UpperCAmelCase         =			cpu_offload_with_hook(self.safety_checker   ,					snake_case__   ,					prev_module_hook=snake_case__   )

										# We'll offload the last model manually.
										UpperCAmelCase         =			hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   a__(	self    :				str   )->  List[str]:

										"""simple docstring"""




										if not hasattr(self.unet   ,					'''_hf_hook'''   ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(snake_case__   ,					'''_hf_hook'''   )
																    and hasattr(module._hf_hook   ,					'''execution_device'''   )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device   )
										return self.device





				@torch.no_grad()
				@replace_example_docstring(snake_case__   )
				def __call__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				Union[str, List[str]]   ,					lowerCAmelCase    :				Union[torch.FloatTensor, List[torch.FloatTensor]]   ,					lowerCAmelCase    :				Union[torch.FloatTensor, List[torch.FloatTensor]]   ,					lowerCAmelCase    :				Optional[Union[str, List[str]]] = None   ,					lowerCAmelCase    :				int = 512   ,					lowerCAmelCase    :				int = 512   ,					lowerCAmelCase    :				int = 100   ,					lowerCAmelCase    :				float = 4.0   ,					lowerCAmelCase    :				int = 1   ,					lowerCAmelCase    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,					lowerCAmelCase    :				Optional[torch.FloatTensor] = None   ,					lowerCAmelCase    :				Optional[str] = "pil"   ,					lowerCAmelCase    :				bool = True   ,					)->  List[Any]:

										"""simple docstring"""




										if isinstance(snake_case__   ,					snake_case__   ):
																UpperCAmelCase         =			1
										elif isinstance(snake_case__   ,					snake_case__   ):
																UpperCAmelCase         =			len(snake_case__   )
										else:
																raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(snake_case__   )}"""   )

										UpperCAmelCase         =			self._execution_device

										UpperCAmelCase         =			batch_size * num_images_per_prompt
										UpperCAmelCase         =			guidance_scale > 1.0

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			self._encode_prompt(
										    snake_case__   ,					snake_case__   ,					snake_case__   ,					snake_case__   ,					snake_case__   )

										if isinstance(snake_case__   ,					snake_case__   ):
																UpperCAmelCase         =			torch.cat(snake_case__   ,					dim=0   )
										if isinstance(snake_case__   ,					snake_case__   ):
																UpperCAmelCase         =			torch.cat(snake_case__   ,					dim=0   )

										if do_classifier_free_guidance:
																UpperCAmelCase         =			image_embeds.repeat_interleave(snake_case__   ,					dim=0   )
																UpperCAmelCase         =			negative_image_embeds.repeat_interleave(snake_case__   ,					dim=0   )

										UpperCAmelCase         =			torch.cat([negative_image_embeds, image_embeds]   ,					dim=0   ).to(
										    dtype=prompt_embeds.dtype   ,					device=snake_case__   )

										self.scheduler.set_timesteps(snake_case__   ,					device=snake_case__   )
										UpperCAmelCase         =			self.scheduler.timesteps

										UpperCAmelCase         =			self.unet.config.in_channels

										UpperCAmelCase       ,       UpperCAmelCase         =			get_new_h_w(snake_case__   ,					snake_case__   ,					self.movq_scale_factor   )

										# create initial latent
										UpperCAmelCase         =			self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)   ,					text_encoder_hidden_states.dtype   ,					snake_case__   ,					snake_case__   ,					snake_case__   ,					self.scheduler   ,					)

										for i, t in enumerate(self.progress_bar(snake_case__   )   ):
																# expand the latents if we are doing classifier free guidance
																UpperCAmelCase         =			torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

																UpperCAmelCase         =			{'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
																UpperCAmelCase         =			self.unet(
																    sample=snake_case__   ,					timestep=snake_case__   ,					encoder_hidden_states=snake_case__   ,					added_cond_kwargs=snake_case__   ,					return_dict=snake_case__   ,					)[0]

																if do_classifier_free_guidance:
																						UpperCAmelCase       ,       UpperCAmelCase         =			noise_pred.split(latents.shape[1]   ,					dim=1   )
																						UpperCAmelCase       ,       UpperCAmelCase         =			noise_pred.chunk(2   )
																						UpperCAmelCase       ,       UpperCAmelCase         =			variance_pred.chunk(2   )
																						UpperCAmelCase         =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						UpperCAmelCase         =			torch.cat([noise_pred, variance_pred_text]   ,					dim=1   )

																if not (
																    hasattr(self.scheduler.config   ,					'''variance_type'''   )
																    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																):
																						UpperCAmelCase       ,       UpperCAmelCase         =			noise_pred.split(latents.shape[1]   ,					dim=1   )

