



import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


lowerCAmelCase__					  =  logging.get_logger(__name__)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				def constraint_to_multiple_of(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=0					,			lowerCamelCase__=None						):
								lowercase__					:   Tuple													=					round(val / multiple						) * multiple

								if max_val is not None and x > max_val:
												lowercase__					:   int													=					math.floor(val / multiple						) * multiple

								if x < min_val:
												lowercase__					:   Tuple													=					math.ceil(val / multiple						) * multiple

								return x

				lowercase__					:   Any													=					(output_size, output_size) if isinstance(lowerCamelCase__					,			lowerCamelCase__						) else output_size

				lowercase__							,							lowercase__					:   Optional[int]													=					get_image_size(lowerCamelCase__						)
				lowercase__							,							lowercase__					:   str													=					output_size

				# determine new height and width
				lowercase__					:   Tuple													=					output_height / input_height
				lowercase__					:   Dict													=					output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width						) < abs(1 - scale_height						):
												# fit width
												lowercase__					:   Tuple													=					scale_width
								else:
												# fit height
												lowercase__					:   Tuple													=					scale_height

				lowercase__					:   str													=					constraint_to_multiple_of(scale_height * input_height					,			multiple=lowerCamelCase__						)
				lowercase__					:   Union[str, Any]													=					constraint_to_multiple_of(scale_width * input_width					,			multiple=lowerCamelCase__						)

				return (new_height, new_width)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		["""pixel_values"""]







					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BILINEAR				,       SCREAMING_SNAKE_CASE :     bool = False				,       SCREAMING_SNAKE_CASE :     int = 1				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Union[int, float] = 1 / 255				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       **SCREAMING_SNAKE_CASE :     Union[str, Any]				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Any													=					size if size is not None else {"height": 384, "width": 384}
									lowercase__					:   Optional[int]													=					get_size_dict(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					do_resize
									lowercase__					:   List[Any]													=					size
									lowercase__					:   Optional[Any]													=					keep_aspect_ratio
									lowercase__					:   List[Any]													=					ensure_multiple_of
									lowercase__					:   Dict													=					resample
									lowercase__					:   int													=					do_rescale
									lowercase__					:   Any													=					rescale_factor
									lowercase__					:   List[Any]													=					do_normalize
									lowercase__					:   Tuple													=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase__					:   int													=					image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Dict[str, int]				,       SCREAMING_SNAKE_CASE :     bool = False				,       SCREAMING_SNAKE_CASE :     int = 1				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BICUBIC				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     List[Any]				,       ):
									lowercase__					:   Optional[int]													=					get_size_dict(SCREAMING_SNAKE_CASE					)
									if "height" not in size or "width" not in size:
													raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""					)
									lowercase__					:   str													=					get_resize_output_image_size(
									    SCREAMING_SNAKE_CASE				,       output_size=(size["height"], size["width"])				,       keep_aspect_ratio=SCREAMING_SNAKE_CASE				,       multiple=SCREAMING_SNAKE_CASE				,       )
									return resize(SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE				,       resample=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[int, float]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     List[str]				,       ):
									return rescale(SCREAMING_SNAKE_CASE				,       scale=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     Any				,       ):
									return normalize(SCREAMING_SNAKE_CASE				,       mean=SCREAMING_SNAKE_CASE				,       std=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     ImageInput				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     int = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     int = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     float = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       SCREAMING_SNAKE_CASE :     ChannelDimension = ChannelDimension.FIRST				,       **SCREAMING_SNAKE_CASE :     str				,       ):
									lowercase__					:   Any													=					do_resize if do_resize is not None else self.do_resize
									lowercase__					:   Dict													=					size if size is not None else self.size
									lowercase__					:   str													=					get_size_dict(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									lowercase__					:   int													=					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									lowercase__					:   str													=					resample if resample is not None else self.resample
									lowercase__					:   Union[str, Any]													=					do_rescale if do_rescale is not None else self.do_rescale
									lowercase__					:   Optional[Any]													=					rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__					:   Union[str, Any]													=					do_normalize if do_normalize is not None else self.do_normalize
									lowercase__					:   Union[str, Any]													=					image_mean if image_mean is not None else self.image_mean
									lowercase__					:   List[str]													=					image_std if image_std is not None else self.image_std

									lowercase__					:   List[Any]													=					make_list_of_images(SCREAMING_SNAKE_CASE					)

									if not valid_images(SCREAMING_SNAKE_CASE					):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."					)

									if do_resize and size is None or resample is None:
													raise ValueError("Size and resample must be specified if do_resize is True."					)

									if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."					)

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."					)

									# All transformations expect numpy arrays.
									lowercase__					:   Optional[Any]													=					[to_numpy_array(SCREAMING_SNAKE_CASE					) for image in images]

									if do_resize:
													lowercase__					:   str													=					[self.resize(image=SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE				,       resample=SCREAMING_SNAKE_CASE					) for image in images]

									if do_rescale:
													lowercase__					:   List[str]													=					[self.rescale(image=SCREAMING_SNAKE_CASE				,       scale=SCREAMING_SNAKE_CASE					) for image in images]

									if do_normalize:
													lowercase__					:   Optional[int]													=					[self.normalize(image=SCREAMING_SNAKE_CASE				,       mean=SCREAMING_SNAKE_CASE				,       std=SCREAMING_SNAKE_CASE					) for image in images]

									lowercase__					:   Any													=					[to_channel_dimension_format(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) for image in images]

									lowercase__					:   Dict													=					{"pixel_values": images}
									return BatchFeature(data=SCREAMING_SNAKE_CASE				,       tensor_type=SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     List[Tuple] = None					):
									lowercase__					:   Any													=					outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
													if len(SCREAMING_SNAKE_CASE					) != len(SCREAMING_SNAKE_CASE					):
																	raise ValueError(
																	    "Make sure that you pass in as many target sizes as the batch dimension of the logits"					)

													if is_torch_tensor(SCREAMING_SNAKE_CASE					):
																	lowercase__					:   Union[str, Any]													=					target_sizes.numpy()

													lowercase__					:   int													=					[]

													for idx in range(len(SCREAMING_SNAKE_CASE					)					):
																	lowercase__					:   Optional[int]													=					torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0					)				,       size=target_sizes[idx]				,       mode="bilinear"				,       align_corners=SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Dict													=					resized_logits[0].argmax(dim=0					)
																	semantic_segmentation.append(SCREAMING_SNAKE_CASE					)
									else:
													lowercase__					:   List[Any]													=					logits.argmax(dim=1					)
													lowercase__					:   Optional[Any]													=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

									return semantic_segmentation









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__						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								lowercase__					:   str													=					sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																lowercase__					:   List[str]													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
												lowercase__					:   str													=					v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												lowercase__					:   int													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
								lowercase__					:   Optional[Any]													=					v
				lowercase__					:   Union[str, 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__						):

				"""simple docstring"""


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


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   str													=					{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__					:   Optional[int]													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
								lowercase__					:   Dict													=					v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																lowercase__					:   List[str]													=					v.replace(lowerCamelCase__					,			lowerCamelCase__						)
												lowercase__					:   int													=					v
				lowercase__					:   Union[str, Any]													=					{v: vae_state_dict[k] for k, v in mapping.items()}
				lowercase__					:   Optional[int]													=					["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__					:   Dict													=					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__						):

				"""simple docstring"""


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

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

				return new_state_dict


def  __lowerCamelCase (       lowerCamelCase__						):

				"""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)




