





import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__lowerCAmelCase 	=      logging.get_logger(__name__)




@dataclass
class 	_UpperCAmelCase						:
					__A        = 42
					__A        = 42
					__A        = None
					__A        = None




					def 						snake_case_		(    self							)    ->	Tuple:
							return json.dumps(dataclasses.asdict(self							)     ,   indent=2							) + "\n"




@dataclass(frozen=_lowercase       )
class 	_UpperCAmelCase						:
					__A        = 42
					__A        = None
					__A        = None
					__A        = None




					def 						snake_case_		(    self							)    ->	str:
							return json.dumps(dataclasses.asdict(self							)							) + "\n"




class 	_UpperCAmelCase						:




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Tuple:
							raise NotImplementedError()




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Optional[Any]:
							raise NotImplementedError()




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	str:
							raise NotImplementedError()




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Optional[int]:
							raise NotImplementedError()




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							raise NotImplementedError()




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	int:
							if len(self.get_labels()							) > 1:
									UpperCamelCase__								=						self.get_labels()[int(example.label							)]
							return example




					@classmethod
					def 						snake_case_		(    cls     ,   __lowerCAmelCase     ,   __lowerCAmelCase=None							)    ->	int:
							with open(__lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8-sig"""							) as f:
									return list(csv.reader(__lowerCAmelCase     ,   delimiter="""\t"""     ,   quotechar=__lowerCAmelCase							)							)




class 	_UpperCAmelCase						( _lowercase       ):




					def __init__(    self     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase="classification"     ,   __lowerCAmelCase=False							)    ->	List[str]:
							UpperCamelCase__								=						[] if labels is None else labels
							UpperCamelCase__								=						[] if examples is None else examples
							UpperCamelCase__								=						mode
							UpperCamelCase__								=						verbose




					def __len__(    self							)    ->	Dict:
							return len(self.examples							)




					def __getitem__(    self     ,   __lowerCAmelCase							)    ->	List[Any]:
							if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							):
									return SingleSentenceClassificationProcessor(labels=self.labels     ,   examples=self.examples[idx]							)
							return self.examples[idx]




					@classmethod
					def 						snake_case_		(    cls     ,   __lowerCAmelCase     ,   __lowerCAmelCase=""     ,   __lowerCAmelCase=0     ,   __lowerCAmelCase=1     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=False     ,   **__lowerCAmelCase							)    ->	str:
							UpperCamelCase__								=						cls(**__lowerCAmelCase							)
							processor.add_examples_from_csv(
							    __lowerCAmelCase     ,   split_name=__lowerCAmelCase     ,   column_label=__lowerCAmelCase     ,   column_text=__lowerCAmelCase     ,   column_id=__lowerCAmelCase     ,   skip_first_row=__lowerCAmelCase     ,   overwrite_labels=__lowerCAmelCase     ,   overwrite_examples=__lowerCAmelCase     ,   )
							return processor




					@classmethod
					def 						snake_case_		(    cls     ,   __lowerCAmelCase     ,   __lowerCAmelCase=None     ,   **__lowerCAmelCase							)    ->	Dict:
							UpperCamelCase__								=						cls(**__lowerCAmelCase							)
							processor.add_examples(__lowerCAmelCase     ,   labels=__lowerCAmelCase							)
							return processor




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=""     ,   __lowerCAmelCase=0     ,   __lowerCAmelCase=1     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   )    ->	str:
							UpperCamelCase__								=						self._read_tsv(__lowerCAmelCase							)
							if skip_first_row:
									UpperCamelCase__								=						lines[1:]
							UpperCamelCase__								=						[]
							UpperCamelCase__								=						[]
							UpperCamelCase__								=						[]
							for i, line in enumerate(__lowerCAmelCase							):
									texts.append(line[column_text]							)
									labels.append(line[column_label]							)
									if column_id is not None:
											ids.append(line[column_id]							)
									else:
											UpperCamelCase__								=						F"""{split_name}-{i}""" if split_name else str(__lowerCAmelCase							)
											ids.append(__lowerCAmelCase							)

