"""simple docstring"""







import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def      a__					(						UpperCAmelCase__				:	str						,	UpperCAmelCase__				:	List[str]=1	)->		Dict:
						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__				:	Tuple						,	UpperCAmelCase__				:	int=0	)->		Optional[int]:
						UpperCAmelCase__    		=						[]
						for old_item in old_list:
												UpperCAmelCase__    		=						old_item
												UpperCAmelCase__    		=						new_item.replace("""block."""						,	"""resnets."""	)
												UpperCAmelCase__    		=						new_item.replace("""conv_shorcut"""						,	"""conv1"""	)
												UpperCAmelCase__    		=						new_item.replace("""in_shortcut"""						,	"""conv_shortcut"""	)
												UpperCAmelCase__    		=						new_item.replace("""temb_proj"""						,	"""time_emb_proj"""	)

												UpperCAmelCase__    		=						shave_segments(UpperCamelCase__						,	n_shave_prefix_segments=UpperCamelCase__	)

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

						return mapping


def      a__					(						UpperCAmelCase__				:	Union[str, Any]						,	UpperCAmelCase__				:	List[Any]=0						,	UpperCAmelCase__				:	Optional[Any]=False	)->		Any:
						UpperCAmelCase__    		=						[]
						for old_item in old_list:
												UpperCAmelCase__    		=						old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		UpperCAmelCase__    		=						new_item.replace("""attn"""						,	"""attentions"""	)
												UpperCAmelCase__    		=						new_item.replace(""".k."""						,	""".key."""	)
												UpperCAmelCase__    		=						new_item.replace(""".v."""						,	""".value."""	)
												UpperCAmelCase__    		=						new_item.replace(""".q."""						,	""".query."""	)

												UpperCAmelCase__    		=						new_item.replace("""proj_out"""						,	"""proj_attn"""	)
												UpperCAmelCase__    		=						new_item.replace("""norm"""						,	"""group_norm"""	)

												UpperCAmelCase__    		=						shave_segments(UpperCamelCase__						,	n_shave_prefix_segments=UpperCamelCase__	)
												mapping.append({"""old""": old_item, """new""": new_item}	)

						return mapping


def      a__					(						UpperCAmelCase__				:	int						,	UpperCAmelCase__				:	Dict						,	UpperCAmelCase__				:	List[str]						,	UpperCAmelCase__				:	Union[str, Any]=None						,	UpperCAmelCase__				:	List[Any]=None						,	UpperCAmelCase__				:	int=None	)->		List[Any]:
						assert isinstance(UpperCamelCase__						,	UpperCamelCase__	), "Paths should be a list of dicts containing 'old' and 'new' keys."

						if attention_paths_to_split is not None:
												if config is None:
																		raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'."""	)

												for path, path_map in attention_paths_to_split.items():
																		UpperCAmelCase__    		=						old_checkpoint[path]
																		UpperCAmelCase__    		=						old_tensor.shape[0] // 3

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

																		UpperCAmelCase__    		=						old_tensor.shape[0] // config.get("""num_head_channels"""						,	1	) // 3

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

																		UpperCAmelCase__    		=						query.reshape(UpperCamelCase__	).squeeze()
																		UpperCAmelCase__    		=						key.reshape(UpperCamelCase__	).squeeze()
																		UpperCAmelCase__    		=						value.reshape(UpperCamelCase__	).squeeze()

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

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												UpperCAmelCase__    		=						new_path.replace("""down."""						,	"""down_blocks."""	)
												UpperCAmelCase__    		=						new_path.replace("""up."""						,	"""up_blocks."""	)

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

												if "attentions" in new_path:
																		UpperCAmelCase__    		=						old_checkpoint[path["""old"""]].squeeze()
												else:
																		UpperCAmelCase__    		=						old_checkpoint[path["""old"""]]


def      a__					(						UpperCAmelCase__				:	Union[str, Any]						,	UpperCAmelCase__				:	Tuple	)->		int:
						UpperCAmelCase__    		=						{}

