






"""simple docstring"""
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def 							snake_case (  A__      ,A__=1		):
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split("."		)[n_shave_prefix_segments:]		)
			else:
						return ".".join(path.split("."		)[:n_shave_prefix_segments]		)

def 							snake_case (  A__      ,A__=0		):
			UpperCAmelCase_	:					List[Any]     	=   []
			for old_item in old_list:
						UpperCAmelCase_	:					str     	=   old_item.replace("in_layers.0"      ,"norm1"		)
						UpperCAmelCase_	:					Optional[int]     	=   new_item.replace("in_layers.2"      ,"conv1"		)

						UpperCAmelCase_	:					int     	=   new_item.replace("out_layers.0"      ,"norm2"		)
						UpperCAmelCase_	:					Optional[Any]     	=   new_item.replace("out_layers.3"      ,"conv2"		)

						UpperCAmelCase_	:					Dict     	=   new_item.replace("emb_layers.1"      ,"time_emb_proj"		)
						UpperCAmelCase_	:					List[str]     	=   new_item.replace("skip_connection"      ,"conv_shortcut"		)

						UpperCAmelCase_	:					Dict     	=   shave_segments(A__      ,n_shave_prefix_segments=A__		)

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

			return mapping

def 							snake_case (  A__      ,A__=0		):
			UpperCAmelCase_	:					int     	=   []
			for old_item in old_list:
						UpperCAmelCase_	:					List[str]     	=   old_item

						UpperCAmelCase_	:					Union[str, Any]     	=   new_item.replace("norm.weight"      ,"group_norm.weight"		)
						UpperCAmelCase_	:					Optional[int]     	=   new_item.replace("norm.bias"      ,"group_norm.bias"		)

						UpperCAmelCase_	:					Union[str, Any]     	=   new_item.replace("proj_out.weight"      ,"proj_attn.weight"		)
						UpperCAmelCase_	:					List[Any]     	=   new_item.replace("proj_out.bias"      ,"proj_attn.bias"		)

						UpperCAmelCase_	:					Tuple     	=   shave_segments(A__      ,n_shave_prefix_segments=A__		)

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

			return mapping

def 							snake_case (  A__      ,A__      ,A__      ,A__=None      ,A__=None      ,A__=None		):
			assert isinstance(A__      ,A__		), "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():
									UpperCAmelCase_	:					Union[str, Any]     	=   old_checkpoint[path]
									UpperCAmelCase_	:					Tuple     	=   old_tensor.shape[0] // 3

									UpperCAmelCase_	:					Optional[int]     	=   (-1, channels) if len(old_tensor.shape		) == 3 else (-1)

									UpperCAmelCase_	:					Union[str, Any]     	=   old_tensor.shape[0] // config["num_head_channels"] // 3

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

									UpperCAmelCase_	:					List[Any]     	=   query.reshape(A__		)
									UpperCAmelCase_	:					int     	=   key.reshape(A__		)
									UpperCAmelCase_	:					Union[str, Any]     	=   value.reshape(A__		)

			for path in paths:
						UpperCAmelCase_	:					Any     	=   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
						UpperCAmelCase_	:					int     	=   new_path.replace("middle_block.0"      ,"mid_block.resnets.0"		)
						UpperCAmelCase_	:					Tuple     	=   new_path.replace("middle_block.1"      ,"mid_block.attentions.0"		)
						UpperCAmelCase_	:					Tuple     	=   new_path.replace("middle_block.2"      ,"mid_block.resnets.1"		)

						if additional_replacements is not None:
									for replacement in additional_replacements:
												UpperCAmelCase_	:					List[str]     	=   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:
									UpperCAmelCase_	:					Tuple     	=   old_checkpoint[path["old"]][:, :, 0]
						else:
									UpperCAmelCase_	:					Union[str, Any]     	=   old_checkpoint[path["old"]]

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					str     	=   {}

			UpperCAmelCase_	:					List[Any]     	=   checkpoint["time_embed.0.weight"]
			UpperCAmelCase_	:					Optional[Any]     	=   checkpoint["time_embed.0.bias"]
			UpperCAmelCase_	:					Optional[int]     	=   checkpoint["time_embed.2.weight"]
			UpperCAmelCase_	:					Optional[int]     	=   checkpoint["time_embed.2.bias"]

			UpperCAmelCase_	:					List[str]     	=   checkpoint["input_blocks.0.0.weight"]
			UpperCAmelCase_	:					Dict     	=   checkpoint["input_blocks.0.0.bias"]

			UpperCAmelCase_	:					List[Any]     	=   checkpoint["out.0.weight"]
			UpperCAmelCase_	:					List[Any]     	=   checkpoint["out.0.bias"]
			UpperCAmelCase_	:					List[Any]     	=   checkpoint["out.2.weight"]
			UpperCAmelCase_	:					Optional[Any]     	=   checkpoint["out.2.bias"]

			# Retrieves the keys for the input blocks only
			UpperCAmelCase_	:					Dict     	=   len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "input_blocks" in layer}		)
			UpperCAmelCase_	:					List[str]     	=   {
			    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
			    for layer_id in range(A__		)
			}

			# Retrieves the keys for the middle blocks only
			UpperCAmelCase_	:					str     	=   len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "middle_block" in layer}		)
			UpperCAmelCase_	:					Dict     	=   {
			    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
			    for layer_id in range(A__		)
			}

