


"""simple docstring"""






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      Dict	)							->	int:

	'''simple docstring'''


	snake_case__  :						Any		=      UNetaDModel(**_UpperCAmelCase	)
	snake_case__  :						List[str]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						Union[str, Any]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.bias"].data

	snake_case__  :						str		=      checkpoint["all_modules.2.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.2.bias"].data

	snake_case__  :						Tuple		=      checkpoint["all_modules.3.weight"].data
	snake_case__  :						Any		=      checkpoint["all_modules.3.bias"].data

	snake_case__  :						Any		=      checkpoint[list(checkpoint.keys()	)[-4]].data
	snake_case__  :						Dict		=      checkpoint[list(checkpoint.keys()	)[-3]].data
	snake_case__  :						List[str]		=      checkpoint[list(checkpoint.keys()	)[-2]].data
	snake_case__  :						Union[str, Any]		=      checkpoint[list(checkpoint.keys()	)[-1]].data

	snake_case__  :						Tuple		=      4

	def set_attention_weights(_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      int				,    _UpperCAmelCase    :      Dict	):
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

		snake_case__  :						Dict		=      old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		snake_case__  :						Tuple		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

	def set_resnet_weights(_UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      int	):
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
		snake_case__  :						Optional[int]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
		snake_case__  :						Optional[int]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Dense_0.bias"""].data

		if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
			snake_case__  :						List[str]		=      old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
			snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

	for i, block in enumerate(new_model_architecture.downsample_blocks	):
		snake_case__  :						Optional[int]		=      hasattr(_UpperCAmelCase				,    "attentions"	)
		for j in range(len(block.resnets	)	):
			set_resnet_weights(block.resnets[j]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1
			if has_attentions:
				set_attention_weights(block.attentions[j]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
				module_index += 1

		if hasattr(_UpperCAmelCase				,    "downsamplers"	) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1
			snake_case__  :						Optional[Any]		=      checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
			snake_case__  :						Union[str, Any]		=      checkpoint[F"""all_modules.{module_index}.Conv_0.bias"""].data
			module_index += 1

	set_resnet_weights(new_model_architecture.mid_block.resnets[0]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
	module_index += 1
	set_attention_weights(new_model_architecture.mid_block.attentions[0]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
	module_index += 1
	set_resnet_weights(new_model_architecture.mid_block.resnets[1]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
	module_index += 1

	for i, block in enumerate(new_model_architecture.up_blocks	):
		snake_case__  :						str		=      hasattr(_UpperCAmelCase				,    "attentions"	)
		for j in range(len(block.resnets	)	):
			set_resnet_weights(block.resnets[j]				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1
		if has_attentions:
			set_attention_weights(
			    block.attentions[0]				,    _UpperCAmelCase				,    _UpperCAmelCase	)  # why can there only be a single attention layer for up?
			module_index += 1

		if hasattr(_UpperCAmelCase				,    "resnet_up"	) and block.resnet_up is not None:
			snake_case__  :						Optional[int]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
			snake_case__  :						List[Any]		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			snake_case__  :						str		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
			snake_case__  :						Tuple		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			set_resnet_weights(block.resnet_up				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1

	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Any		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
	module_index += 1
	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Union[str, Any]		=      checkpoint[F"""all_modules.{module_index}.bias"""].data

	return new_model_architecture.state_dict()


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

					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
					    type=str,
					    required=False,
					    help="""Path to the checkpoint to convert.""",
					)

					parser.add_argument(
					    """--config_file""",
					    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
					    type=str,
					    required=False,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument(
					    """--dump_path""",
					    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
					    type=str,
					    required=False,
					    help="""Path to the output model.""",
					)

					lowercase  		=			parser.parse_args()

					lowercase  		=			torch.load(args.checkpoint_path, map_location="""cpu""")

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

					lowercase  		=			convert_ncsnpp_checkpoint(
					    checkpoint,
					    config,
					)

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

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

					try:
										lowercase  		=			ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										lowercase  		=			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)












"""simple docstring"""




import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


UpperCamelCase_	 =logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase_	 ="""
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""




@dataclass
class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       42




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       int,					lowerCAmelCase__						:       PriorTransformer,					lowerCAmelCase__						:       CLIPTextModelWithProjection,					lowerCAmelCase__						:       CLIPTokenizer,					lowerCAmelCase__						:       HeunDiscreteScheduler,					lowerCAmelCase__						:       ShapERenderer,					)      -> Optional[int]:




										'''simple docstring'''



										super().__init__()

										self.register_modules(
										    prior=lowerCAmelCase__,					text_encoder=lowerCAmelCase__,					tokenizer=lowerCAmelCase__,					scheduler=lowerCAmelCase__,					renderer=lowerCAmelCase__,					)






				def 							snake_case      (					self						:       List[Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[str]				)      -> Optional[int]:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase     :   Any      =       randn_tensor(lowerCAmelCase__,					generator=lowerCAmelCase__,					device=lowerCAmelCase__,					dtype=lowerCAmelCase__				)
										else:
																if latents.shape != shape:
																						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""				)
																_UpperCamelCase     :   List[Any]      =       latents.to(lowerCAmelCase__				)

										_UpperCamelCase     :   Union[str, Any]      =       latents * scheduler.init_noise_sigma
										return latents






				def 							snake_case      (					self						:       int,					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     :   List[str]      =       torch.device(f"""cuda:{gpu_id}"""				)

										_UpperCamelCase     :   List[str]      =       [self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Tuple=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     :   str      =       torch.device(f"""cuda:{gpu_id}"""				)

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

										_UpperCamelCase     :   Tuple      =       None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(lowerCAmelCase__,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

										if self.safety_checker is not None:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(self.safety_checker,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