						UpperCAmelCase__    		=						checkpoint["""temb.dense.0.weight"""]
						UpperCAmelCase__    		=						checkpoint["""temb.dense.0.bias"""]
						UpperCAmelCase__    		=						checkpoint["""temb.dense.1.weight"""]
						UpperCAmelCase__    		=						checkpoint["""temb.dense.1.bias"""]

						UpperCAmelCase__    		=						checkpoint["""norm_out.weight"""]
						UpperCAmelCase__    		=						checkpoint["""norm_out.bias"""]

						UpperCAmelCase__    		=						checkpoint["""conv_in.weight"""]
						UpperCAmelCase__    		=						checkpoint["""conv_in.bias"""]
						UpperCAmelCase__    		=						checkpoint["""conv_out.weight"""]
						UpperCAmelCase__    		=						checkpoint["""conv_out.bias"""]

						UpperCAmelCase__    		=						len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """down""" in layer}	)
						UpperCAmelCase__    		=						{
						    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)
						}

						UpperCAmelCase__    		=						len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """up""" in layer}	)
						UpperCAmelCase__    		=						{layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)}

						for i in range(UpperCamelCase__	):
												UpperCAmelCase__    		=						(i - 1) // (config["""layers_per_block"""] + 1)

												if any("""downsample""" in layer for layer in down_blocks[i]	):
																		UpperCAmelCase__    		=						checkpoint[
																		    f'down.{i}.downsample.op.weight'
																		]
																		UpperCAmelCase__    		=						checkpoint[f'down.{i}.downsample.op.bias']
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

												if any("""block""" in layer for layer in down_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """block""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
																		    for layer_id in range(UpperCamelCase__	)
																		}

																		if num_blocks > 0:
																								for j in range(config["""layers_per_block"""]	):
																														UpperCAmelCase__    		=						renew_resnet_paths(blocks[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__	)

												if any("""attn""" in layer for layer in down_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """attn""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
																		    for layer_id in range(UpperCamelCase__	)
																		}

																		if num_attn > 0:
																								for j in range(config["""layers_per_block"""]	):
																														UpperCAmelCase__    		=						renew_attention_paths(attns[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	config=UpperCamelCase__	)

						UpperCAmelCase__    		=						[key for key in checkpoint if """mid.block_1""" in key]
						UpperCAmelCase__    		=						[key for key in checkpoint if """mid.block_2""" in key]
						UpperCAmelCase__    		=						[key for key in checkpoint if """mid.attn_1""" in key]

						# Mid new 2
						UpperCAmelCase__    		=						renew_resnet_paths(UpperCamelCase__	)
						assign_to_checkpoint(
						    UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]						,	)

						UpperCAmelCase__    		=						renew_resnet_paths(UpperCamelCase__	)
						assign_to_checkpoint(
						    UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]						,	)

						UpperCAmelCase__    		=						renew_attention_paths(UpperCamelCase__						,	in_mid=UpperCamelCase__	)
						assign_to_checkpoint(
						    UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]						,	)

						for i in range(UpperCamelCase__	):
												UpperCAmelCase__    		=						num_up_blocks - 1 - i

												if any("""upsample""" in layer for layer in up_blocks[i]	):
																		UpperCAmelCase__    		=						checkpoint[
																		    f'up.{i}.upsample.conv.weight'
																		]
																		UpperCAmelCase__    		=						checkpoint[f'up.{i}.upsample.conv.bias']

												if any("""block""" in layer for layer in up_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """block""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)
																		}

																		if num_blocks > 0:
																								for j in range(config["""layers_per_block"""] + 1	):
																														UpperCAmelCase__    		=						{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
																														UpperCAmelCase__    		=						renew_resnet_paths(blocks[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[replace_indices]	)

												if any("""attn""" in layer for layer in up_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """attn""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)
																		}

																		if num_attn > 0:
																								for j in range(config["""layers_per_block"""] + 1	):
																														UpperCAmelCase__    		=						{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
																														UpperCAmelCase__    		=						renew_attention_paths(attns[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[replace_indices]	)