			# Retrieves the keys for the output blocks only
			UpperCAmelCase_	:					List[str]     	=   len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "output_blocks" in layer}		)
			UpperCAmelCase_	:					Optional[Any]     	=   {
			    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
			    for layer_id in range(A__		)
			}

			for i in range(1      ,A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   (i - 1) // (config["num_res_blocks"] + 1)
						UpperCAmelCase_	:					Dict     	=   (i - 1) % (config["num_res_blocks"] + 1)

						UpperCAmelCase_	:					Union[str, Any]     	=   [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
						UpperCAmelCase_	:					Optional[int]     	=   [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:
									UpperCAmelCase_	:					List[str]     	=   checkpoint[
									    F"""input_blocks.{i}.0.op.weight"""
									]
									UpperCAmelCase_	:					List[Any]     	=   checkpoint[
									    F"""input_blocks.{i}.0.op.bias"""
									]
									continue

						UpperCAmelCase_	:					Any     	=   renew_resnet_paths(A__		)
						UpperCAmelCase_	:					Tuple     	=   {"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						UpperCAmelCase_	:					Tuple     	=   {"old": "resnets.2.op", "new": "downsamplers.0.op"}
						assign_to_checkpoint(
						    A__      ,A__      ,A__      ,additional_replacements=[meta_path, resnet_op]      ,config=A__		)

						if len(A__		):
									UpperCAmelCase_	:					Union[str, Any]     	=   renew_attention_paths(A__		)
									UpperCAmelCase_	:					List[Any]     	=   {
									    "old": F"""input_blocks.{i}.1""",
									    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
									}
									UpperCAmelCase_	:					Union[str, Any]     	=   {
									    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(
									    A__      ,A__      ,A__      ,additional_replacements=[meta_path]      ,attention_paths_to_split=A__      ,config=A__      ,)

			UpperCAmelCase_	:					Dict     	=   middle_blocks[0]
			UpperCAmelCase_	:					List[str]     	=   middle_blocks[1]
			UpperCAmelCase_	:					List[Any]     	=   middle_blocks[2]

			UpperCAmelCase_	:					Dict     	=   renew_resnet_paths(A__		)
			assign_to_checkpoint(A__      ,A__      ,A__      ,config=A__		)

			UpperCAmelCase_	:					List[str]     	=   renew_resnet_paths(A__		)
			assign_to_checkpoint(A__      ,A__      ,A__      ,config=A__		)

			UpperCAmelCase_	:					Union[str, Any]     	=   renew_attention_paths(A__		)
			UpperCAmelCase_	:					Optional[int]     	=   {
			    "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(
			    A__      ,A__      ,A__      ,attention_paths_to_split=A__      ,config=A__		)

			for i in range(A__		):
						UpperCAmelCase_	:					Optional[Any]     	=   i // (config["num_res_blocks"] + 1)
						UpperCAmelCase_	:					Optional[Any]     	=   i % (config["num_res_blocks"] + 1)
						UpperCAmelCase_	:					Optional[int]     	=   [shave_segments(A__      ,2		) for name in output_blocks[i]]
						UpperCAmelCase_	:					Union[str, Any]     	=   {}

						for layer in output_block_layers:
									UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   layer.split("."		)[0], shave_segments(A__      ,1		)
									if layer_id in output_block_list:
												output_block_list[layer_id].append(A__		)
									else:
												UpperCAmelCase_	:					Dict     	=   [layer_name]

						if len(A__		) > 1:
									UpperCAmelCase_	:					Optional[Any]     	=   [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
									UpperCAmelCase_	:					str     	=   [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

									UpperCAmelCase_	:					List[Any]     	=   renew_resnet_paths(A__		)
									UpperCAmelCase_	:					List[Any]     	=   renew_resnet_paths(A__		)

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

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												UpperCAmelCase_	:					Union[str, Any]     	=   list(output_block_list.values()		).index(["conv.weight", "conv.bias"]		)
												UpperCAmelCase_	:					str     	=   checkpoint[
												    F"""output_blocks.{i}.{index}.conv.weight"""
												]
												UpperCAmelCase_	:					List[str]     	=   checkpoint[
												    F"""output_blocks.{i}.{index}.conv.bias"""
												]

												# Clear attentions as they have been attributed above.
												if len(A__		) == 2:
															UpperCAmelCase_	:					Dict     	=   []

									if len(A__		):
												UpperCAmelCase_	:					Any     	=   renew_attention_paths(A__		)
												UpperCAmelCase_	:					str     	=   {
												    "old": F"""output_blocks.{i}.1""",
												    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												UpperCAmelCase_	:					str     	=   {
												    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(
												    A__      ,A__      ,A__      ,additional_replacements=[meta_path]      ,attention_paths_to_split=to_split if any("qkv" in key for key in attentions		) else None      ,config=A__      ,)
						else:
									UpperCAmelCase_	:					Optional[Any]     	=   renew_resnet_paths(A__      ,n_shave_prefix_segments=1		)
									for path in resnet_0_paths:
												UpperCAmelCase_	:					Any     	=   ".".join(["output_blocks", str(A__		), path["old"]]		)
												UpperCAmelCase_	:					Union[str, Any]     	=   ".".join(["up_blocks", str(A__		), "resnets", str(A__		), path["new"]]		)

												UpperCAmelCase_	:					Optional[int]     	=   checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
			lowerCamelCase_							      = 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.''')

			lowerCamelCase_							      = parser.parse_args()

			lowerCamelCase_							      = torch.load(args.checkpoint_path)

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

			lowerCamelCase_							      = convert_ldm_checkpoint(checkpoint, config)

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

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

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

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











"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