							return self.add_examples(
							    __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   overwrite_labels=__lowerCAmelCase     ,   overwrite_examples=__lowerCAmelCase							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False							)    ->	Union[str, Any]:
							if labels is not None and len(__lowerCAmelCase							) != len(__lowerCAmelCase							):
									raise ValueError(
									    F"""Text and labels have mismatched lengths {len(__lowerCAmelCase							)} and {len(__lowerCAmelCase							)}"""							)
							if ids is not None and len(__lowerCAmelCase							) != len(__lowerCAmelCase							):
									raise ValueError(F"""Text and ids have mismatched lengths {len(__lowerCAmelCase							)} and {len(__lowerCAmelCase							)}"""							)
							if ids is None:
									UpperCamelCase__								=						[None] * len(__lowerCAmelCase							)
							if labels is None:
									UpperCamelCase__								=						[None] * len(__lowerCAmelCase							)
							UpperCamelCase__								=						[]
							UpperCamelCase__								=						set()
							for text_or_text_and_label, label, guid in zip(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							):
									if isinstance(__lowerCAmelCase     ,   (tuple, list)							) and label is None:
											UpperCamelCase__	,      UpperCamelCase__								=						text_or_text_and_label
									else:
											UpperCamelCase__								=						text_or_text_and_label
									added_labels.add(__lowerCAmelCase							)
									examples.append(InputExample(guid=__lowerCAmelCase     ,   text_a=__lowerCAmelCase     ,   text_b=__lowerCAmelCase     ,   label=__lowerCAmelCase							)							)

							# Update examples
							if overwrite_examples:
									UpperCamelCase__								=						examples
							else:
									self.examples.extend(__lowerCAmelCase							)

							# Update labels
							if overwrite_labels:
									UpperCamelCase__								=						list(__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						list(set(self.labels							).union(__lowerCAmelCase							)							)

							return self.examples




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=0     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=None     ,   )    ->	Dict:
							if max_length is None:
									UpperCamelCase__								=						tokenizer.max_len

							UpperCamelCase__								=						{label: i for i, label in enumerate(self.labels							)}

							UpperCamelCase__								=						[]
							for ex_index, example in enumerate(self.examples							):
									if ex_index % 10_000 == 0:
											logger.info(F"""Tokenizing example {ex_index}"""							)

									UpperCamelCase__								=						tokenizer.encode(
									    example.text_a     ,   add_special_tokens=__lowerCAmelCase     ,   max_length=min(__lowerCAmelCase     ,   tokenizer.max_len							)     ,   )
									all_input_ids.append(__lowerCAmelCase							)

							UpperCamelCase__								=						max(len(__lowerCAmelCase							) for input_ids in all_input_ids							)

							UpperCamelCase__								=						[]
							for ex_index, (input_ids, example) in enumerate(zip(__lowerCAmelCase     ,   self.examples							)							):
									if ex_index % 10_000 == 0:
											logger.info(F"""Writing example {ex_index}/{len(self.examples							)}"""							)
									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									UpperCamelCase__								=						[1 if mask_padding_with_zero else 0] * len(__lowerCAmelCase							)

									# Zero-pad up to the sequence length.
									UpperCamelCase__								=						batch_length - len(__lowerCAmelCase							)
									if pad_on_left:
											UpperCamelCase__								=						([pad_token] * padding_length) + input_ids
											UpperCamelCase__								=						([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
									else:
											UpperCamelCase__								=						input_ids + ([pad_token] * padding_length)
											UpperCamelCase__								=						attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

									if len(__lowerCAmelCase							) != batch_length:
											raise ValueError(F"""Error with input length {len(__lowerCAmelCase							)} vs {batch_length}"""							)
									if len(__lowerCAmelCase							) != batch_length:
											raise ValueError(F"""Error with input length {len(__lowerCAmelCase							)} vs {batch_length}"""							)

									if self.mode == "classification":
											UpperCamelCase__								=						label_map[example.label]
									elif self.mode == "regression":
											UpperCamelCase__								=						float(example.label							)
									else:
											raise ValueError(self.mode							)

									if ex_index < 5 and self.verbose:
											logger.info("""*** Example ***"""							)
											logger.info(F"""guid: {example.guid}"""							)
											logger.info(F"""input_ids: {' '.join([str(__lowerCAmelCase							) for x in input_ids]							)}"""							)
											logger.info(F"""attention_mask: {' '.join([str(__lowerCAmelCase							) for x in attention_mask]							)}"""							)
											logger.info(F"""label: {example.label} (id = {label})"""							)

									features.append(InputFeatures(input_ids=__lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   label=__lowerCAmelCase							)							)

							if return_tensors is None:
									return features
							elif return_tensors == "tf":
									if not is_tf_available():
											raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""							)
									import tensorflow as tf

									def gen():
											for ex in features:
													yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

									UpperCamelCase__								=						tf.data.Dataset.from_generator(
									    __lowerCAmelCase     ,   ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,   ({"""input_ids""": tf.TensorShape([None]							), """attention_mask""": tf.TensorShape([None]							)}, tf.TensorShape([]							))     ,   )
									return dataset
							elif return_tensors == "pt":
									if not is_torch_available():
											raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""							)
									import torch
									from torch.utils.data import TensorDataset

									UpperCamelCase__								=						torch.tensor([f.input_ids for f in features]     ,   dtype=torch.long							)
									UpperCamelCase__								=						torch.tensor([f.attention_mask for f in features]     ,   dtype=torch.long							)
									if self.mode == "classification":
											UpperCamelCase__								=						torch.tensor([f.label for f in features]     ,   dtype=torch.long							)
									elif self.mode == "regression":
											UpperCamelCase__								=						torch.tensor([f.label for f in features]     ,   dtype=torch.float							)

									UpperCamelCase__								=						TensorDataset(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)
									return dataset
							else:
									raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""							)












import argparse

import torch

from diffusers import UNetaDConditionModel

def      a    (   _lowercase :  Dict			,    _lowercase :  Dict			,    _lowercase :  Any			,    _lowercase :  Tuple=None			,    _lowercase :  List[Any]=None			,    _lowercase :  Optional[Any]=None							)  ->Any:
		assert isinstance(_lowercase			,    _lowercase							), "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__								=						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["""num_head_channels"""] // 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(_lowercase							)
						UpperCamelCase__								=						key.reshape(_lowercase							)
						UpperCamelCase__								=						value.reshape(_lowercase							)

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

				# These have already been assigned
				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

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

        # proj_attn.weight has to be converted from conv 1D to linear
				UpperCamelCase__								=						old_checkpoint[path["""old"""]]
				UpperCamelCase__								=						["""proj_attn.weight"""]
				UpperCamelCase__								=						["""proj_out.weight""", """proj_in.weight"""]
				if any(k in new_path for k in names							):
						UpperCamelCase__								=						weight[:, :, 0]
				elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
						UpperCamelCase__								=						weight[:, :, 0]
				else:
						UpperCamelCase__								=						weight

def      a    (   _lowercase :  Tuple			,    _lowercase :  List[Any]=0							)  ->int:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				UpperCamelCase__								=						old_item

				#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
				#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

				#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
				#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

				#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

		return mapping

def      a    (   _lowercase :  Union[str, Any]			,    _lowercase :  int=1							)  ->str:
		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    (   _lowercase :  List[str]			,    _lowercase :  Any=0							)  ->Any:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				mapping.append({"""old""": old_item, """new""": old_item}							)

		return mapping

def      a    (   _lowercase :  List[str]			,    _lowercase :  int=0							)  ->Tuple:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				UpperCamelCase__								=						old_item.replace("""in_layers.0"""			,    """norm1"""							)
				UpperCamelCase__								=						new_item.replace("""in_layers.2"""			,    """conv1"""							)

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

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

				UpperCamelCase__								=						shave_segments(_lowercase			,    n_shave_prefix_segments=_lowercase							)

				if "temopral_conv" not in old_item:
						mapping.append({"""old""": old_item, """new""": new_item}							)

		return mapping

def      a    (   _lowercase :  List[Any]			,    _lowercase :  List[Any]			,    _lowercase :  int=None			,    _lowercase :  Optional[Any]=False							)  ->List[str]:
		UpperCamelCase__								=						{}
		UpperCamelCase__								=						list(checkpoint.keys()							)

		UpperCamelCase__								=						"""model.diffusion_model."""

		# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
		if sum(k.startswith("""model_ema"""							) for k in keys							) > 1_0_0 and extract_ema:
				print(F"""Checkpoint {path} has both EMA and non-EMA weights."""							)
				print(
				    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
				    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""							)
				for key in keys:
						if key.startswith("""model.diffusion_model"""							):
								UpperCamelCase__								=						"""model_ema.""" + """""".join(key.split("""."""							)[1:]							)
								UpperCamelCase__								=						checkpoint.pop(_lowercase							)
		else:
				if sum(k.startswith("""model_ema"""							) for k in keys							) > 1_0_0:
						print(
						    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
						    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""							)

				for key in keys:
						UpperCamelCase__								=						checkpoint.pop(_lowercase							)

		UpperCamelCase__								=						{}

		UpperCamelCase__								=						unet_state_dict["""time_embed.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.0.bias"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.2.weight"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.2.bias"""]

		if config["class_embed_type"] is None:
				# No parameters to port
				...
		elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.0.weight"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.0.bias"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.2.weight"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.2.bias"""]
		else:
				raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""							)

		UpperCamelCase__								=						unet_state_dict["""input_blocks.0.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""input_blocks.0.0.bias"""]

		UpperCamelCase__								=						[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""							)]
		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
		assign_to_checkpoint(_lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						unet_state_dict["""out.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""out.0.bias"""]
		UpperCamelCase__								=						unet_state_dict["""out.2.weight"""]
		UpperCamelCase__								=						unet_state_dict["""out.2.bias"""]

		# Retrieves the keys for the input blocks only
		UpperCamelCase__								=						len({""".""".join(layer.split("""."""							)[:2]							) for layer in unet_state_dict if """input_blocks""" in layer}							)
		UpperCamelCase__								=						{
		    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
		    for layer_id in range(_lowercase							)
		}

		# Retrieves the keys for the middle blocks only
		UpperCamelCase__								=						len({""".""".join(layer.split("""."""							)[:2]							) for layer in unet_state_dict if """middle_block""" in layer}							)
		UpperCamelCase__								=						{
		    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
		    for layer_id in range(_lowercase							)
		}

		# Retrieves the keys for the output blocks only
		UpperCamelCase__								=						len({""".""".join(layer.split("""."""							)[:2]							) for layer in unet_state_dict if """output_blocks""" in layer}							)
		UpperCamelCase__								=						{
		    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(_lowercase							)
		}

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

				UpperCamelCase__								=						[
				    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
				]
				UpperCamelCase__								=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
				UpperCamelCase__								=						[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

				if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
						UpperCamelCase__								=						unet_state_dict.pop(
						    F"""input_blocks.{i}.op.weight"""							)
						UpperCamelCase__								=						unet_state_dict.pop(
						    F"""input_blocks.{i}.op.bias"""							)

				UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
				UpperCamelCase__								=						{"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				assign_to_checkpoint(
				    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

				UpperCamelCase__								=						[key for key in resnets if """temopral_conv""" in key]
				UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
				UpperCamelCase__								=						{
				    """old""": F"""input_blocks.{i}.0.temopral_conv""",
				    """new""": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
				}
				assign_to_checkpoint(
				    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

				if len(_lowercase							):
						UpperCamelCase__								=						renew_attention_paths(_lowercase							)
						UpperCamelCase__								=						{"""old""": F"""input_blocks.{i}.1""", """new""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
						assign_to_checkpoint(
						    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

				if len(_lowercase							):
						UpperCamelCase__								=						renew_attention_paths(_lowercase							)
						UpperCamelCase__								=						{
						    """old""": F"""input_blocks.{i}.2""",
						    """new""": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
						}
						assign_to_checkpoint(
						    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						middle_blocks[0]
		UpperCamelCase__								=						[key for key in resnet_a if """temopral_conv""" in key]
		UpperCamelCase__								=						middle_blocks[1]
		UpperCamelCase__								=						middle_blocks[2]
		UpperCamelCase__								=						middle_blocks[3]
		UpperCamelCase__								=						[key for key in resnet_a if """temopral_conv""" in key]

