



'''simple docstring'''

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 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Dict:
		_a					:				Optional[int]				=							{k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				_a					:				List[Any]				=							sd_name
		for k, v in mapping.items():
				if "resnets" in k:
						for sd_part, hf_part in unet_conversion_map_resnet:
								_a					:				int				=							v.replace(lowerCAmelCase_						,      lowerCAmelCase_  )
						_a					:				str				=							v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						_a					:				Optional[int]				=							v.replace(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				Optional[int]				=							v
		_a					:				Any				=							{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 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Dict:
		return w.reshape(*w.shape						,      1						,      1  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Tuple:
		_a					:				Optional[int]				=							{k: k for k in vae_state_dict.keys()}
		for k, v in mapping.items():
				for sd_part, hf_part in vae_conversion_map:
						_a					:				List[Any]				=							v.replace(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				int				=							v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								_a					:				List[Any]				=							v.replace(lowerCAmelCase_						,      lowerCAmelCase_  )
						_a					:				int				=							v
		_a					:				Optional[Any]				=							{v: vae_state_dict[k] for k, v in mapping.items()}
		_a					:				List[str]				=							['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"""  )
								_a					:				Tuple				=							reshape_weight_for_sd(lowerCAmelCase_  )
		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 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Tuple:
		_a					:				int				=							{}
		_a					:				Optional[Any]				=							{}
		_a					:				List[str]				=							{}
		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'  )
				):
						_a					:				List[Any]				=							k[: -len('.q_proj.weight'  )]
						_a					:				str				=							k[-len('q_proj.weight'  )]
						if k_pre not in capture_qkv_weight:
								_a					:				Optional[Any]				=							[None, None, None]
						_a					:				int				=							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'  )
				):
						_a					:				Dict				=							k[: -len('.q_proj.bias'  )]
						_a					:				str				=							k[-len('q_proj.bias'  )]
						if k_pre not in capture_qkv_bias:
								_a					:				str				=							[None, None, None]
						_a					:				Any				=							v
						continue

				_a					:				Union[str, Any]				=							textenc_pattern.sub(lambda lowerCAmelCase_  : protected[re.escape(m.group(0  )  )]						,      lowerCAmelCase_  )
				_a					:				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'  )
				_a					:				str				=							textenc_pattern.sub(lambda lowerCAmelCase_  : protected[re.escape(m.group(0  )  )]						,      lowerCAmelCase_  )
				_a					:				Optional[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'  )
				_a					:				Dict				=							textenc_pattern.sub(lambda lowerCAmelCase_  : protected[re.escape(m.group(0  )  )]						,      lowerCAmelCase_  )
				_a					:				int				=							torch.cat(lowerCAmelCase_  )

		return new_state_dict



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		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)









'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

__lowerCAmelCase						      =       '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase						      =       BASE_URL + '''/user'''

# https://github.com/settings/tokens
__lowerCAmelCase						      =       os.environ.get('''USER_TOKEN''', '''''')



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				dict[Any, Any]:
		_a					:				Union[str, Any]				=							{
		    'Authorization': f"""token {auth_token}""",
		    'Accept': 'application/vnd.github.v3+json',
		}
		return requests.get(lowerCAmelCase_						,      headers=lowerCAmelCase_  ).json()


if __name__ == "__main__":  # pragma: no cover
							if USER_TOKEN:
														for key, value in fetch_github_info(USER_TOKEN).items():
																					print(f"""{key}: {value}""")
							else:
														raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')




