





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCAmelCase__						:			Union[str, Any]									=[
    # (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'''),
]

UpperCAmelCase__						:			Dict									=[
    # (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'''),
]

UpperCAmelCase__						:			Optional[int]									=[]
# 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
														UpperCAmelCase__						:			List[str]									=F"down_blocks.{i}.resnets.{j}."
														UpperCAmelCase__						:			List[Any]									=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
																					UpperCAmelCase__						:			Tuple									=F"down_blocks.{i}.attentions.{j}."
																					UpperCAmelCase__						:			int									=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
														UpperCAmelCase__						:			List[Any]									=F"up_blocks.{i}.resnets.{j}."
														UpperCAmelCase__						:			Optional[int]									=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
																					UpperCAmelCase__						:			str									=F"up_blocks.{i}.attentions.{j}."
																					UpperCAmelCase__						:			int									=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
														UpperCAmelCase__						:			Tuple									=F"down_blocks.{i}.downsamplers.0.conv."
														UpperCAmelCase__						:			Optional[int]									=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
														UpperCAmelCase__						:			List[str]									=F"up_blocks.{i}.upsamplers.0."
														UpperCAmelCase__						:			Dict									=F"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCAmelCase__						:			int									='''mid_block.attentions.0.'''
UpperCAmelCase__						:			Optional[Any]									='''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							UpperCAmelCase__						:			Union[str, Any]									=F"mid_block.resnets.{j}."
							UpperCAmelCase__						:			Tuple									=F"middle_block.{2*j}."
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def      _lowercase					(						_UpperCAmelCase				)						->  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.
				lowerCamelCase	 ={k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								lowerCamelCase	 =sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																lowerCamelCase	 =v.replace(_UpperCAmelCase			,      _UpperCAmelCase				)
												lowerCamelCase	 =v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												lowerCamelCase	 =v.replace(_UpperCAmelCase			,      _UpperCAmelCase				)
								lowerCamelCase	 =v
				lowerCamelCase	 ={v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

UpperCAmelCase__						:			str									=[
    # (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):
														UpperCAmelCase__						:			List[str]									=F"encoder.down_blocks.{i}.resnets.{j}."
														UpperCAmelCase__						:			Optional[Any]									=F"encoder.down.{i}.block.{j}."
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														UpperCAmelCase__						:			Any									=F"down_blocks.{i}.downsamplers.0."
														UpperCAmelCase__						:			str									=F"down.{i}.downsample."
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														UpperCAmelCase__						:			Any									=F"up_blocks.{i}.upsamplers.0."
														UpperCAmelCase__						:			List[str]									=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):
														UpperCAmelCase__						:			List[Any]									=F"decoder.up_blocks.{i}.resnets.{j}."
														UpperCAmelCase__						:			Tuple									=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):
							UpperCAmelCase__						:			Any									=F"mid_block.resnets.{i}."
							UpperCAmelCase__						:			List[Any]									=F"mid.block_{i+1}."
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape			,      1			,      1				)





def      _lowercase					(						_UpperCAmelCase				)						->  Tuple:
				lowerCamelCase	 ={k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												lowerCamelCase	 =v.replace(_UpperCAmelCase			,      _UpperCAmelCase				)
								lowerCamelCase	 =v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																lowerCamelCase	 =v.replace(_UpperCAmelCase			,      _UpperCAmelCase				)
												lowerCamelCase	 =v
				lowerCamelCase	 ={v: vae_state_dict[k] for k, v in mapping.items()}
				lowerCamelCase	 =["""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"""				)
																lowerCamelCase	 =reshape_weight_for_sd(_UpperCAmelCase				)
				return new_state_dict


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


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

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





def      _lowercase					(						_UpperCAmelCase				)						->  Tuple:
				lowerCamelCase	 ={}
				lowerCamelCase	 ={}
				lowerCamelCase	 ={}
				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"""				)
								):
												lowerCamelCase	 =k[: -len(""".q_proj.weight"""				)]
												lowerCamelCase	 =k[-len("""q_proj.weight"""				)]
												if k_pre not in capture_qkv_weight:
																lowerCamelCase	 =[None, None, None]
												lowerCamelCase	 =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"""				)
								):
												lowerCamelCase	 =k[: -len(""".q_proj.bias"""				)]
												lowerCamelCase	 =k[-len("""q_proj.bias"""				)]
												if k_pre not in capture_qkv_bias:
																lowerCamelCase	 =[None, None, None]
												lowerCamelCase	 =v
												continue

								lowerCamelCase	 =textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]			,      _UpperCAmelCase				)
								lowerCamelCase	 =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"""				)
								lowerCamelCase	 =textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]			,      _UpperCAmelCase				)
								lowerCamelCase	 =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"""				)
								lowerCamelCase	 =textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]			,      _UpperCAmelCase				)
								lowerCamelCase	 =torch.cat(_UpperCAmelCase				)

				return new_state_dict





def      _lowercase					(						_UpperCAmelCase				)						->  str:
				return text_enc_dict


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

							UpperCAmelCase__						:			List[str]									=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
							UpperCAmelCase__						:			str									=osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
							UpperCAmelCase__						:			List[str]									=osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
							UpperCAmelCase__						:			Optional[Any]									=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):
														UpperCAmelCase__						:			int									=load_file(unet_path, device='''cpu''')
							else:
														UpperCAmelCase__						:			Tuple									=osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
														UpperCAmelCase__						:			str									=torch.load(unet_path, map_location='''cpu''')

							if osp.exists(vae_path):
														UpperCAmelCase__						:			List[Any]									=load_file(vae_path, device='''cpu''')
							else:
														UpperCAmelCase__						:			Optional[int]									=osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
														UpperCAmelCase__						:			Optional[Any]									=torch.load(vae_path, map_location='''cpu''')

							if osp.exists(text_enc_path):
														UpperCAmelCase__						:			Any									=load_file(text_enc_path, device='''cpu''')
							else:
														UpperCAmelCase__						:			Union[str, Any]									=osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
														UpperCAmelCase__						:			Dict									=torch.load(text_enc_path, map_location='''cpu''')

							# Convert the UNet model
							UpperCAmelCase__						:			List[str]									=convert_unet_state_dict(unet_state_dict)
							UpperCAmelCase__						:			str									={'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							UpperCAmelCase__						:			Optional[Any]									=convert_vae_state_dict(vae_state_dict)
							UpperCAmelCase__						:			Optional[int]									={'''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
							UpperCAmelCase__						:			str									='''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
														UpperCAmelCase__						:			Dict									={'''transformer.''' + k: v for k, v in text_enc_dict.items()}
														UpperCAmelCase__						:			Union[str, Any]									=convert_text_enc_state_dict_vaa(text_enc_dict)
														UpperCAmelCase__						:			Any									={'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
							else:
														UpperCAmelCase__						:			Optional[Any]									=convert_text_enc_state_dict(text_enc_dict)
														UpperCAmelCase__						:			str									={'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							UpperCAmelCase__						:			Tuple									={**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														UpperCAmelCase__						:			Optional[int]									={k: v.half() for k, v in state_dict.items()}

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












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
							from .pipeline_kandinsky import KandinskyPipeline
							from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
							from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
							from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
							from .text_encoder import MultilingualCLIP





