


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Any=1   ) ->   Optional[Any]:
						"""simple docstring"""







						if n_shave_prefix_segments >= 0:
												return ".".join(path.split("."   )[n_shave_prefix_segments:]   )
						else:
												return ".".join(path.split("."   )[:n_shave_prefix_segments]   )


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Tuple=0   ) ->   List[str]:
						"""simple docstring"""







						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_   )

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

						return mapping


def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				List[str]=0   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						[]
						for old_item in old_list:
												lowerCAmelCase__       =						old_item

												lowerCAmelCase__       =						new_item.replace("norm.weight"    ,   "group_norm.weight"   )
												lowerCAmelCase__       =						new_item.replace("norm.bias"    ,   "group_norm.bias"   )

												lowerCAmelCase__       =						new_item.replace("proj_out.weight"    ,   "proj_attn.weight"   )
												lowerCAmelCase__       =						new_item.replace("proj_out.bias"    ,   "proj_attn.bias"   )

												lowerCAmelCase__       =						shave_segments(UpperCamelCase_    ,   n_shave_prefix_segments=UpperCamelCase_   )

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

						return mapping


def        _a  (     UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				str=None    ,   UpperCamelCase_					:				str=None    ,   UpperCamelCase_					:				str=None   ) ->   Any:
						"""simple docstring"""







						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

												# Global renaming happens here
												lowerCAmelCase__       =						new_path.replace("middle_block.0"    ,   "mid_block.resnets.0"   )
												lowerCAmelCase__       =						new_path.replace("middle_block.1"    ,   "mid_block.attentions.0"   )
												lowerCAmelCase__       =						new_path.replace("middle_block.2"    ,   "mid_block.resnets.1"   )

												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
												if "proj_attn.weight" in new_path:
																		lowerCAmelCase__       =						old_checkpoint[path["old"]][:, :, 0]
												else:
																		lowerCAmelCase__       =						old_checkpoint[path["old"]]


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Tuple   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						{}

						lowerCAmelCase__       =						checkpoint["time_embed.0.weight"]
						lowerCAmelCase__       =						checkpoint["time_embed.0.bias"]
						lowerCAmelCase__       =						checkpoint["time_embed.2.weight"]
						lowerCAmelCase__       =						checkpoint["time_embed.2.bias"]

						lowerCAmelCase__       =						checkpoint["input_blocks.0.0.weight"]
						lowerCAmelCase__       =						checkpoint["input_blocks.0.0.bias"]

						lowerCAmelCase__       =						checkpoint["out.0.weight"]
						lowerCAmelCase__       =						checkpoint["out.0.bias"]
						lowerCAmelCase__       =						checkpoint["out.2.weight"]
						lowerCAmelCase__       =						checkpoint["out.2.bias"]

						# Retrieves the keys for the input blocks only
						lowerCAmelCase__       =						len({".".join(layer.split("."   )[:2]   ) for layer in checkpoint if "input_blocks" in layer}   )
						lowerCAmelCase__       =						{
						    layer_id: [key for key in checkpoint 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 checkpoint if "middle_block" in layer}   )
						lowerCAmelCase__       =						{
						    layer_id: [key for key in checkpoint 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 checkpoint if "output_blocks" in layer}   )
						lowerCAmelCase__       =						{
						    layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
						    for layer_id in range(UpperCamelCase_   )
						}

						for i in range(1    ,   UpperCamelCase_   ):
												lowerCAmelCase__       =						(i - 1) // (config["num_res_blocks"] + 1)
												lowerCAmelCase__       =						(i - 1) % (config["num_res_blocks"] + 1)

												lowerCAmelCase__       =						[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
												lowerCAmelCase__       =						[key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

												if F"input_blocks.{i}.0.op.weight" in checkpoint:
																		lowerCAmelCase__       =						checkpoint[
																		    F"input_blocks.{i}.0.op.weight"
																		]
																		lowerCAmelCase__       =						checkpoint[
																		    F"input_blocks.{i}.0.op.bias"
																		]
																		continue

