





import argparse

import torch

from diffusers import UNetaDConditionModel





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=None	,					_UpperCAmelCase=None	,					_UpperCAmelCase=None )  ->     Union[str, Any]:
				assert isinstance(_UpperCAmelCase	,					_UpperCAmelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												lowerCamelCase		       =old_checkpoint[path]
												lowerCamelCase		       =old_tensor.shape[0] // 3

												lowerCamelCase		       =(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

												lowerCamelCase		       =old_tensor.shape[0] // config["""num_head_channels"""] // 3

												lowerCamelCase		       =old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
												lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase		       =old_tensor.split(channels // num_heads	,					dim=1 )

												lowerCamelCase		       =query.reshape(_UpperCAmelCase )
												lowerCamelCase		       =key.reshape(_UpperCAmelCase )
												lowerCamelCase		       =value.reshape(_UpperCAmelCase )

				for path in paths:
								lowerCamelCase		       =path["""new"""]

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowerCamelCase		       =new_path.replace(replacement["""old"""]	,					replacement["""new"""] )

        # proj_attn.weight has to be converted from conv 1D to linear
								lowerCamelCase		       =old_checkpoint[path["""old"""]]
								lowerCamelCase		       =["""proj_attn.weight"""]
								lowerCamelCase		       =["""proj_out.weight""", """proj_in.weight"""]
								if any(k in new_path for k in names ):
												lowerCamelCase		       =weight[:, :, 0]
								elif any(k in new_path for k in names_a ) and len(weight.shape ) > 2 and ".attentions." not in new_path:
												lowerCamelCase		       =weight[:, :, 0]
								else:
												lowerCamelCase		       =weight





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     List[Any]:
				lowerCamelCase		       =[]
				for old_item in old_list:
								lowerCamelCase		       =old_item

								#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
								#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

								#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
								#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

								#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

								mapping.append({"""old""": old_item, """new""": new_item} )

				return mapping





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=1 )  ->     Any:
				if n_shave_prefix_segments >= 0:
								return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
				else:
								return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     int:
				lowerCamelCase		       =[]
				for old_item in old_list:
								mapping.append({"""old""": old_item, """new""": old_item} )

				return mapping





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     Optional[int]:
				lowerCamelCase		       =[]
				for old_item in old_list:
								lowerCamelCase		       =old_item.replace("""in_layers.0"""	,					"""norm1""" )
								lowerCamelCase		       =new_item.replace("""in_layers.2"""	,					"""conv1""" )

								lowerCamelCase		       =new_item.replace("""out_layers.0"""	,					"""norm2""" )
								lowerCamelCase		       =new_item.replace("""out_layers.3"""	,					"""conv2""" )

								lowerCamelCase		       =new_item.replace("""emb_layers.1"""	,					"""time_emb_proj""" )
								lowerCamelCase		       =new_item.replace("""skip_connection"""	,					"""conv_shortcut""" )

								lowerCamelCase		       =shave_segments(_UpperCAmelCase	,					n_shave_prefix_segments=_UpperCAmelCase )

								if "temopral_conv" not in old_item:
												mapping.append({"""old""": old_item, """new""": new_item} )

				return mapping





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=None	,					_UpperCAmelCase=False )  ->     int:
				lowerCamelCase		       ={}
				lowerCamelCase		       =list(checkpoint.keys() )

				lowerCamelCase		       ="""model.diffusion_model."""

				# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
				if sum(k.startswith("""model_ema""" ) for k in keys ) > 1_00 and extract_ema:
								print(F"""Checkpoint {path} has both EMA and non-EMA weights.""" )
								print(
								    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
								    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.""" )
								for key in keys:
												if key.startswith("""model.diffusion_model""" ):
																lowerCamelCase		       ="""model_ema.""" + """""".join(key.split(""".""" )[1:] )
																lowerCamelCase		       =checkpoint.pop(_UpperCAmelCase )
				else:
								if sum(k.startswith("""model_ema""" ) for k in keys ) > 1_00:
												print(
												    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
												    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag.""" )

								for key in keys:
												lowerCamelCase		       =checkpoint.pop(_UpperCAmelCase )

				lowerCamelCase		       ={}

				lowerCamelCase		       =unet_state_dict["""time_embed.0.weight"""]
				lowerCamelCase		       =unet_state_dict["""time_embed.0.bias"""]
				lowerCamelCase		       =unet_state_dict["""time_embed.2.weight"""]
				lowerCamelCase		       =unet_state_dict["""time_embed.2.bias"""]

				if config["class_embed_type"] is None:
								# No parameters to port
								...
				elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
								lowerCamelCase		       =unet_state_dict["""label_emb.0.0.weight"""]
								lowerCamelCase		       =unet_state_dict["""label_emb.0.0.bias"""]
								lowerCamelCase		       =unet_state_dict["""label_emb.0.2.weight"""]
								lowerCamelCase		       =unet_state_dict["""label_emb.0.2.bias"""]
				else:
								raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}""" )

				lowerCamelCase		       =unet_state_dict["""input_blocks.0.0.weight"""]
				lowerCamelCase		       =unet_state_dict["""input_blocks.0.0.bias"""]

				lowerCamelCase		       =[v for v in unet_state_dict if v.startswith("""input_blocks.0.1""" )]
				lowerCamelCase		       =renew_attention_paths(_UpperCAmelCase )
				lowerCamelCase		       ={"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
				assign_to_checkpoint(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

				lowerCamelCase		       =unet_state_dict["""out.0.weight"""]
				lowerCamelCase		       =unet_state_dict["""out.0.bias"""]
				lowerCamelCase		       =unet_state_dict["""out.2.weight"""]
				lowerCamelCase		       =unet_state_dict["""out.2.bias"""]

				# Retrieves the keys for the input blocks only
				lowerCamelCase		       =len({""".""".join(layer.split(""".""" )[:2] ) for layer in unet_state_dict if """input_blocks""" in layer} )
				lowerCamelCase		       ={
				    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
				    for layer_id in range(_UpperCAmelCase )
				}

				# Retrieves the keys for the middle blocks only
				lowerCamelCase		       =len({""".""".join(layer.split(""".""" )[:2] ) for layer in unet_state_dict if """middle_block""" in layer} )
				lowerCamelCase		       ={
				    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
				    for layer_id in range(_UpperCAmelCase )
				}

				# Retrieves the keys for the output blocks only
				lowerCamelCase		       =len({""".""".join(layer.split(""".""" )[:2] ) for layer in unet_state_dict if """output_blocks""" in layer} )
				lowerCamelCase		       ={
				    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
				    for layer_id in range(_UpperCAmelCase )
				}

				for i in range(1	,					_UpperCAmelCase ):
								lowerCamelCase		       =(i - 1) // (config["""layers_per_block"""] + 1)
								lowerCamelCase		       =(i - 1) % (config["""layers_per_block"""] + 1)

								lowerCamelCase		       =[
								    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
								]
								lowerCamelCase		       =[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
								lowerCamelCase		       =[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

								if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
												lowerCamelCase		       =unet_state_dict.pop(
												    F"""input_blocks.{i}.op.weight""" )
												lowerCamelCase		       =unet_state_dict.pop(
												    F"""input_blocks.{i}.op.bias""" )

								lowerCamelCase		       =renew_resnet_paths(_UpperCAmelCase )
								lowerCamelCase		       ={"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								assign_to_checkpoint(
								    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

								lowerCamelCase		       =[key for key in resnets if """temopral_conv""" in key]
								lowerCamelCase		       =renew_temp_conv_paths(_UpperCAmelCase )
								lowerCamelCase		       ={
								    """old""": F"""input_blocks.{i}.0.temopral_conv""",
								    """new""": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
								}
								assign_to_checkpoint(
								    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

								if len(_UpperCAmelCase ):
												lowerCamelCase		       =renew_attention_paths(_UpperCAmelCase )
												lowerCamelCase		       ={"""old""": F"""input_blocks.{i}.1""", """new""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

								if len(_UpperCAmelCase ):
												lowerCamelCase		       =renew_attention_paths(_UpperCAmelCase )
												lowerCamelCase		       ={
												    """old""": F"""input_blocks.{i}.2""",
												    """new""": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
												}
												assign_to_checkpoint(
												    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

				lowerCamelCase		       =middle_blocks[0]
				lowerCamelCase		       =[key for key in resnet_a if """temopral_conv""" in key]
				lowerCamelCase		       =middle_blocks[1]
				lowerCamelCase		       =middle_blocks[2]
				lowerCamelCase		       =middle_blocks[3]
				lowerCamelCase		       =[key for key in resnet_a if """temopral_conv""" in key]

				lowerCamelCase		       =renew_resnet_paths(_UpperCAmelCase )
				lowerCamelCase		       ={"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
				assign_to_checkpoint(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					config=_UpperCAmelCase	,					additional_replacements=[meta_path] )

				lowerCamelCase		       =renew_temp_conv_paths(_UpperCAmelCase )
				lowerCamelCase		       ={"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
				assign_to_checkpoint(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					config=_UpperCAmelCase	,					additional_replacements=[meta_path] )

				lowerCamelCase		       =renew_resnet_paths(_UpperCAmelCase )
				lowerCamelCase		       ={"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
				assign_to_checkpoint(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					config=_UpperCAmelCase	,					additional_replacements=[meta_path] )

				lowerCamelCase		       =renew_temp_conv_paths(_UpperCAmelCase )
				lowerCamelCase		       ={"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
				assign_to_checkpoint(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					config=_UpperCAmelCase	,					additional_replacements=[meta_path] )

				lowerCamelCase		       =renew_attention_paths(_UpperCAmelCase )
				lowerCamelCase		       ={"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
				assign_to_checkpoint(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

				lowerCamelCase		       =renew_attention_paths(_UpperCAmelCase )
				lowerCamelCase		       ={"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
				assign_to_checkpoint(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