																# compute the previous noisy sample x_t -> x_t-1
																UpperCAmelCase         =			self.scheduler.step(
																    snake_case__   ,					snake_case__   ,					snake_case__   ,					generator=snake_case__   ,					).prev_sample
										# post-processing
										UpperCAmelCase         =			self.movq.decode(snake_case__   ,					force_not_quantize=snake_case__   )['''sample''']

										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""   )

										if output_type in ["np", "pil"]:
																UpperCAmelCase         =			image * 0.5 + 0.5
																UpperCAmelCase         =			image.clamp(0   ,					1   )
																UpperCAmelCase         =			image.cpu().permute(0   ,					2   ,					3   ,					1   ).float().numpy()

										if output_type == "pil":
																UpperCAmelCase         =			self.numpy_to_pil(snake_case__   )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=snake_case__   )









'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Tuple=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    lowerCamelCase__   ( A     :    int   ,     A     :    Optional[Any]=0  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]
						for old_item in old_list:
												UpperCAmelCase         =			old_item.replace('''in_layers.0'''   ,     '''norm1'''  )
												UpperCAmelCase         =			new_item.replace('''in_layers.2'''   ,     '''conv1'''  )

												UpperCAmelCase         =			new_item.replace('''out_layers.0'''   ,     '''norm2'''  )
												UpperCAmelCase         =			new_item.replace('''out_layers.3'''   ,     '''conv2'''  )

												UpperCAmelCase         =			new_item.replace('''emb_layers.1'''   ,     '''time_emb_proj'''  )
												UpperCAmelCase         =			new_item.replace('''skip_connection'''   ,     '''conv_shortcut'''  )

												UpperCAmelCase         =			shave_segments(A   ,     n_shave_prefix_segments=A  )

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

						return mapping


def    lowerCamelCase__   ( A     :    Any   ,     A     :    int=0  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]
						for old_item in old_list:
												UpperCAmelCase         =			old_item

												UpperCAmelCase         =			new_item.replace('''norm.weight'''   ,     '''group_norm.weight'''  )
												UpperCAmelCase         =			new_item.replace('''norm.bias'''   ,     '''group_norm.bias'''  )

												UpperCAmelCase         =			new_item.replace('''proj_out.weight'''   ,     '''proj_attn.weight'''  )
												UpperCAmelCase         =			new_item.replace('''proj_out.bias'''   ,     '''proj_attn.bias'''  )

												UpperCAmelCase         =			shave_segments(A   ,     n_shave_prefix_segments=A  )

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

						return mapping


def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    Union[str, Any]   ,     A     :    int   ,     A     :    Dict=None   ,     A     :    Optional[int]=None   ,     A     :    Optional[Any]=None  ):




						'''simple docstring'''






						assert isinstance(A   ,     A  ), "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():
																		UpperCAmelCase         =			old_checkpoint[path]
																		UpperCAmelCase         =			old_tensor.shape[0] // 3

																		UpperCAmelCase         =			(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

																		UpperCAmelCase         =			old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

																		UpperCAmelCase         =			query.reshape(A  )
																		UpperCAmelCase         =			key.reshape(A  )
																		UpperCAmelCase         =			value.reshape(A  )

						for path in paths:
												UpperCAmelCase         =			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
												UpperCAmelCase         =			new_path.replace('''middle_block.0'''   ,     '''mid_block.resnets.0'''  )
												UpperCAmelCase         =			new_path.replace('''middle_block.1'''   ,     '''mid_block.attentions.0'''  )
												UpperCAmelCase         =			new_path.replace('''middle_block.2'''   ,     '''mid_block.resnets.1'''  )

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								UpperCAmelCase         =			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:
																		UpperCAmelCase         =			old_checkpoint[path['''old''']][:, :, 0]
												else:
																		UpperCAmelCase         =			old_checkpoint[path['''old''']]


