



"""simple docstring"""
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__SCREAMING_SNAKE_CASE	:    Dict     				=   [
    # (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'),
]

__SCREAMING_SNAKE_CASE	:    Any     				=   [
    # (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'),
]

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

__SCREAMING_SNAKE_CASE	:    Dict     				=   'mid_block.attentions.0.'
__SCREAMING_SNAKE_CASE	:    Any     				=   'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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



def 							lowerCAmelCase_( lowercase_  :				Tuple				)						->							int:
				# 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 #
# ================#

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

			if i < 3:
						__SCREAMING_SNAKE_CASE	:    int     				=   F"""down_blocks.{i}.downsamplers.0."""
						__SCREAMING_SNAKE_CASE	:    List[Any]     				=   F"""down.{i}.downsample."""
						vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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



def 							lowerCAmelCase_( lowercase_  :				Tuple				)						->							List[str]:
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape    , 1    , 1				)



def 							lowerCAmelCase_( lowercase_  :				List[str]				)						->							int:
				_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 #
# =========================#


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

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



def 							lowerCAmelCase_( lowercase_  :				List[str]				)						->							Union[str, Any]:
				_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 lowercase_				: 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 lowercase_				: 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 lowercase_				: protected[re.escape(m.group(0				)				)]    , __UpperCAmelCase				)
								_lowerCamelCase   	=					torch.cat(__UpperCAmelCase				)

				return new_state_dict



def 							lowerCAmelCase_( lowercase_  :				Optional[int]				)						->							Optional[Any]:
				return text_enc_dict


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE	:    int     				=   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.'''
			)

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

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

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

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

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

			# Put together new checkpoint
			__SCREAMING_SNAKE_CASE	:    Union[str, Any]     				=   {**unet_state_dict, **vae_state_dict, **text_enc_dict}
			if args.half:
						__SCREAMING_SNAKE_CASE	:    Dict     				=   {k: v.half() for k, v in state_dict.items()}

			if args.use_safetensors:
						save_file(state_dict, args.checkpoint_path)
			else:
						__SCREAMING_SNAKE_CASE	:    Optional[int]     				=   {'state_dict': state_dict}
						torch.save(state_dict, args.checkpoint_path)






from __future__ import annotations

from collections.abc import Iterator





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     value
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     None





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						int							,							_lowerCAmelCase    :						Node		):
       SCREAMING_SNAKE_CASE_      		=     tree



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Node | None		):
       if node is None:
             return 0
       return node.value + (
           self.depth_first_search(node.left		) + self.depth_first_search(node.right		)
       )




 def __iter__(				self    :						Dict		):
       yield self.depth_first_search(self.tree		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()