										# We'll offload the last model manually.
										_UpperCamelCase     :   Union[str, Any]      =       hook






				@property
				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										if self.device != torch.device('''meta'''				) or not hasattr(self.text_encoder,					'''_hf_hook'''				):
																return self.device
										for module in self.text_encoder.modules():
																if (
																    hasattr(lowerCAmelCase__,					'''_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






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					)      -> List[str]:




										'''simple docstring'''



										len(lowerCAmelCase__				) if isinstance(lowerCAmelCase__,					lowerCAmelCase__				) else 1

										# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
										_UpperCamelCase     :   Optional[Any]      =       0
										# get prompt text embeddings
										_UpperCamelCase     :   Union[str, Any]      =       self.tokenizer(
										    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowerCAmelCase__,					return_tensors='''pt''',					)
										_UpperCamelCase     :   Union[str, Any]      =       text_inputs.input_ids
										_UpperCamelCase     :   Union[str, Any]      =       self.tokenizer(lowerCAmelCase__,					padding='''longest''',					return_tensors='''pt'''				).input_ids

										if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase__,					lowerCAmelCase__				):
																_UpperCamelCase     :   Tuple      =       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     :   Dict      =       self.text_encoder(text_input_ids.to(lowerCAmelCase__				)				)
										_UpperCamelCase     :   Optional[int]      =       text_encoder_output.text_embeds

										_UpperCamelCase     :   Any      =       prompt_embeds.repeat_interleave(lowerCAmelCase__,					dim=0				)
										# in Shap-E it normalize the prompt_embeds and then later rescale it
										_UpperCamelCase     :   str      =       prompt_embeds / torch.linalg.norm(lowerCAmelCase__,					dim=-1,					keepdim=lowerCAmelCase__				)

										if do_classifier_free_guidance:
																_UpperCamelCase     :   Any      =       torch.zeros_like(lowerCAmelCase__				)

																# 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     :   Optional[Any]      =       torch.cat([negative_prompt_embeds, prompt_embeds]				)

										# Rescale the features to have unit variance
										_UpperCamelCase     :   Union[str, Any]      =       math.sqrt(prompt_embeds.shape[1]				) * prompt_embeds

										return prompt_embeds






				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase__				)
				def __call__(					self						:       Optional[int],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int = 1,					lowerCAmelCase__						:       int = 2_5,					lowerCAmelCase__						:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,					lowerCAmelCase__						:       Optional[torch.FloatTensor] = None,					lowerCAmelCase__						:       float = 4.0,					lowerCAmelCase__						:       int = 6_4,					lowerCAmelCase__						:       Optional[str] = "pil",					lowerCAmelCase__						:       bool = True,					)      -> Any:




										'''simple docstring'''



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

										_UpperCamelCase     :   Dict      =       self._execution_device

										_UpperCamelCase     :   List[Any]      =       batch_size * num_images_per_prompt

										_UpperCamelCase     :   List[str]      =       guidance_scale > 1.0
										_UpperCamelCase     :   str      =       self._encode_prompt(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

										# prior

										self.scheduler.set_timesteps(lowerCAmelCase__,					device=lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       self.scheduler.timesteps

										_UpperCamelCase     :   Optional[Any]      =       self.prior.config.num_embeddings
										_UpperCamelCase     :   Optional[Any]      =       self.prior.config.embedding_dim

										_UpperCamelCase     :   Tuple      =       self.prepare_latents(
										    (batch_size, num_embeddings * embedding_dim),					prompt_embeds.dtype,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					self.scheduler,					)

										# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
										_UpperCamelCase     :   Dict      =       latents.reshape(latents.shape[0],					lowerCAmelCase__,					lowerCAmelCase__				)

										for i, t in enumerate(self.progress_bar(lowerCAmelCase__				)				):
																# expand the latents if we are doing classifier free guidance
																_UpperCamelCase     :   Optional[int]      =       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
																_UpperCamelCase     :   Dict      =       self.scheduler.scale_model_input(lowerCAmelCase__,					lowerCAmelCase__				)

																_UpperCamelCase     :   Tuple      =       self.prior(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					proj_embedding=lowerCAmelCase__,					).predicted_image_embedding

																# remove the variance
																_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       noise_pred.split(
																    scaled_model_input.shape[2],					dim=2				)  # batch_size, num_embeddings, embedding_dim

																if do_classifier_free_guidance is not None:
																						_UpperCamelCase  ,				_UpperCamelCase     :   List[str]      =       noise_pred.chunk(2				)
																						_UpperCamelCase     :   Optional[Any]      =       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																_UpperCamelCase     :   Dict      =       self.scheduler.step(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					sample=lowerCAmelCase__,					).prev_sample

										if output_type == "latent":
																return ShapEPipelineOutput(images=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       []
										for i, latent in enumerate(lowerCAmelCase__				):
																_UpperCamelCase     :   Union[str, Any]      =       self.renderer.decode(
																    latent[None, :],					lowerCAmelCase__,					size=lowerCAmelCase__,					ray_batch_size=4_0_9_6,					n_coarse_samples=6_4,					n_fine_samples=1_2_8,					)
																images.append(lowerCAmelCase__				)

										_UpperCamelCase     :   str      =       torch.stack(lowerCAmelCase__				)

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

										_UpperCamelCase     :   Dict      =       images.cpu().numpy()

										if output_type == "pil":
																_UpperCamelCase     :   Any      =       [self.numpy_to_pil(lowerCAmelCase__				) for image in images]

										# Offload last model to CPU
										if hasattr(self,					'''final_offload_hook'''				) and self.final_offload_hook is not None:
																self.final_offload_hook.offload()

										if not return_dict:
																return (images,)

										return ShapEPipelineOutput(images=lowerCAmelCase__				)