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			{}

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

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

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

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

						# Retrieves the keys for the middle blocks only
						UpperCAmelCase         =			len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''middle_block''' in layer}  )
						UpperCAmelCase         =			{
						    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
						    for layer_id in range(A  )
						}

						# Retrieves the keys for the output blocks only
						UpperCAmelCase         =			len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''output_blocks''' in layer}  )
						UpperCAmelCase         =			{
						    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
						    for layer_id in range(A  )
						}

						for i in range(1   ,     A  ):
												UpperCAmelCase         =			(i - 1) // (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			(i - 1) % (config['''num_res_blocks'''] + 1)

												UpperCAmelCase         =			[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
												UpperCAmelCase         =			[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:
																		UpperCAmelCase         =			checkpoint[
																		    f"""input_blocks.{i}.0.op.weight"""
																		]
																		UpperCAmelCase         =			checkpoint[
																		    f"""input_blocks.{i}.0.op.bias"""
																		]
																		continue

												UpperCAmelCase         =			renew_resnet_paths(A  )
												UpperCAmelCase         =			{'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												UpperCAmelCase         =			{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
												assign_to_checkpoint(
												    A   ,     A   ,     A   ,     additional_replacements=[meta_path, resnet_op]   ,     config=A  )

												if len(A  ):
																		UpperCAmelCase         =			renew_attention_paths(A  )
																		UpperCAmelCase         =			{
																		    '''old''': f"""input_blocks.{i}.1""",
																		    '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																		}
																		UpperCAmelCase         =			{
																		    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(
																		    A   ,     A   ,     A   ,     additional_replacements=[meta_path]   ,     attention_paths_to_split=A   ,     config=A   ,     )

						UpperCAmelCase         =			middle_blocks[0]
						UpperCAmelCase         =			middle_blocks[1]
						UpperCAmelCase         =			middle_blocks[2]

						UpperCAmelCase         =			renew_resnet_paths(A  )
						assign_to_checkpoint(A   ,     A   ,     A   ,     config=A  )

						UpperCAmelCase         =			renew_resnet_paths(A  )
						assign_to_checkpoint(A   ,     A   ,     A   ,     config=A  )

						UpperCAmelCase         =			renew_attention_paths(A  )
						UpperCAmelCase         =			{
						    '''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(
						    A   ,     A   ,     A   ,     attention_paths_to_split=A   ,     config=A  )

						for i in range(A  ):
												UpperCAmelCase         =			i // (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			i % (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			[shave_segments(A   ,     2  ) for name in output_blocks[i]]
												UpperCAmelCase         =			{}

												for layer in output_block_layers:
																		UpperCAmelCase       ,       UpperCAmelCase         =			layer.split('''.'''  )[0], shave_segments(A   ,     1  )
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(A  )
																		else:
																								UpperCAmelCase         =			[layer_name]

												if len(A  ) > 1:
																		UpperCAmelCase         =			[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
																		UpperCAmelCase         =			[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

																		UpperCAmelCase         =			renew_resnet_paths(A  )
																		UpperCAmelCase         =			renew_resnet_paths(A  )

																		UpperCAmelCase         =			{'''old''': f"""output_blocks.{i}.0""", '''new''': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																		assign_to_checkpoint(A   ,     A   ,     A   ,     additional_replacements=[meta_path]   ,     config=A  )

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

																								# Clear attentions as they have been attributed above.
																								if len(A  ) == 2:
																														UpperCAmelCase         =			[]

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

																								UpperCAmelCase         =			checkpoint[old_path]

						return new_checkpoint


if __name__ == "__main__":
	_lowercase    :    Union[str, Any]			     =  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.""")

	_lowercase    :    Dict			     =  parser.parse_args()

	_lowercase    :    List[Any]			     =  torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		_lowercase    :    List[str]			     =  json.loads(f.read())

	_lowercase    :    Union[str, Any]			     =  convert_ldm_checkpoint(checkpoint, config)

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

	_lowercase    :    Any			     =  UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		_lowercase    :    Tuple			     =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
		_lowercase    :    Optional[Any]			     =  VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		_lowercase    :    Optional[Any]			     =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)