						UpperCAmelCase__    		=						{k.replace("""mid_new_2"""						,	"""mid_block"""	): v for k, v in new_checkpoint.items()}
						return new_checkpoint


def      a__					(						UpperCAmelCase__				:	Any						,	UpperCAmelCase__				:	int	)->		Optional[Any]:
						UpperCAmelCase__    		=						{}

						UpperCAmelCase__    		=						checkpoint["""encoder.norm_out.weight"""]
						UpperCAmelCase__    		=						checkpoint["""encoder.norm_out.bias"""]

						UpperCAmelCase__    		=						checkpoint["""encoder.conv_in.weight"""]
						UpperCAmelCase__    		=						checkpoint["""encoder.conv_in.bias"""]
						UpperCAmelCase__    		=						checkpoint["""encoder.conv_out.weight"""]
						UpperCAmelCase__    		=						checkpoint["""encoder.conv_out.bias"""]

						UpperCAmelCase__    		=						checkpoint["""decoder.norm_out.weight"""]
						UpperCAmelCase__    		=						checkpoint["""decoder.norm_out.bias"""]

						UpperCAmelCase__    		=						checkpoint["""decoder.conv_in.weight"""]
						UpperCAmelCase__    		=						checkpoint["""decoder.conv_in.bias"""]
						UpperCAmelCase__    		=						checkpoint["""decoder.conv_out.weight"""]
						UpperCAmelCase__    		=						checkpoint["""decoder.conv_out.bias"""]

						UpperCAmelCase__    		=						len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """down""" in layer}	)
						UpperCAmelCase__    		=						{
						    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)
						}

						UpperCAmelCase__    		=						len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """up""" in layer}	)
						UpperCAmelCase__    		=						{layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)}

						for i in range(UpperCamelCase__	):
												UpperCAmelCase__    		=						(i - 1) // (config["""layers_per_block"""] + 1)

												if any("""downsample""" in layer for layer in down_blocks[i]	):
																		UpperCAmelCase__    		=						checkpoint[
																		    f'encoder.down.{i}.downsample.conv.weight'
																		]
																		UpperCAmelCase__    		=						checkpoint[
																		    f'encoder.down.{i}.downsample.conv.bias'
																		]

												if any("""block""" in layer for layer in down_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """block""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
																		    for layer_id in range(UpperCamelCase__	)
																		}

																		if num_blocks > 0:
																								for j in range(config["""layers_per_block"""]	):
																														UpperCAmelCase__    		=						renew_resnet_paths(blocks[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__	)

												if any("""attn""" in layer for layer in down_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """attn""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
																		    for layer_id in range(UpperCamelCase__	)
																		}

																		if num_attn > 0:
																								for j in range(config["""layers_per_block"""]	):
																														UpperCAmelCase__    		=						renew_attention_paths(attns[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	config=UpperCamelCase__	)

						UpperCAmelCase__    		=						[key for key in checkpoint if """mid.block_1""" in key]
						UpperCAmelCase__    		=						[key for key in checkpoint if """mid.block_2""" in key]
						UpperCAmelCase__    		=						[key for key in checkpoint if """mid.attn_1""" in key]

						# Mid new 2
						UpperCAmelCase__    		=						renew_resnet_paths(UpperCamelCase__	)
						assign_to_checkpoint(
						    UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]						,	)

						UpperCAmelCase__    		=						renew_resnet_paths(UpperCamelCase__	)
						assign_to_checkpoint(
						    UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]						,	)

