





'''simple docstring'''






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__UpperCamelCase					    =	[
    # (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					    =	[
    # (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					    =	[]
# 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					    =	f"""down_blocks.{i}.resnets.{j}."""
		__UpperCamelCase					    =	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					    =	f"""down_blocks.{i}.attentions.{j}."""
			__UpperCamelCase					    =	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					    =	f"""up_blocks.{i}.resnets.{j}."""
		__UpperCamelCase					    =	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					    =	f"""up_blocks.{i}.attentions.{j}."""
			__UpperCamelCase					    =	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					    =	f"""down_blocks.{i}.downsamplers.0.conv."""
		__UpperCamelCase					    =	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					    =	f"""up_blocks.{i}.upsamplers.0."""
		__UpperCamelCase					    =	f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__UpperCamelCase					    =	"mid_block.attentions.0."
__UpperCamelCase					    =	"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	__UpperCamelCase					    =	f"""mid_block.resnets.{j}."""
	__UpperCamelCase					    =	f"""middle_block.{2*j}."""
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			__snake_case					:      str							      =     {k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						__snake_case					:      Dict							      =     sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												__snake_case					:      Optional[int]							      =     v.replace(_lowerCamelCase    ,	_lowerCamelCase )
									__snake_case					:      Optional[Any]							      =     v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									__snake_case					:      Tuple							      =     v.replace(_lowerCamelCase    ,	_lowerCamelCase )
						__snake_case					:      int							      =     v
			__snake_case					:      str							      =     {v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


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

__UpperCamelCase					    =	[
    # (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					    =	f"""encoder.down_blocks.{i}.resnets.{j}."""
		__UpperCamelCase					    =	f"""encoder.down.{i}.block.{j}."""
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		__UpperCamelCase					    =	f"""down_blocks.{i}.downsamplers.0."""
		__UpperCamelCase					    =	f"""down.{i}.downsample."""
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		__UpperCamelCase					    =	f"""up_blocks.{i}.upsamplers.0."""
		__UpperCamelCase					    =	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					    =	f"""decoder.up_blocks.{i}.resnets.{j}."""
		__UpperCamelCase					    =	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					    =	f"""mid_block.resnets.{i}."""
	__UpperCamelCase					    =	f"""mid.block_{i+1}."""
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__UpperCamelCase					    =	[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]







def 					_a						(       _lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase ) ->				Tuple:



			"""simple docstring"""


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


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


__UpperCamelCase					    =	[
    # (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					    =	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__UpperCamelCase					    =	re.compile("|".join(protected.keys()))

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







def 					_a						(       _lowerCamelCase ) ->				Optional[Any]:



			"""simple docstring"""


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

						__snake_case					:      str							      =     textenc_pattern.sub(lambda _lowerCamelCase : protected[re.escape(m.group(0 ) )]    ,	_lowerCamelCase )
						__snake_case					:      Tuple							      =     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					:      Tuple							      =     textenc_pattern.sub(lambda _lowerCamelCase : protected[re.escape(m.group(0 ) )]    ,	_lowerCamelCase )
						__snake_case					:      int							      =     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""" )
						__snake_case					:      Any							      =     textenc_pattern.sub(lambda _lowerCamelCase : protected[re.escape(m.group(0 ) )]    ,	_lowerCamelCase )
						__snake_case					:      Optional[int]							      =     torch.cat(_lowerCamelCase )

			return new_state_dict







def 					_a						(       _lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			return text_enc_dict


if __name__ == "__main__":
	__UpperCamelCase					    =	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					    =	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					    =	osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
	__UpperCamelCase					    =	osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
	__UpperCamelCase					    =	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					    =	load_file(unet_path, device="cpu")
	else:
		__UpperCamelCase					    =	osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
		__UpperCamelCase					    =	torch.load(unet_path, map_location="cpu")

	if osp.exists(vae_path):
		__UpperCamelCase					    =	load_file(vae_path, device="cpu")
	else:
		__UpperCamelCase					    =	osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
		__UpperCamelCase					    =	torch.load(vae_path, map_location="cpu")

	if osp.exists(text_enc_path):
		__UpperCamelCase					    =	load_file(text_enc_path, device="cpu")
	else:
		__UpperCamelCase					    =	osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
		__UpperCamelCase					    =	torch.load(text_enc_path, map_location="cpu")

	# Convert the UNet model
	__UpperCamelCase					    =	convert_unet_state_dict(unet_state_dict)
	__UpperCamelCase					    =	{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

	# Convert the VAE model
	__UpperCamelCase					    =	convert_vae_state_dict(vae_state_dict)
	__UpperCamelCase					    =	{"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					    =	"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					    =	{"transformer." + k: v for k, v in text_enc_dict.items()}
		__UpperCamelCase					    =	convert_text_enc_state_dict_vaa(text_enc_dict)
		__UpperCamelCase					    =	{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
	else:
		__UpperCamelCase					    =	convert_text_enc_state_dict(text_enc_dict)
		__UpperCamelCase					    =	{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

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

	if args.use_safetensors:
		save_file(state_dict, args.checkpoint_path)
	else:
		__UpperCamelCase					    =	{"state_dict": state_dict}
		torch.save(state_dict, args.checkpoint_path)









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     det - k * (trace**2)
											# Can change the value
											if r > 0.5:
														corner_list.append([x, y, r]			)
														color_img.itemset((y, x, 0)  ,      0			)
														color_img.itemset((y, x, 1)  ,      0			)
														color_img.itemset((y, x, 2)  ,      2_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


