


"""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							:    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'),
]

__SCREAMING_SNAKE_CASE							:    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'),
]

__SCREAMING_SNAKE_CASE							:    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
								__SCREAMING_SNAKE_CASE							:    int		=    f"""down_blocks.{i}.resnets.{j}."""
								__SCREAMING_SNAKE_CASE							:    str		=    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[int]		=    f"""down_blocks.{i}.attentions.{j}."""
												__SCREAMING_SNAKE_CASE							:    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
								__SCREAMING_SNAKE_CASE							:    Any		=    f"""up_blocks.{i}.resnets.{j}."""
								__SCREAMING_SNAKE_CASE							:    Tuple		=    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							:    Optional[Any]		=    f"""up_blocks.{i}.attentions.{j}."""
												__SCREAMING_SNAKE_CASE							:    str		=    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							:    Optional[int]		=    f"""down_blocks.{i}.downsamplers.0.conv."""
								__SCREAMING_SNAKE_CASE							:    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[Any]		=    f"""up_blocks.{i}.upsamplers.0."""
								__SCREAMING_SNAKE_CASE							:    Union[str, 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							:    List[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							:    str		=    f"""middle_block.{2*j}."""
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							snake_case_           =		{k: k for k in unet_state_dict.keys()}
							for sd_name, hf_name in unet_conversion_map:
														snake_case_           =		sd_name
							for k, v in mapping.items():
														if "resnets" in k:
																					for sd_part, hf_part in unet_conversion_map_resnet:
																												snake_case_           =		v.replace(lowercase__				, lowercase__				)
																					snake_case_           =		v
							for k, v in mapping.items():
														for sd_part, hf_part in unet_conversion_map_layer:
																					snake_case_           =		v.replace(lowercase__				, lowercase__				)
														snake_case_           =		v
							snake_case_           =		{v: unet_state_dict[k] for k, v in mapping.items()}
							return new_state_dict


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

__SCREAMING_SNAKE_CASE							:    List[Any]		=    [
    # (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							:    List[Any]		=    f"""encoder.down_blocks.{i}.resnets.{j}."""
								__SCREAMING_SNAKE_CASE							:    Tuple		=    f"""encoder.down.{i}.block.{j}."""
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

				if i < 3:
								__SCREAMING_SNAKE_CASE							:    List[Any]		=    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							:    Optional[Any]		=    f"""up_blocks.{i}.upsamplers.0."""
								__SCREAMING_SNAKE_CASE							:    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):
								__SCREAMING_SNAKE_CASE							:    str		=    f"""decoder.up_blocks.{i}.resnets.{j}."""
								__SCREAMING_SNAKE_CASE							:    Dict		=    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							:    Tuple		=    f"""mid_block.resnets.{i}."""
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    f"""mid.block_{i+1}."""
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							return w.reshape(*w.shape				, 1				, 1				)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							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:
																					snake_case_           =		v.replace(lowercase__				, lowercase__				)
														snake_case_           =		v
							for k, v in mapping.items():
														if "attentions" in k:
																					for sd_part, hf_part in vae_conversion_map_attn:
																												snake_case_           =		v.replace(lowercase__				, lowercase__				)
																					snake_case_           =		v
							snake_case_           =		{v: vae_state_dict[k] for k, v in mapping.items()}
							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"""				)
																												snake_case_           =		reshape_weight_for_sd(lowercase__				)
							return new_state_dict


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


__SCREAMING_SNAKE_CASE							:    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'),
]
__SCREAMING_SNAKE_CASE							:    Any		=    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__SCREAMING_SNAKE_CASE							:    Any		=    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 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							snake_case_           =		{}
							snake_case_           =		{}
							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"""				)
														):
																					snake_case_           =		k[: -len(""".q_proj.weight"""				)]
																					snake_case_           =		k[-len("""q_proj.weight"""				)]
																					if k_pre not in capture_qkv_weight:
																												snake_case_           =		[None, None, None]
																					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"""				)
														):
																					snake_case_           =		k[: -len(""".q_proj.bias"""				)]
																					snake_case_           =		k[-len("""q_proj.bias"""				)]
																					if k_pre not in capture_qkv_bias:
																												snake_case_           =		[None, None, None]
																					snake_case_           =		v
																					continue

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

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

							return new_state_dict







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							return text_enc_dict


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Dict		=    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							:    Dict		=    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							:    List[str]		=    osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
				__SCREAMING_SNAKE_CASE							:    Optional[int]		=    osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
				__SCREAMING_SNAKE_CASE							:    Tuple		=    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							:    Optional[int]		=    load_file(unet_path, device='cpu')
				else:
								__SCREAMING_SNAKE_CASE							:    str		=    osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
								__SCREAMING_SNAKE_CASE							:    Tuple		=    torch.load(unet_path, map_location='cpu')

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

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

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

				# Put together new checkpoint
				__SCREAMING_SNAKE_CASE							:    Tuple		=    {**unet_state_dict, **vae_state_dict, **text_enc_dict}
				if args.half:
								__SCREAMING_SNAKE_CASE							:    List[str]		=    {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							:    Any		=    {'state_dict': state_dict}
								torch.save(state_dict, args.checkpoint_path)







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# bit count represents no. of bits in the gray code
							if bit_count < 0:
														raise ValueError("""The given input must be positive"""				)

							# get the generated string sequence
							snake_case_           =		gray_code_sequence_string(_SCREAMING_SNAKE_CASE				)
							#
							# convert them to integers
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														snake_case_           =		int(sequence[i]				, 2				)

							return sequence







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# The approach is a recursive one
							# Base case achieved when either n = 0 or n=1
							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							snake_case_           =		1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							snake_case_           =		gray_code_sequence_string(bit_count - 1				)

							snake_case_           =		[]

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2				):
														snake_case_           =		"""0""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2				)				):
														snake_case_           =		"""1""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



