



'''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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					{k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        lowerCAmelCase__ :  Optional[int]							      =					sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                lowerCAmelCase__ :  Any							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            lowerCAmelCase__ :  List[Any]							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    lowerCAmelCase__ :  Tuple							      =					{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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return w.reshape(*w.shape							,    1							,    1			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  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:
            lowerCAmelCase__ :  str							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                lowerCAmelCase__ :  Dict							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    lowerCAmelCase__ :  Union[str, Any]							      =					{v: vae_state_dict[k] for k, v in mapping.items()}
    lowerCAmelCase__ :  Tuple							      =					["""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"""			)
                lowerCAmelCase__ :  Optional[int]							      =					reshape_weight_for_sd(UpperCamelCase			)
    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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

        lowerCAmelCase__ :  Dict							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					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"""			)
        lowerCAmelCase__ :  Any							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Tuple							      =					torch.cat(UpperCamelCase			)

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

    return new_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    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

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

						for _ in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       graph.transition(__SCREAMING_SNAKE_CASE							)
												visited[node] += 1

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()



