






'''simple docstring'''





from typing import List, Optional, Union

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

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


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

A        =			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n'



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		List[Any]=8      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				SCREAMING_SNAKE_CASE_   :	Any													=   width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor



def 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		Optional[Any]=512    ,    lowerCAmelCase__				:		str=512      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   pil_image.resize((w, h)    ,    resample=Image.BICUBIC    ,    reducing_gap=1      )
				SCREAMING_SNAKE_CASE_   :	Any													=   np.array(pil_image.convert('RGB'      )      )
				SCREAMING_SNAKE_CASE_   :	Any													=   arr.astype(np.floataa      ) / 127.5 - 1
				SCREAMING_SNAKE_CASE_   :	Any													=   np.transpose(_lowerCamelCase    ,    [2, 0, 1]      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   torch.from_numpy(_lowerCamelCase      ).unsqueeze(0      )
				return image


class 							__a							(  __snake_case						):





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    ):
						super().__init__()

						self.register_modules(
						    unet=_lowercase ,    scheduler=_lowercase ,    movq=_lowercase ,    )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   2 ** (len(self.movq.config.block_out_channels       ) - 1)


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   min(int(num_inference_steps * strength       ) ,    _lowercase       )

						SCREAMING_SNAKE_CASE_   :	str													=   max(num_inference_steps - init_timestep ,    0       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):

						if not isinstance(_lowercase ,    (torch.Tensor, PIL.Image.Image, list)       ):
										raise ValueError(
										    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowercase       )}'''       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   image.to(device=_lowercase ,    dtype=_lowercase       )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   batch_size * num_images_per_prompt

						if image.shape[1] == 4:
										SCREAMING_SNAKE_CASE_   :	int													=   image

						else:
										if isinstance(_lowercase ,    _lowercase       ) and len(_lowercase       ) != batch_size:
														raise ValueError(
														    F'''You have passed a list of generators of length {len(_lowercase       )}, but requested an effective batch'''
														    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''       )

										elif isinstance(_lowercase ,    _lowercase       ):
														SCREAMING_SNAKE_CASE_   :	int													=   [
														    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_lowercase       )
														]
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.cat(_lowercase ,    dim=0       )
										else:
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.movq.encode(_lowercase       ).latent_dist.sample(_lowercase       )

										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.movq.config.scaling_factor * init_latents

						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.cat([init_latents] ,    dim=0       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   init_latents.shape
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   randn_tensor(_lowercase ,    generator=_lowercase ,    device=_lowercase ,    dtype=_lowercase       )

						# get latents
						SCREAMING_SNAKE_CASE_   :	Any													=   self.scheduler.add_noise(_lowercase ,    _lowercase ,    _lowercase       )

						SCREAMING_SNAKE_CASE_   :	Any													=   init_latents

						return latents


		def   __snake_case			(						self ,    UpperCamelCase__=0       ):

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

						SCREAMING_SNAKE_CASE_   :	Any													=   torch.device(F'''cuda:{gpu_id}'''       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
										if cpu_offloaded_model is not None:
														cpu_offload(_lowercase ,    _lowercase       )


		def   __snake_case			(						self ,    UpperCamelCase__=0       ):

						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.'       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.device(F'''cuda:{gpu_id}'''       )

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

						SCREAMING_SNAKE_CASE_   :	Tuple													=   None
						for cpu_offloaded_model in [self.unet, self.movq]:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   cpu_offload_with_hook(_lowercase ,    _lowercase ,    prev_module_hook=_lowercase       )

						# We'll offload the last model manually.
						SCREAMING_SNAKE_CASE_   :	Tuple													=   hook


		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def   __snake_case			(						self       ):

						if not hasattr(self.unet ,    '_hf_hook'       ):
										return self.device
						for module in self.unet.modules():
										if (
										    hasattr(_lowercase ,    '_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(_lowercase       )
		def __call__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = 512 ,    UpperCamelCase__ = 512 ,    UpperCamelCase__ = 100 ,    UpperCamelCase__ = 4.0 ,    UpperCamelCase__ = 0.3 ,    UpperCamelCase__ = 1 ,    UpperCamelCase__ = None ,    UpperCamelCase__ = "pil" ,    UpperCamelCase__ = True ,    ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self._execution_device

						SCREAMING_SNAKE_CASE_   :	List[str]													=   guidance_scale > 1.0

						if isinstance(_lowercase ,    _lowercase       ):
										SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.cat(_lowercase ,    dim=0       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_embeds.shape[0]
						if isinstance(_lowercase ,    _lowercase       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.cat(_lowercase ,    dim=0       )

						if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE_   :	int													=   image_embeds.repeat_interleave(_lowercase ,    dim=0       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   negative_image_embeds.repeat_interleave(_lowercase ,    dim=0       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.cat([negative_image_embeds, image_embeds] ,    dim=0       ).to(dtype=self.unet.dtype ,    device=_lowercase       )

						if not isinstance(_lowercase ,    _lowercase       ):
										SCREAMING_SNAKE_CASE_   :	List[str]													=   [image]
						if not all(isinstance(_lowercase ,    (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
										raise ValueError(
										    F'''Input is in incorrect format: {[type(_lowercase       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''       )

						SCREAMING_SNAKE_CASE_   :	str													=   torch.cat([prepare_image(_lowercase ,    _lowercase ,    _lowercase       ) for i in image] ,    dim=0       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   image.to(dtype=image_embeds.dtype ,    device=_lowercase       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.movq.encode(_lowercase       )["""latents"""]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   latents.repeat_interleave(_lowercase ,    dim=0       )
						self.scheduler.set_timesteps(_lowercase ,    device=_lowercase       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.get_timesteps(_lowercase ,    _lowercase ,    _lowercase       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   timesteps[:1].repeat(batch_size * num_images_per_prompt       )
						SCREAMING_SNAKE_CASE_   :	int													=   downscale_height_and_width(_lowercase ,    _lowercase ,    self.movq_scale_factor       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.prepare_latents(
						    _lowercase ,    _lowercase ,    _lowercase ,    _lowercase ,    image_embeds.dtype ,    _lowercase ,    _lowercase       )
						for i, t in enumerate(self.progress_bar(_lowercase       )       ):
										# expand the latents if we are doing classifier free guidance
										SCREAMING_SNAKE_CASE_   :	Dict													=   torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

										SCREAMING_SNAKE_CASE_   :	Tuple													=   {"""image_embeds""": image_embeds}
										SCREAMING_SNAKE_CASE_   :	str													=   self.unet(
										    sample=_lowercase ,    timestep=_lowercase ,    encoder_hidden_states=_lowercase ,    added_cond_kwargs=_lowercase ,    return_dict=_lowercase ,    )[0]

										if do_classifier_free_guidance:
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   noise_pred.split(latents.shape[1] ,    dim=1       )
														SCREAMING_SNAKE_CASE_   :	int													=   noise_pred.chunk(2       )
														SCREAMING_SNAKE_CASE_   :	int													=   variance_pred.chunk(2       )
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   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"]
										):
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   noise_pred.split(latents.shape[1] ,    dim=1       )

										# compute the previous noisy sample x_t -> x_t-1
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.scheduler.step(
										    _lowercase ,    _lowercase ,    _lowercase ,    generator=_lowercase ,    )[0]

						# post-processing
						SCREAMING_SNAKE_CASE_   :	int													=   self.movq.decode(_lowercase ,    force_not_quantize=_lowercase       )["""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"]:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   image * 0.5 + 0.5
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image.clamp(0 ,    1       )
										SCREAMING_SNAKE_CASE_   :	List[str]													=   image.cpu().permute(0 ,    2 ,    3 ,    1       ).float().numpy()

						if output_type == "pil":
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.numpy_to_pil(_lowercase       )

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=_lowercase       )







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

A        =			[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
				# loop over downblocks/upblocks

				for j in range(2):
								# loop over resnets/attentions for downblocks
								A        =			f"""down_blocks.{i}.resnets.{j}."""
								A        =			f"""input_blocks.{3*i + j + 1}.0."""
								unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

								if i < 3:
												# no attention layers in down_blocks.3
												A        =			f"""down_blocks.{i}.attentions.{j}."""
												A        =			f"""input_blocks.{3*i + j + 1}.1."""
												unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

				for j in range(3):
								# loop over resnets/attentions for upblocks
								A        =			f"""up_blocks.{i}.resnets.{j}."""
								A        =			f"""output_blocks.{3*i + j}.0."""
								unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

								if i > 0:
												# no attention layers in up_blocks.0
												A        =			f"""up_blocks.{i}.attentions.{j}."""
												A        =			f"""output_blocks.{3*i + j}.1."""
												unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

				if i < 3:
								# no downsample in down_blocks.3
								A        =			f"""down_blocks.{i}.downsamplers.0.conv."""
								A        =			f"""input_blocks.{3*(i+1)}.0.op."""
								unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

								# no upsample in up_blocks.3
								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
								unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A        =			'mid_block.attentions.0.'
A        =			'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
				A        =			f"""mid_block.resnets.{j}."""
				A        =			f"""middle_block.{2*j}."""
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																SCREAMING_SNAKE_CASE_   :	int													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	List[str]													=   v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												SCREAMING_SNAKE_CASE_   :	str													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	str													=   {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

A        =			[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
				# down_blocks have two resnets
				for j in range(2):
								A        =			f"""encoder.down_blocks.{i}.resnets.{j}."""
								A        =			f"""encoder.down.{i}.block.{j}."""
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

				if i < 3:
								A        =			f"""down_blocks.{i}.downsamplers.0."""
								A        =			f"""down.{i}.downsample."""
								vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

								A        =			f"""up_blocks.{i}.upsamplers.0."""
								A        =			f"""up.{3-i}.upsample."""
								vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

				# up_blocks have three resnets
				# also, up blocks in hf are numbered in reverse from sd
				for j in range(3):
								A        =			f"""decoder.up_blocks.{i}.resnets.{j}."""
								A        =			f"""decoder.up.{3-i}.block.{j}."""
								vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
				A        =			f"""mid_block.resnets.{i}."""
				A        =			f"""mid.block_{i+1}."""
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A        =			[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				return w.reshape(*w.shape    ,    1    ,    1      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												SCREAMING_SNAKE_CASE_   :	Any													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	int													=   v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																SCREAMING_SNAKE_CASE_   :	Dict													=   v.replace(lowerCAmelCase__    ,    lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE_   :	Tuple													=   v
				SCREAMING_SNAKE_CASE_   :	Dict													=   {v: vae_state_dict[k] for k, v in mapping.items()}
				SCREAMING_SNAKE_CASE_   :	int													=   ['q', 'k', 'v', 'proj_out']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F'''mid.attn_1.{weight_name}.weight''' in k:
																print(F'''Reshaping {k} for SD format'''      )
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   reshape_weight_for_sd(lowerCAmelCase__      )
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


A        =			[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
A        =			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A        =			re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A        =			{'q': 0, 'k': 1, 'v': 2}



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {}
				SCREAMING_SNAKE_CASE_   :	List[str]													=   {}
				SCREAMING_SNAKE_CASE_   :	Any													=   {}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith('.self_attn.q_proj.weight'      )
								    or k.endswith('.self_attn.k_proj.weight'      )
								    or k.endswith('.self_attn.v_proj.weight'      )
								):
												SCREAMING_SNAKE_CASE_   :	int													=   k[: -len('.q_proj.weight'      )]
												SCREAMING_SNAKE_CASE_   :	List[str]													=   k[-len('q_proj.weight'      )]
												if k_pre not in capture_qkv_weight:
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	int													=   v
												continue

								if (
								    k.endswith('.self_attn.q_proj.bias'      )
								    or k.endswith('.self_attn.k_proj.bias'      )
								    or k.endswith('.self_attn.v_proj.bias'      )
								):
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   k[: -len('.q_proj.bias'      )]
												SCREAMING_SNAKE_CASE_   :	str													=   k[-len('q_proj.bias'      )]
												if k_pre not in capture_qkv_bias:
																SCREAMING_SNAKE_CASE_   :	Tuple													=   [None, None, None]
												SCREAMING_SNAKE_CASE_   :	Any													=   v
												continue

								SCREAMING_SNAKE_CASE_   :	Tuple													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Any													=   v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.cat(lowerCAmelCase__      )

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'      )
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   textenc_pattern.sub(lambda lowerCAmelCase__      : protected[re.escape(m.group(0      )      )]    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.cat(lowerCAmelCase__      )

				return new_state_dict



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				return text_enc_dict


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

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
				parser.add_argument(
				    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
				)

				A        =			parser.parse_args()

				assert args.model_path is not None, "Must provide a model path!"

				assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

				# Path for safetensors
				A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
				A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
				A        =			osp.join(args.model_path, 'text_encoder', 'model.safetensors')

				# Load models from safetensors if it exists, if it doesn't pytorch
				if osp.exists(unet_path):
								A        =			load_file(unet_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
								A        =			torch.load(unet_path, map_location='cpu')

				if osp.exists(vae_path):
								A        =			load_file(vae_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
								A        =			torch.load(vae_path, map_location='cpu')

				if osp.exists(text_enc_path):
								A        =			load_file(text_enc_path, device='cpu')
				else:
								A        =			osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
								A        =			torch.load(text_enc_path, map_location='cpu')

				# Convert the UNet model
				A        =			convert_unet_state_dict(unet_state_dict)
				A        =			{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

				# Convert the VAE model
				A        =			convert_vae_state_dict(vae_state_dict)
				A        =			{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

				# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
				A        =			'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

				if is_vaa_model:
								# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
								A        =			{'transformer.' + k: v for k, v in text_enc_dict.items()}
								A        =			convert_text_enc_state_dict_vaa(text_enc_dict)
								A        =			{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
				else:
								A        =			convert_text_enc_state_dict(text_enc_dict)
								A        =			{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

				# Put together new checkpoint
				A        =			{**unet_state_dict, **vae_state_dict, **text_enc_dict}
				if args.half:
								A        =			{k: v.half() for k, v in state_dict.items()}

				if args.use_safetensors:
								save_file(state_dict, args.checkpoint_path)
				else:
								A        =			{'state_dict': state_dict}
								torch.save(state_dict, args.checkpoint_path)