




"""simple docstring"""







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__A   :  Optional[int]					=      [
    # (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   :  List[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'),
]

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

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

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

def 						snake_case__       (					_lowerCamelCase				)			->Optional[int]:






		"""simple docstring"""


		__lowercase				:		str												=				{k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				__lowercase				:		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:
								__lowercase				:		Union[str, Any]												=				v.replace(_lowerCamelCase,  _lowerCamelCase				)
						__lowercase				:		Optional[int]												=				v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						__lowercase				:		List[str]												=				v.replace(_lowerCamelCase,  _lowerCamelCase				)
				__lowercase				:		Tuple												=				v
		__lowercase				:		Tuple												=				{v: unet_state_dict[k] for k, v in mapping.items()}
		return new_state_dict


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

__A   :  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):
												__A   :  Tuple					=      F"""encoder.down_blocks.{i}.resnets.{j}."""
												__A   :  Any					=      F"""encoder.down.{i}.block.{j}."""
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				{k: k for k in vae_state_dict.keys()}
		for k, v in mapping.items():
				for sd_part, hf_part in vae_conversion_map:
						__lowercase				:		List[Any]												=				v.replace(_lowerCamelCase,  _lowerCamelCase				)
				__lowercase				:		int												=				v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								__lowercase				:		int												=				v.replace(_lowerCamelCase,  _lowerCamelCase				)
						__lowercase				:		int												=				v
		__lowercase				:		List[str]												=				{v: vae_state_dict[k] for k, v in mapping.items()}
		__lowercase				:		Any												=				["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'				)
								__lowercase				:		int												=				reshape_weight_for_sd(_lowerCamelCase				)
		return new_state_dict


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


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

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

def 						snake_case__       (					_lowerCamelCase				)			->str:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				{}
		__lowercase				:		List[Any]												=				{}
		__lowercase				:		Optional[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"				)
				):
						__lowercase				:		str												=				k[: -len(".q_proj.weight"				)]
						__lowercase				:		Any												=				k[-len("q_proj.weight"				)]
						if k_pre not in capture_qkv_weight:
								__lowercase				:		str												=				[None, None, None]
						__lowercase				:		List[Any]												=				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"				)
				):
						__lowercase				:		int												=				k[: -len(".q_proj.bias"				)]
						__lowercase				:		Any												=				k[-len("q_proj.bias"				)]
						if k_pre not in capture_qkv_bias:
								__lowercase				:		Tuple												=				[None, None, None]
						__lowercase				:		int												=				v
						continue

				__lowercase				:		List[Any]												=				textenc_pattern.sub(lambda _lowerCamelCase				: protected[re.escape(m.group(0				)				)],  _lowerCamelCase				)
				__lowercase				:		List[str]												=				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"				)
				__lowercase				:		Any												=				textenc_pattern.sub(lambda _lowerCamelCase				: protected[re.escape(m.group(0				)				)],  _lowerCamelCase				)
				__lowercase				:		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"				)
				__lowercase				:		List[Any]												=				textenc_pattern.sub(lambda _lowerCamelCase				: protected[re.escape(m.group(0				)				)],  _lowerCamelCase				)
				__lowercase				:		Any												=				torch.cat(_lowerCamelCase				)

		return new_state_dict

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		return text_enc_dict


if __name__ == "__main__":
						__A   :  Optional[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.'
						)

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

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

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

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

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

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

						if args.use_safetensors:
												save_file(state_dict, args.checkpoint_path)
						else:
												__A   :  List[str]					=      {'state_dict': state_dict}
												torch.save(state_dict, args.checkpoint_path)






"""simple docstring"""







from collections import deque

from .hash_table import HashTable




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def __init__(     self					:	Optional[int]    ,   *lowercase__					:	Any    ,   **lowercase__					:	Union[str, Any]						):
						super().__init__(*lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	Dict    ,   lowercase__					:	Dict						):
						__lowercase				:		Dict												=				deque([]						) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(lowercase__						)
						__lowercase				:		Union[str, Any]												=				self.values[key]



				def       snake_case    (     self					:	str						):
						return (
						    sum(self.charge_factor - len(lowercase__						) for slot in self.values						)
						    / self.size_table
						    * self.charge_factor
						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	str=None						):
						if not (
						    len(self.values[key]						) == self.charge_factor and self.values.count(lowercase__						) == 0
						):
								return key
						return super()._collision_resolution(lowercase__    ,   lowercase__						)
