
"""simple docstring"""





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 				A_  (__a					, __a=1					):

					'''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_  (__a					, __a=0					):

					'''simple docstring'''





					A_								=    []
					for old_item in old_list:
										A_								=    old_item.replace("in_layers.0"					, "norm1"					)
										A_								=    new_item.replace("in_layers.2"					, "conv1"					)

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

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

										A_								=    shave_segments(__a					, n_shave_prefix_segments=__a					)

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

					return mapping
def 				A_  (__a					, __a=0					):

					'''simple docstring'''





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

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

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

										A_								=    shave_segments(__a					, n_shave_prefix_segments=__a					)

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

					return mapping
def 				A_  (__a					, __a					, __a					, __a=None					, __a=None					, __a=None					):

					'''simple docstring'''





					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():
															A_								=    old_checkpoint[path]
															A_								=    old_tensor.shape[0] // 3

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

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

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

															A_								=    query.reshape(__a					)
															A_								=    key.reshape(__a					)
															A_								=    value.reshape(__a					)

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

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				A_								=    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:
															A_								=    old_checkpoint[path["old"]][:, :, 0]
										else:
															A_								=    old_checkpoint[path["old"]]
def 				A_  (__a					, __a					):

					'''simple docstring'''





					A_								=    {}

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

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

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

					# Retrieves the keys for the input blocks only
					A_								=    len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "input_blocks" in layer}					)
					A_								=    {
					    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
					A_								=    len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "middle_block" in layer}					)
					A_								=    {
					    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
					A_								=    len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "output_blocks" in layer}					)
					A_								=    {
					    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					):
										A_								=    (i - 1) // (config["num_res_blocks"] + 1)
										A_								=    (i - 1) % (config["num_res_blocks"] + 1)

										A_								=    [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
										A_								=    [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:
															A_								=    checkpoint[
															    f'input_blocks.{i}.0.op.weight'
															]
															A_								=    checkpoint[
															    f'input_blocks.{i}.0.op.bias'
															]
															continue

										A_								=    renew_resnet_paths(__a					)
										A_								=    {"old": f'input_blocks.{i}.0', "new": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
										A_								=    {"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					):
															A_								=    renew_attention_paths(__a					)
															A_								=    {
															    "old": f'input_blocks.{i}.1',
															    "new": f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
															}
															A_								=    {
															    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					, )

					A_								=    middle_blocks[0]
					A_								=    middle_blocks[1]
					A_								=    middle_blocks[2]

					A_								=    renew_resnet_paths(__a					)
					assign_to_checkpoint(__a					, __a					, __a					, config=__a					)

					A_								=    renew_resnet_paths(__a					)
					assign_to_checkpoint(__a					, __a					, __a					, config=__a					)

					A_								=    renew_attention_paths(__a					)
					A_								=    {
					    "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					):
										A_								=    i // (config["num_res_blocks"] + 1)
										A_								=    i % (config["num_res_blocks"] + 1)
										A_								=    [shave_segments(__a					, 2					) for name in output_blocks[i]]
										A_								=    {}

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

										if len(__a					) > 1:
															A_								=    [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
															A_								=    [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

															A_								=    renew_resnet_paths(__a					)
															A_								=    renew_resnet_paths(__a					)

															A_								=    {"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():
																				A_								=    list(output_block_list.values()					).index(["conv.weight", "conv.bias"]					)
																				A_								=    checkpoint[
																				    f'output_blocks.{i}.{index}.conv.weight'
																				]
																				A_								=    checkpoint[
																				    f'output_blocks.{i}.{index}.conv.bias'
																				]

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

															if len(__a					):
																				A_								=    renew_attention_paths(__a					)
																				A_								=    {
																				    "old": f'output_blocks.{i}.1',
																				    "new": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																				}
																				A_								=    {
																				    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:
															A_								=    renew_resnet_paths(__a					, n_shave_prefix_segments=1					)
															for path in resnet_0_paths:
																				A_								=    ".".join(["output_blocks", str(__a					), path["old"]]					)
																				A_								=    ".".join(["up_blocks", str(__a					), "resnets", str(__a					), path["new"]]					)

																				A_								=    checkpoint[old_path]

					return new_checkpoint


if __name__ == "__main__":
		UpperCamelCase_       :    Any						      =			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_       :    List[Any]						      =			parser.parse_args()

		UpperCamelCase_       :    Optional[int]						      =			torch.load(args.checkpoint_path)

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

		UpperCamelCase_       :    Optional[Any]						      =			convert_ldm_checkpoint(checkpoint, config)

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

		UpperCamelCase_       :    int						      =			UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

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

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






"""simple docstring"""





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL




