



'''simple docstring'''





import torch

from transformers import AutoTokenizer


class 	lowerCamelCase__ (		_a   ):


						def __init__( self						:  Any      ,       _a						:  int			):
													a__:       Union[str, Any]											=AutoTokenizer.from_pretrained(_a			)


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  List[Any]			):

													if isinstance(_a      ,       _a			) and all(isinstance(_x      ,       _a			) for _x in x			):
																				a__:       Tuple											=None
																				for l in x:
																											a__:       str											=self.tokenizer(
																											    _a      ,       padding="max_length"      ,       max_length=3_8_4      ,       truncation=_a      ,       return_tensors="pt"      ,       )
																											a__:       Tuple											=torch.tensor([len(_a			)]			)
																											if d is not None:
																																		for k in d.keys():
																																									a__:       List[Any]											=torch.cat((d[k], t[k])      ,       0			)
																											else:
																																		a__:       Optional[Any]											=t

																				a__:       Union[str, Any]											=torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"			)			)
																				a__:       Optional[Any]											=torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"			)			)

													elif isinstance(_a      ,       _a			) and all(isinstance(_x      ,       _a			) for _x in x			):
																				a__:       Any											=self.tokenizer(
																				    _a      ,       padding="max_length"      ,       max_length=3_8_4      ,       truncation=_a      ,       return_tensors="pt"      ,       )

													else:
																				raise Exception(
																				    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
																				    " strings` for supports are supported."			)

													return d







						def   _lowerCamelCase			( self						:  int      ,       _a						:  Dict      ,       _a						:  List[str]      ,       _a						:  Optional[int]      ,       _a						:  str      ,       _a						:  Union[str, Any]=0.7_0			):
													a__:       Tuple											=[]
													for idx in range(len(W_query["input_ids"]			)			):
																				a__:       Optional[Any]											=range(p_start.shape[1]			)

																				a__:       str											=[]
																				for start_id in start_indexes:
																											for end_id in end_indexes:
																																		if start_id < end_id:
																																									output.append(
																																									    (
																																									        start_id,
																																									        end_id,
																																									        p_start[idx][start_id].item(),
																																									        p_end[idx][end_id].item(),
																																									    )			)

																				output.sort(key=lambda _a			: (tup[2] * tup[3])      ,       reverse=_a			)
																				a__:       Any											=[]
																				for k in range(len(_a			)			):
																											if output[k][2] * output[k][3] >= thresh:
																																		a__:       str											=output[k][0], output[k][1]
																																		a__:       int											=self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]			)
																																		temp.append((decoded, output[k][2] * output[k][3])			)

																				final_outputs.append(_a			)

													return final_outputs










import argparse
import json

import torch

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





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  Optional[Any]=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 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]     , __magic_name__	:  Tuple=0						):
							a__:       Dict											=[]
							for old_item in old_list:
														a__:       Tuple											=old_item
														a__:       Any											=new_item.replace("block."     , "resnets."						)
														a__:       Optional[int]											=new_item.replace("conv_shorcut"     , "conv1"						)
														a__:       Dict											=new_item.replace("in_shortcut"     , "conv_shortcut"						)
														a__:       Optional[int]											=new_item.replace("temb_proj"     , "time_emb_proj"						)

														a__:       Optional[int]											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)

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

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]     , __magic_name__	:  Optional[int]=0     , __magic_name__	:  Dict=False						):
							a__:       Optional[int]											=[]
							for old_item in old_list:
														a__:       Dict											=old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					a__:       Optional[int]											=new_item.replace("attn"     , "attentions"						)
														a__:       Dict											=new_item.replace(".k."     , ".key."						)
														a__:       Dict											=new_item.replace(".v."     , ".value."						)
														a__:       str											=new_item.replace(".q."     , ".query."						)

														a__:       Union[str, Any]											=new_item.replace("proj_out"     , "proj_attn"						)
														a__:       Tuple											=new_item.replace("norm"     , "group_norm"						)

														a__:       Any											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)
														mapping.append({"old": old_item, "new": new_item}						)

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Tuple     , __magic_name__	:  Tuple     , __magic_name__	:  int     , __magic_name__	:  Union[str, Any]=None     , __magic_name__	:  Tuple=None     , __magic_name__	:  Union[str, Any]=None						):
							assert isinstance(__magic_name__     , __magic_name__						), "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():
																					a__:       Any											=old_checkpoint[path]
																					a__:       List[Any]											=old_tensor.shape[0] // 3

																					a__:       List[Any]											=(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

																					a__:       Optional[int]											=old_tensor.shape[0] // config.get("num_head_channels"     , 1						) // 3