				for i in range(_UpperCAmelCase ):
								lowerCamelCase		       =i // (config["""layers_per_block"""] + 1)
								lowerCamelCase		       =i % (config["""layers_per_block"""] + 1)
								lowerCamelCase		       =[shave_segments(_UpperCAmelCase	,					2 ) for name in output_blocks[i]]
								lowerCamelCase		       ={}

								for layer in output_block_layers:
												lowerCamelCase       ,     lowerCamelCase		       =layer.split(""".""" )[0], shave_segments(_UpperCAmelCase	,					1 )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(_UpperCAmelCase )
												else:
																lowerCamelCase		       =[layer_name]

								if len(_UpperCAmelCase ) > 1:
												lowerCamelCase		       =[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												lowerCamelCase		       =[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
												lowerCamelCase		       =[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

												lowerCamelCase		       =renew_resnet_paths(_UpperCAmelCase )
												lowerCamelCase		       =renew_resnet_paths(_UpperCAmelCase )

												lowerCamelCase		       ={"""old""": F"""output_blocks.{i}.0""", """new""": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

												lowerCamelCase		       =[key for key in resnets if """temopral_conv""" in key]
												lowerCamelCase		       =renew_temp_conv_paths(_UpperCAmelCase )
												lowerCamelCase		       ={
												    """old""": F"""output_blocks.{i}.0.temopral_conv""",
												    """new""": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
												}
												assign_to_checkpoint(
												    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

												lowerCamelCase		       ={k: sorted(_UpperCAmelCase ) for k, v in output_block_list.items()}
												if ["conv.bias", "conv.weight"] in output_block_list.values():
																lowerCamelCase		       =list(output_block_list.values() ).index(["""conv.bias""", """conv.weight"""] )
																lowerCamelCase		       =unet_state_dict[
																    F"""output_blocks.{i}.{index}.conv.weight"""
																]
																lowerCamelCase		       =unet_state_dict[
																    F"""output_blocks.{i}.{index}.conv.bias"""
																]

																# Clear attentions as they have been attributed above.
																if len(_UpperCAmelCase ) == 2:
																				lowerCamelCase		       =[]

												if len(_UpperCAmelCase ):
																lowerCamelCase		       =renew_attention_paths(_UpperCAmelCase )
																lowerCamelCase		       ={
																    """old""": F"""output_blocks.{i}.1""",
																    """new""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																assign_to_checkpoint(
																    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )

												if len(_UpperCAmelCase ):
																lowerCamelCase		       =renew_attention_paths(_UpperCAmelCase )
																lowerCamelCase		       ={
																    """old""": F"""output_blocks.{i}.2""",
																    """new""": F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																}
																assign_to_checkpoint(
																    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					additional_replacements=[meta_path]	,					config=_UpperCAmelCase )
								else:
												lowerCamelCase		       =renew_resnet_paths(_UpperCAmelCase	,					n_shave_prefix_segments=1 )
												for path in resnet_0_paths:
																lowerCamelCase		       =""".""".join(["""output_blocks""", str(_UpperCAmelCase ), path["""old"""]] )
																lowerCamelCase		       =""".""".join(["""up_blocks""", str(_UpperCAmelCase ), """resnets""", str(_UpperCAmelCase ), path["""new"""]] )
																lowerCamelCase		       =unet_state_dict[old_path]

												lowerCamelCase		       =[l for l in output_block_layers if """temopral_conv""" in l]
												for path in temopral_conv_paths:
																lowerCamelCase		       =path.split("""temopral_conv.""" )[-1]
																lowerCamelCase		       =""".""".join(["""output_blocks""", str(_UpperCAmelCase ), str(_UpperCAmelCase ), """temopral_conv""", pruned_path] )
																lowerCamelCase		       =""".""".join(["""up_blocks""", str(_UpperCAmelCase ), """temp_convs""", str(_UpperCAmelCase ), pruned_path] )
																lowerCamelCase		       =unet_state_dict[old_path]

				return new_checkpoint


if __name__ == "__main__":
							UpperCAmelCase__  :	Tuple     =argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
							)
							parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
							UpperCAmelCase__  :	Any     =parser.parse_args()

							UpperCAmelCase__  :	Union[str, Any]     =torch.load(args.checkpoint_path, map_location='''cpu''')
							UpperCAmelCase__  :	int     =UNetaDConditionModel()

							UpperCAmelCase__  :	Tuple     =convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

							UpperCAmelCase__  :	int     =set(unet.state_dict().keys()) - set(converted_ckpt.keys())
							UpperCAmelCase__  :	Union[str, Any]     =set(converted_ckpt.keys()) - set(unet.state_dict().keys())

							assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

							# load state_dict
							unet.load_state_dict(converted_ckpt)

							unet.save_pretrained(args.dump_path)

							# -- finish converting the unet --












from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available

@dataclass
class 					__A   (  a			):
				__A            = 42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
							from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
							from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline





