


'''simple docstring'''






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__A						:						List[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'),
]

__A						:						List[Any]  =  [
    # (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'),
]

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

__A						:						Union[str, Any]  =  'mid_block.attentions.0.'
__A						:						Union[str, Any]  =  'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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





def   UpperCAmelCase   (		lowerCamelCase_	:List[Any]			):







		'''simple docstring'''
		snake_case_		:				List[Any]      							=    {k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				snake_case_		:				List[Any]      							=    sd_name
		for k, v in mapping.items():
				if "resnets" in k:
						for sd_part, hf_part in unet_conversion_map_resnet:
								snake_case_		:				Any      							=    v.replace(lowercase_					,   lowercase_			)
						snake_case_		:				Dict      							=    v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						snake_case_		:				int      							=    v.replace(lowercase_					,   lowercase_			)
				snake_case_		:				Union[str, Any]      							=    v
		snake_case_		:				str      							=    {v: unet_state_dict[k] for k, v in mapping.items()}
		return new_state_dict


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

__A						:						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):
														__A						:						int  =  F'encoder.down_blocks.{i}.resnets.{j}.'
														__A						:						Dict  =  F'encoder.down.{i}.block.{j}.'
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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





def   UpperCAmelCase   (		lowerCamelCase_	:List[str]			):







		'''simple docstring'''
		return w.reshape(*w.shape					,   1					,   1			)





def   UpperCAmelCase   (		lowerCamelCase_	:Optional[int]			):







		'''simple docstring'''
		snake_case_		:				Any      							=    {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_		:				int      							=    v.replace(lowercase_					,   lowercase_			)
				snake_case_		:				int      							=    v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								snake_case_		:				Tuple      							=    v.replace(lowercase_					,   lowercase_			)
						snake_case_		:				Union[str, Any]      							=    v
		snake_case_		:				List[Any]      							=    {v: vae_state_dict[k] for k, v in mapping.items()}
		snake_case_		:				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'''			)
								snake_case_		:				Union[str, Any]      							=    reshape_weight_for_sd(lowercase_			)
		return new_state_dict


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


__A						:						Dict  =  [
    # (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'),
]
__A						:						Union[str, Any]  =  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__A						:						str  =  re.compile('|'.join(protected.keys()))

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





def   UpperCAmelCase   (		lowerCamelCase_	:Tuple			):







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

				snake_case_		:				str      							=    textenc_pattern.sub(lambda lowerCamelCase_			: protected[re.escape(m.group(0			)			)]					,   lowercase_			)
				snake_case_		:				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"""			)
				snake_case_		:				Dict      							=    textenc_pattern.sub(lambda lowerCamelCase_			: protected[re.escape(m.group(0			)			)]					,   lowercase_			)
				snake_case_		:				Dict      							=    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_		:				Union[str, Any]      							=    textenc_pattern.sub(lambda lowerCamelCase_			: protected[re.escape(m.group(0			)			)]					,   lowercase_			)
				snake_case_		:				str      							=    torch.cat(lowercase_			)

		return new_state_dict





def   UpperCAmelCase   (		lowerCamelCase_	:Optional[int]			):







		'''simple docstring'''
		return text_enc_dict


if __name__ == "__main__":
							__A						:						Optional[int]  =  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.'
							)

							__A						:						Optional[int]  =  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
							__A						:						Tuple  =  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
							__A						:						int  =  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
							__A						:						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):
														__A						:						Optional[int]  =  load_file(unet_path, device='cpu')
							else:
														__A						:						Union[str, Any]  =  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
														__A						:						int  =  torch.load(unet_path, map_location='cpu')

							if osp.exists(vae_path):
														__A						:						int  =  load_file(vae_path, device='cpu')
							else:
														__A						:						Dict  =  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
														__A						:						Optional[Any]  =  torch.load(vae_path, map_location='cpu')

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

							# Convert the UNet model
							__A						:						List[str]  =  convert_unet_state_dict(unet_state_dict)
							__A						:						Union[str, Any]  =  {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

