





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_lowerCamelCase   				=			[
    # (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'),
]

_lowerCamelCase   				=			[
    # (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'),
]

_lowerCamelCase   				=			[]
# 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
		_lowerCamelCase   				=			F'''down_blocks.{i}.resnets.{j}.'''
		_lowerCamelCase   				=			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
			_lowerCamelCase   				=			F'''down_blocks.{i}.attentions.{j}.'''
			_lowerCamelCase   				=			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
		_lowerCamelCase   				=			F'''up_blocks.{i}.resnets.{j}.'''
		_lowerCamelCase   				=			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
			_lowerCamelCase   				=			F'''up_blocks.{i}.attentions.{j}.'''
			_lowerCamelCase   				=			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
		_lowerCamelCase   				=			F'''down_blocks.{i}.downsamplers.0.conv.'''
		_lowerCamelCase   				=			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
		_lowerCamelCase   				=			F'''up_blocks.{i}.upsamplers.0.'''
		_lowerCamelCase   				=			F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

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

for j in range(2):
	_lowerCamelCase   				=			F'''mid_block.resnets.{j}.'''
	_lowerCamelCase   				=			F'''middle_block.{2*j}.'''
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		# buyer beware: this is a *brittle* function,
		# and correct output requires that all of these pieces interact in
		# the exact order in which I have arranged them.
		SCREAMING_SNAKE_CASE__			=      {k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				SCREAMING_SNAKE_CASE__			=      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__			=      v.replace(UpperCamelCase__					,   UpperCamelCase__    )
						SCREAMING_SNAKE_CASE__			=      v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						SCREAMING_SNAKE_CASE__			=      v.replace(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      v
		SCREAMING_SNAKE_CASE__			=      {v: unet_state_dict[k] for k, v in mapping.items()}
		return new_state_dict


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

_lowerCamelCase   				=			[
    # (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):
		_lowerCamelCase   				=			F'''encoder.down_blocks.{i}.resnets.{j}.'''
		_lowerCamelCase   				=			F'''encoder.down.{i}.block.{j}.'''
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		_lowerCamelCase   				=			F'''down_blocks.{i}.downsamplers.0.'''
		_lowerCamelCase   				=			F'''down.{i}.downsample.'''
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		_lowerCamelCase   				=			F'''up_blocks.{i}.upsamplers.0.'''
		_lowerCamelCase   				=			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):
		_lowerCamelCase   				=			F'''decoder.up_blocks.{i}.resnets.{j}.'''
		_lowerCamelCase   				=			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):
	_lowerCamelCase   				=			F'''mid_block.resnets.{i}.'''
	_lowerCamelCase   				=			F'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str    ):
		# convert HF linear weights to SD conv2d weights
		return w.reshape(*w.shape					,   1					,   1    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):
		SCREAMING_SNAKE_CASE__			=      {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__			=      v.replace(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								SCREAMING_SNAKE_CASE__			=      v.replace(UpperCamelCase__					,   UpperCamelCase__    )
						SCREAMING_SNAKE_CASE__			=      v
		SCREAMING_SNAKE_CASE__			=      {v: vae_state_dict[k] for k, v in mapping.items()}
		SCREAMING_SNAKE_CASE__			=      ["""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__			=      reshape_weight_for_sd(UpperCamelCase__    )
		return new_state_dict


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


_lowerCamelCase   				=			[
    # (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'),
]
_lowerCamelCase   				=			{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCamelCase   				=			re.compile('|'.join(protected.keys()))

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):
		SCREAMING_SNAKE_CASE__			=      {}
		SCREAMING_SNAKE_CASE__			=      {}
		SCREAMING_SNAKE_CASE__			=      {}
		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__			=      k[: -len(""".q_proj.weight"""    )]
						SCREAMING_SNAKE_CASE__			=      k[-len("""q_proj.weight"""    )]
						if k_pre not in capture_qkv_weight:
								SCREAMING_SNAKE_CASE__			=      [None, None, None]
						SCREAMING_SNAKE_CASE__			=      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__			=      k[: -len(""".q_proj.bias"""    )]
						SCREAMING_SNAKE_CASE__			=      k[-len("""q_proj.bias"""    )]
						if k_pre not in capture_qkv_bias:
								SCREAMING_SNAKE_CASE__			=      [None, None, None]
						SCREAMING_SNAKE_CASE__			=      v
						continue