						UpperCAmelCase__    		=						renew_attention_paths(UpperCamelCase__						,	in_mid=UpperCamelCase__	)
						assign_to_checkpoint(
						    UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]						,	)

						for i in range(UpperCamelCase__	):
												UpperCAmelCase__    		=						num_up_blocks - 1 - i

												if any("""upsample""" in layer for layer in up_blocks[i]	):
																		UpperCAmelCase__    		=						checkpoint[
																		    f'decoder.up.{i}.upsample.conv.weight'
																		]
																		UpperCAmelCase__    		=						checkpoint[
																		    f'decoder.up.{i}.upsample.conv.bias'
																		]

												if any("""block""" in layer for layer in up_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """block""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)
																		}

																		if num_blocks > 0:
																								for j in range(config["""layers_per_block"""] + 1	):
																														UpperCAmelCase__    		=						{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
																														UpperCAmelCase__    		=						renew_resnet_paths(blocks[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[replace_indices]	)

												if any("""attn""" in layer for layer in up_blocks[i]	):
																		UpperCAmelCase__    		=						len(
																		    {""".""".join(shave_segments(UpperCamelCase__						,	3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """attn""" in layer}	)
																		UpperCAmelCase__    		=						{
																		    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(UpperCamelCase__	)
																		}

																		if num_attn > 0:
																								for j in range(config["""layers_per_block"""] + 1	):
																														UpperCAmelCase__    		=						{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
																														UpperCAmelCase__    		=						renew_attention_paths(attns[j]	)
																														assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[replace_indices]	)

						UpperCAmelCase__    		=						{k.replace("""mid_new_2"""						,	"""mid_block"""	): v for k, v in new_checkpoint.items()}
						UpperCAmelCase__    		=						checkpoint["""quant_conv.weight"""]
						UpperCAmelCase__    		=						checkpoint["""quant_conv.bias"""]
						if "quantize.embedding.weight" in checkpoint:
												UpperCAmelCase__    		=						checkpoint["""quantize.embedding.weight"""]
						UpperCAmelCase__    		=						checkpoint["""post_quant_conv.weight"""]
						UpperCAmelCase__    		=						checkpoint["""post_quant_conv.bias"""]

						return new_checkpoint


if __name__ == "__main__":
						UpperCamelCase_				:				int							      =						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.""")

						UpperCamelCase_				:				Optional[Any]							      =						parser.parse_args()
						UpperCamelCase_				:				str							      =						torch.load(args.checkpoint_path)

						with open(args.config_file) as f:
												UpperCamelCase_				:				Union[str, Any]							      =						json.loads(f.read())

						# unet case
						UpperCamelCase_				:				int							      =						{key.split(""".""")[0] for key in checkpoint.keys()}
						if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
												UpperCamelCase_				:				Any							      =						convert_vq_autoenc_checkpoint(checkpoint, config)
						else:
												UpperCamelCase_				:				Union[str, Any]							      =						convert_ddpm_checkpoint(checkpoint, config)

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

						if config["_class_name"] == "VQModel":
												UpperCamelCase_				:				List[str]							      =						VQModel(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						elif config["_class_name"] == "AutoencoderKL":
												UpperCamelCase_				:				int							      =						AutoencoderKL(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						else:
												UpperCamelCase_				:				Optional[Any]							      =						UNetaDModel(**config)
												model.load_state_dict(converted_checkpoint)

												UpperCamelCase_				:				int							      =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

												UpperCamelCase_				:				List[Any]							      =						DDPMPipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCamelCase       =	abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Tuple			):
		from transformers.testing_utils import pytest_addoption_shared

		pytest_addoption_shared(UpperCamelCase__			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Tuple			):
		from transformers.testing_utils import pytest_terminal_summary_main

		SCREAMING_SNAKE_CASE__								=					terminalreporter.config.getoption("""--make-reports"""			)
		if make_reports:
				pytest_terminal_summary_main(UpperCamelCase__    ,      id=UpperCamelCase__			)