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

																					a__:       Any											=query.reshape(__magic_name__						).squeeze()
																					a__:       Optional[Any]											=key.reshape(__magic_name__						).squeeze()
																					a__:       List[Any]											=value.reshape(__magic_name__						).squeeze()

							for path in paths:
														a__:       Optional[Any]											=path["new"]

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														a__:       Any											=new_path.replace("down."     , "down_blocks."						)
														a__:       Union[str, Any]											=new_path.replace("up."     , "up_blocks."						)

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

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





def 						__lowerCamelCase       (      __magic_name__	:  Any     , __magic_name__	:  List[str]						):
							a__:       Tuple											={}

							a__:       Optional[Any]											=checkpoint["temb.dense.0.weight"]
							a__:       List[str]											=checkpoint["temb.dense.0.bias"]
							a__:       Dict											=checkpoint["temb.dense.1.weight"]
							a__:       int											=checkpoint["temb.dense.1.bias"]

							a__:       Union[str, Any]											=checkpoint["norm_out.weight"]
							a__:       Tuple											=checkpoint["norm_out.bias"]

							a__:       Dict											=checkpoint["conv_in.weight"]
							a__:       Any											=checkpoint["conv_in.bias"]
							a__:       List[Any]											=checkpoint["conv_out.weight"]
							a__:       Tuple											=checkpoint["conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "down" in layer}						)
							a__:       int											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       Any											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "up" in layer}						)
							a__:       List[str]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Dict											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"down.{i}.downsample.op.weight"
																					]
																					a__:       int											=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]						):
																					a__:       List[Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       List[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__						)

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       str											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       str											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

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

							a__:       Union[str, Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       Dict											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

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

							a__:       Optional[Any]											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]     , )

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

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Tuple											=checkpoint[
																					    F"up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[F"up.{i}.upsample.conv.bias"]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       Dict											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Dict											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Optional[int]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Union[str, Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       List[Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       List[str]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       List[str]											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       Optional[int]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__lowerCamelCase       (      __magic_name__	:  str     , __magic_name__	:  Optional[Any]						):
							a__:       List[str]											={}

							a__:       Optional[Any]											=checkpoint["encoder.norm_out.weight"]
							a__:       Any											=checkpoint["encoder.norm_out.bias"]

							a__:       List[str]											=checkpoint["encoder.conv_in.weight"]
							a__:       List[str]											=checkpoint["encoder.conv_in.bias"]
							a__:       Union[str, Any]											=checkpoint["encoder.conv_out.weight"]
							a__:       Optional[Any]											=checkpoint["encoder.conv_out.bias"]

							a__:       List[Any]											=checkpoint["decoder.norm_out.weight"]
							a__:       Tuple											=checkpoint["decoder.norm_out.bias"]

							a__:       Optional[int]											=checkpoint["decoder.conv_in.weight"]
							a__:       str											=checkpoint["decoder.conv_in.bias"]
							a__:       Tuple											=checkpoint["decoder.conv_out.weight"]
							a__:       Dict											=checkpoint["decoder.conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "down" in layer}						)
							a__:       Optional[Any]											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       int											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "up" in layer}						)
							a__:       Optional[int]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.weight"
																					]
																					a__:       Union[str, Any]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.bias"
																					]

														if any("block" in layer for layer in down_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       List[str]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

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

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       int											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       int											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

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

							a__:       List[Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       int											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

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

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

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

							for i in range(__magic_name__						):
														a__:       List[Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Optional[Any]											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.bias"
																					]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Union[str, Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Union[str, Any]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Optional[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Dict											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       int											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       List[Any]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							a__:       List[Any]											=checkpoint["quant_conv.weight"]
							a__:       Tuple											=checkpoint["quant_conv.bias"]
							if "quantize.embedding.weight" in checkpoint:
														a__:       Dict											=checkpoint["quantize.embedding.weight"]
							a__:       Union[str, Any]											=checkpoint["post_quant_conv.weight"]
							a__:       str											=checkpoint["post_quant_conv.bias"]

							return new_checkpoint


if __name__ == "__main__":
		__UpperCAmelCase						  =     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						  =     parser.parse_args()
		__UpperCAmelCase						  =     torch.load(args.checkpoint_path)

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

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

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

		if config["_class_name"] == "VQModel":
				__UpperCAmelCase						  =     VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				__UpperCAmelCase						  =     AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				__UpperCAmelCase						  =     UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				__UpperCAmelCase						  =     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				__UpperCAmelCase						  =     DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)





