



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

snake_case__       :  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"""),
]

snake_case__       :  List[str]     		= [
    # (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"""),
]

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

snake_case__       :  str     		= """mid_block.attentions.0."""
snake_case__       :  Optional[Any]     		= """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	snake_case__       :  str     		= F'''mid_block.resnets.{j}.'''
	snake_case__       :  Dict     		= F'''middle_block.{2*j}.'''
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	# 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.
	__lowercase									=      {k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		__lowercase									=      sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				__lowercase									=      v.replace(__lowerCAmelCase	,						__lowerCAmelCase )
			__lowercase									=      v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			__lowercase									=      v.replace(__lowerCAmelCase	,						__lowerCAmelCase )
		__lowercase									=      v
	__lowercase									=      {v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


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

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

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

		snake_case__       :  List[str]     		= F'''up_blocks.{i}.upsamplers.0.'''
		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):
		snake_case__       :  str     		= F'''decoder.up_blocks.{i}.resnets.{j}.'''
		snake_case__       :  Tuple     		= 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):
	snake_case__       :  Tuple     		= F'''mid_block.resnets.{i}.'''
	snake_case__       :  List[str]     		= F'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	# convert HF linear weights to SD conv2d weights
	return w.reshape(*w.shape	,						1	,						1 )



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	__lowercase									=      {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									=      v.replace(__lowerCAmelCase	,						__lowerCAmelCase )
		__lowercase									=      v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				__lowercase									=      v.replace(__lowerCAmelCase	,						__lowerCAmelCase )
			__lowercase									=      v
	__lowercase									=      {v: vae_state_dict[k] for k, v in mapping.items()}
	__lowercase									=      ["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									=      reshape_weight_for_sd(__lowerCAmelCase )
	return new_state_dict


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


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

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



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	__lowercase									=      {}
	__lowercase									=      {}
	__lowercase									=      {}
	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									=      k[: -len(".q_proj.weight" )]
			__lowercase									=      k[-len("q_proj.weight" )]
			if k_pre not in capture_qkv_weight:
				__lowercase									=      [None, None, None]
			__lowercase									=      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									=      k[: -len(".q_proj.bias" )]
			__lowercase									=      k[-len("q_proj.bias" )]
			if k_pre not in capture_qkv_bias:
				__lowercase									=      [None, None, None]
			__lowercase									=      v
			continue

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

	return new_state_dict



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	return text_enc_dict


if __name__ == "__main__":
	snake_case__       :  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."""
	)

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

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

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

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

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

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

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










'''simple docstring'''




from __future__ import annotations

import os
from collections.abc import Mapping

_SCREAMING_SNAKE_CASE							     =			tuple[int, int]
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			set[int]   ,       __snake_case							:			Mapping[EdgeT, int]						)->	None:
        snake_case 						=   vertices
        snake_case 						=   {
            (min(__snake_case						), max(__snake_case						)): weight for edge, weight in edges.items()
        }



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			EdgeT   ,       __snake_case							:			int						)->	None:
        self.vertices.add(edge[0]						)
        self.vertices.add(edge[1]						)
        snake_case 						=   weight



     def      lowerCAmelCase			(   self							:			str						)->	Graph:
        snake_case 						=   Graph({min(self.vertices						)}   ,       {}						)
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42

        while len(subgraph.vertices						) < len(self.vertices						):
           snake_case 						=   max(self.edges.values()						) + 1
           for edge, weight in self.edges.items():
              if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                 if weight < min_weight:
                    snake_case 						=   edge
                    snake_case 						=   weight

           subgraph.add_edge(__snake_case   ,       __snake_case						)

        return subgraph





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "p107_network.txt"		)		->  int:
   snake_case 						=   os.path.abspath(os.path.dirname(__lowerCAmelCase		)		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {}
   snake_case 						=   42
   snake_case 						=   42
   snake_case 						=   42

   with open(__lowerCAmelCase		) as f:
      snake_case 						=   f.read().strip().split("""\n"""		)

   snake_case 						=   [line.split(""","""		) for line in data]

   for edgea in range(1      ,    len(__lowerCAmelCase		)		):
      for edgea in range(__lowerCAmelCase		):
         if adjaceny_matrix[edgea][edgea] != "-":
            snake_case 						=   int(adjaceny_matrix[edgea][edgea]		)

   snake_case 						=   Graph(set(range(len(__lowerCAmelCase		)		)		)      ,    __lowerCAmelCase		)

   snake_case 						=   graph.prims_algorithm()

   snake_case 						=   sum(graph.edges.values()		)
   snake_case 						=   sum(subgraph.edges.values()		)

   return initial_total - optimal_total


if __name__ == "__main__":
 print(F"""{solution() = }""")