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

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

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														__A						:						Dict  =  {'state_dict': state_dict}
														torch.save(state_dict, args.checkpoint_path)






"""simple docstring"""


import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


lowerCamelCase__						= False
try:
  lowerCamelCase__						= _is_package_available("google.colab")
except ModuleNotFoundError:
  pass



@input.register
class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							Union[str, Any]       , __a :							str = None       , __a :							list = []						)		->					Union[str, Any]:
        _UpperCamelCase   :						Any    =							0
        _UpperCamelCase   :						Any    =							choices
        _UpperCamelCase   :						Tuple    =							prompt
        if sys.platform == "win32":
              _UpperCamelCase   :						str    =							"*"
        else:
              _UpperCamelCase   :						Dict    =							"➔ "

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , __a :							Tuple       , __a :							str = ""						)		->					Dict:
        if sys.platform != "win32":
              writeColor(self.choices[index]       , 32       , __a						)
        else:
              forceWrite(self.choices[index]       , __a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							int						)		->					Dict:
        if index == self.position:
              forceWrite(F''' {self.arrow_char} '''						)
              self.write_choice(__a						)
        else:
              forceWrite(F'''    {self.choices[index]}'''						)
        reset_cursor()

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							Direction       , __a :							int = 1						)		->					List[str]:
        _UpperCamelCase   :						Tuple    =							self.position
        if direction == Direction.DOWN:
              if self.position + 1 >= len(self.choices						):
                    return
              self.position += num_spaces
        else:
              if self.position - 1 < 0:
                    return
              self.position -= num_spaces
        clear_line()
        self.print_choice(__a						)
        move_cursor(__a       , direction.name						)
        self.print_choice(self.position						)

  @input.mark(KEYMAP["up"]						)
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        self.move_direction(Direction.UP						)

  @input.mark(KEYMAP["down"]						)
  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        self.move_direction(Direction.DOWN						)

  @input.mark(KEYMAP["newline"]						)
  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					str:
        move_cursor(len(self.choices						) - self.position       , "DOWN"						)
        return self.position

  @input.mark(KEYMAP["interrupt"]						)
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Tuple:
        move_cursor(len(self.choices						) - self.position       , "DOWN"						)
        raise KeyboardInterrupt

  @input.mark_multiple(*[KEYMAP[str(__a						)] for number in range(10						)]						)
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					List[str]:
        _UpperCamelCase   :						Union[str, Any]    =							int(chr(self.current_selection						)						)
        _UpperCamelCase   :						List[str]    =							index - self.position
        if index == self.position:
              return
        if index < len(self.choices						):
              if self.position > index:
                    self.move_direction(Direction.UP       , -movement						)
              elif self.position < index:
                    self.move_direction(Direction.DOWN       , __a						)
              else:
                    return
        else:
              return


  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							int = 0						)		->					List[Any]:
        if self.prompt:
              linebreak()
              forceWrite(self.prompt       , "\n"						)
              if in_colab:
                    forceWrite("Please input a choice index (starting from 0), and press enter"       , "\n"						)
              else:
                    forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"       , "\n"						)
        _UpperCamelCase   :						int    =							default_choice
        for i in range(len(self.choices						)						):
              self.print_choice(__a						)
              forceWrite("\n"						)
        move_cursor(len(self.choices						) - self.position       , "UP"						)
        with cursor.hide():
              while True:
                    if in_colab:
                          try:
                                _UpperCamelCase   :						Union[str, Any]    =							int(builtins.input()						)
                          except ValueError:
                                _UpperCamelCase   :						int    =							default_choice
                    else:
                          _UpperCamelCase   :						Any    =							self.handle_input()
                    if choice is not None:
                          reset_cursor()
                          for _ in range(len(self.choices						) + 1						):
                                move_cursor(1       , "UP"						)
                                clear_line()
                          self.write_choice(__a       , "\n"						)
                          return choice