												lowerCAmelCase__       =						renew_resnet_paths(UpperCamelCase_   )
												lowerCAmelCase__       =						{"old": F"input_blocks.{i}.0", "new": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
												lowerCAmelCase__       =						{"old": "resnets.2.op", "new": "downsamplers.0.op"}
												assign_to_checkpoint(
												    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   additional_replacements=[meta_path, resnet_op]    ,   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}",
																		}
																		lowerCAmelCase__       =						{
																		    F"input_blocks.{i}.1.qkv.bias": {
																		        "key": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
																		        "query": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
																		        "value": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
																		    },
																		    F"input_blocks.{i}.1.qkv.weight": {
																		        "key": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
																		        "query": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
																		        "value": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
																		    },
																		}
																		assign_to_checkpoint(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   additional_replacements=[meta_path]    ,   attention_paths_to_split=UpperCamelCase_    ,   config=UpperCamelCase_    ,   )

						lowerCAmelCase__       =						middle_blocks[0]
						lowerCAmelCase__       =						middle_blocks[1]
						lowerCAmelCase__       =						middle_blocks[2]

						lowerCAmelCase__       =						renew_resnet_paths(UpperCamelCase_   )
						assign_to_checkpoint(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   config=UpperCamelCase_   )

						lowerCAmelCase__       =						renew_resnet_paths(UpperCamelCase_   )
						assign_to_checkpoint(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   config=UpperCamelCase_   )

						lowerCAmelCase__       =						renew_attention_paths(UpperCamelCase_   )
						lowerCAmelCase__       =						{
						    "middle_block.1.qkv.bias": {
						        "key": "mid_block.attentions.0.key.bias",
						        "query": "mid_block.attentions.0.query.bias",
						        "value": "mid_block.attentions.0.value.bias",
						    },
						    "middle_block.1.qkv.weight": {
						        "key": "mid_block.attentions.0.key.weight",
						        "query": "mid_block.attentions.0.query.weight",
						        "value": "mid_block.attentions.0.value.weight",
						    },
						}
						assign_to_checkpoint(
						    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   attention_paths_to_split=UpperCamelCase_    ,   config=UpperCamelCase_   )

						for i in range(UpperCamelCase_   ):
												lowerCAmelCase__       =						i // (config["num_res_blocks"] + 1)
												lowerCAmelCase__       =						i % (config["num_res_blocks"] + 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__       =						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_   )

																		if ["conv.weight", "conv.bias"] in output_block_list.values():
																								lowerCAmelCase__       =						list(output_block_list.values()   ).index(["conv.weight", "conv.bias"]   )
																								lowerCAmelCase__       =						checkpoint[
																								    F"output_blocks.{i}.{index}.conv.weight"
																								]
																								lowerCAmelCase__       =						checkpoint[
																								    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}",
																								}
																								lowerCAmelCase__       =						{
																								    F"output_blocks.{i}.1.qkv.bias": {
																								        "key": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
																								        "query": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
																								        "value": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
																								    },
																								    F"output_blocks.{i}.1.qkv.weight": {
																								        "key": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
																								        "query": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
																								        "value": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
																								    },
																								}
																								assign_to_checkpoint(
																								    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   additional_replacements=[meta_path]    ,   attention_paths_to_split=to_split if any("qkv" in key for key in attentions   ) else None    ,   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__       =						checkpoint[old_path]

						return new_checkpoint


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
	)

	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help='''The config json file corresponding to the architecture.''',
	)

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	a_							=    parser.parse_args()

	a_							=    torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		a_							=    json.loads(f.read())

	a_							=    convert_ldm_checkpoint(checkpoint, config)

	if "ldm" in config:
		del config["ldm"]

	a_							=    UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		a_							=    DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
		a_							=    VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

		a_							=    LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)














# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)