				SCREAMING_SNAKE_CASE__			=      textenc_pattern.sub(lambda UpperCamelCase__    : protected[re.escape(m.group(0    )    )]					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      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__			=      textenc_pattern.sub(lambda UpperCamelCase__    : protected[re.escape(m.group(0    )    )]					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__    )

		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__			=      textenc_pattern.sub(lambda UpperCamelCase__    : protected[re.escape(m.group(0    )    )]					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__    )

		return new_state_dict






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]    ):
		return text_enc_dict


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

	_lowerCamelCase   				=			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
	_lowerCamelCase   				=			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
	_lowerCamelCase   				=			osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
	_lowerCamelCase   				=			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):
		_lowerCamelCase   				=			load_file(unet_path, device='cpu')
	else:
		_lowerCamelCase   				=			osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
		_lowerCamelCase   				=			torch.load(unet_path, map_location='cpu')

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

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

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

	# Convert the VAE model
	_lowerCamelCase   				=			convert_vae_state_dict(vae_state_dict)
	_lowerCamelCase   				=			{'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
	_lowerCamelCase   				=			'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
		_lowerCamelCase   				=			{'transformer.' + k: v for k, v in text_enc_dict.items()}
		_lowerCamelCase   				=			convert_text_enc_state_dict_vaa(text_enc_dict)
		_lowerCamelCase   				=			{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
	else:
		_lowerCamelCase   				=			convert_text_enc_state_dict(text_enc_dict)
		_lowerCamelCase   				=			{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

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






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""					,   UpperCamelCase__					,   )
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return image
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [image]

		if isinstance(image[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      image[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE__			=      [np.array(i.resize((w, h)					,   resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      np.array(UpperCamelCase__    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      image.transpose(0					,   3					,   1					,   2    )
				SCREAMING_SNAKE_CASE__			=      2.0 * image - 1.0
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(image[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return image






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return mask
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [mask]

		if isinstance(mask[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      mask[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE__			=      [np.array(m.convert("""L"""    ).resize((w, h)					,   resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      0
				SCREAMING_SNAKE_CASE__			=      1
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(mask[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return mask




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42


							def __init__(							self    :Any							,				__A    :List[Any]							,				__A    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__()
									self.register_modules(unet=__A							,				scheduler=__A		)


							@torch.no_grad()
							def __call__(							self    :str							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :int = 250							,				__A    :float = 0.0							,				__A    :int = 10							,				__A    :int = 10							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      image

									SCREAMING_SNAKE_CASE__			=      _preprocess_image(__A		)
									SCREAMING_SNAKE_CASE__			=      original_image.to(device=self.device							,				dtype=self.unet.dtype		)
									SCREAMING_SNAKE_CASE__			=      _preprocess_mask(__A		)
									SCREAMING_SNAKE_CASE__			=      mask_image.to(device=self.device							,				dtype=self.unet.dtype		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(__A							,				__A		) and len(__A		) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__A		)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=self.device							,				dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(__A							,				__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      eta

									SCREAMING_SNAKE_CASE__			=      self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE__			=      generator[0] if isinstance(__A							,				__A		) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											if t < t_last:
													# predict the noise residual
													SCREAMING_SNAKE_CASE__			=      self.unet(__A							,				__A		).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE__			=      self.scheduler.step(__A							,				__A							,				__A							,				__A							,				__A							,				__A		).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE__			=      self.scheduler.undo_step(__A							,				__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      t

									SCREAMING_SNAKE_CASE__			=      (image / 2 + 0.5).clamp(0							,				1		)
									SCREAMING_SNAKE_CASE__			=      image.cpu().permute(0							,				2							,				3							,				1		).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE__			=      self.numpy_to_pil(__A		)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)