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		for i in range(_lowercase							):
				UpperCamelCase__								=						i // (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						i % (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						[shave_segments(_lowercase			,    2							) for name in output_blocks[i]]
				UpperCamelCase__								=						{}

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

				if len(_lowercase							) > 1:
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)

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

						UpperCamelCase__								=						[key for key in resnets if """temopral_conv""" in key]
						UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
						UpperCamelCase__								=						{
						    """old""": F"""output_blocks.{i}.0.temopral_conv""",
						    """new""": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
						}
						assign_to_checkpoint(
						    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

						UpperCamelCase__								=						{k: sorted(_lowercase							) for k, v in output_block_list.items()}
						if ["conv.bias", "conv.weight"] in output_block_list.values():
								UpperCamelCase__								=						list(output_block_list.values()							).index(["""conv.bias""", """conv.weight"""]							)
								UpperCamelCase__								=						unet_state_dict[
								    F"""output_blocks.{i}.{index}.conv.weight"""
								]
								UpperCamelCase__								=						unet_state_dict[
								    F"""output_blocks.{i}.{index}.conv.bias"""
								]

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

						if len(_lowercase							):
								UpperCamelCase__								=						renew_attention_paths(_lowercase							)
								UpperCamelCase__								=						{
								    """old""": F"""output_blocks.{i}.1""",
								    """new""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
								}
								assign_to_checkpoint(
								    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

						if len(_lowercase							):
								UpperCamelCase__								=						renew_attention_paths(_lowercase							)
								UpperCamelCase__								=						{
								    """old""": F"""output_blocks.{i}.2""",
								    """new""": F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
								}
								assign_to_checkpoint(
								    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)
				else:
						UpperCamelCase__								=						renew_resnet_paths(_lowercase			,    n_shave_prefix_segments=1							)
						for path in resnet_0_paths:
								UpperCamelCase__								=						""".""".join(["""output_blocks""", str(_lowercase							), path["""old"""]]							)
								UpperCamelCase__								=						""".""".join(["""up_blocks""", str(_lowercase							), """resnets""", str(_lowercase							), path["""new"""]]							)
								UpperCamelCase__								=						unet_state_dict[old_path]

						UpperCamelCase__								=						[l for l in output_block_layers if """temopral_conv""" in l]
						for path in temopral_conv_paths:
								UpperCamelCase__								=						path.split("""temopral_conv."""							)[-1]
								UpperCamelCase__								=						""".""".join(["""output_blocks""", str(_lowercase							), str(_lowercase							), """temopral_conv""", pruned_path]							)
								UpperCamelCase__								=						""".""".join(["""up_blocks""", str(_lowercase							), """temp_convs""", str(_lowercase							), pruned_path]							)
								UpperCamelCase__								=						unet_state_dict[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("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
				__lowerCAmelCase 	=      parser.parse_args()

				__lowerCAmelCase 	=      torch.load(args.checkpoint_path, map_location="cpu")
				__lowerCAmelCase 	=      UNetaDConditionModel()

				__lowerCAmelCase 	=      convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

				__lowerCAmelCase 	=      set(unet.state_dict().keys()) - set(converted_ckpt.keys())
				__lowerCAmelCase 	=      set(converted_ckpt.keys()) - set(unet.state_dict().keys())

				assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

				# load state_dict
				unet.load_state_dict(converted_ckpt)

				unet.save_pretrained(args.dump_path)

				# -- finish converting the unet --





