






"""simple docstring"""


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


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)




@dataclass
class 	__UpperCamelCase    :
					lowercase_						:							str
					lowercase_						:							str
					lowercase_						:							Optional[str]			=     None
					lowercase_						:							Optional[str]			=     None
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					str:
											return json.dumps(dataclasses.asdict(self      )    ,   indent=2      ) + "\n"




@dataclass(frozen=UpperCamelCase	)
class 	__UpperCamelCase    :
					lowercase_						:							List[int]
					lowercase_						:							Optional[List[int]]			=     None
					lowercase_						:							Optional[List[int]]			=     None
					lowercase_						:							Optional[Union[int, float]]			=     None
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Optional[Any]:
											return json.dumps(dataclasses.asdict(self      )      ) + "\n"




class 	__UpperCamelCase    :
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Any      )  ->					Dict:
											raise NotImplementedError()
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Optional[Any]      )  ->					Tuple:
											raise NotImplementedError()
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  List[str]      )  ->					Dict:
											raise NotImplementedError()
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  List[str]      )  ->					Optional[Any]:
											raise NotImplementedError()
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Dict:
											raise NotImplementedError()
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  Optional[int]      )  ->					str:
											if len(self.get_labels()      ) > 1:
																	lowerCAmelCase      :int				  = self.get_labels()[int(example.label      )]
											return example
					@classmethod
					def 					UpperCAmelCase__   (       cls       :  Any    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  List[Any]=None      )  ->					Optional[Any]:
											with open(UpperCAmelCase    ,   'r'    ,   encoding='utf-8-sig'      ) as f:
																	return list(csv.reader(UpperCAmelCase    ,   delimiter='\t'    ,   quotechar=UpperCAmelCase      )      )




class 	__UpperCamelCase    (    UpperCamelCase	):
					def __init__(       self       :  List[Any]    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Union[str, Any]="classification"    ,   UpperCAmelCase       :  Union[str, Any]=False      )  ->					Any:
											lowerCAmelCase      :Optional[int]				  = [] if labels is None else labels
											lowerCAmelCase      :Tuple				  = [] if examples is None else examples
											lowerCAmelCase      :Optional[Any]				  = mode
											lowerCAmelCase      :Dict				  = verbose
					def __len__(       self       :  str      )  ->					int:
											return len(self.examples      )
					def __getitem__(       self       :  str    ,   UpperCAmelCase       :  str      )  ->					List[Any]:
											if isinstance(UpperCAmelCase    ,   UpperCAmelCase      ):
																	return SingleSentenceClassificationProcessor(labels=self.labels    ,   examples=self.examples[idx]      )
											return self.examples[idx]
					@classmethod
					def 					UpperCAmelCase__   (       cls       :  Any    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  int=""    ,   UpperCAmelCase       :  str=0    ,   UpperCAmelCase       :  Optional[int]=1    ,   UpperCAmelCase       :  str=None    ,   UpperCAmelCase       :  List[Any]=False    ,   **UpperCAmelCase       :  List[Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = cls(**UpperCAmelCase      )
											processor.add_examples_from_csv(
											    UpperCAmelCase    ,   split_name=UpperCAmelCase    ,   column_label=UpperCAmelCase    ,   column_text=UpperCAmelCase    ,   column_id=UpperCAmelCase    ,   skip_first_row=UpperCAmelCase    ,   overwrite_labels=UpperCAmelCase    ,   overwrite_examples=UpperCAmelCase    ,   )
											return processor
					@classmethod
					def 					UpperCAmelCase__   (       cls       :  Union[str, Any]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Union[str, Any]=None    ,   **UpperCAmelCase       :  Optional[int]      )  ->					str:
											lowerCAmelCase      :int				  = cls(**UpperCAmelCase      )
											processor.add_examples(UpperCAmelCase    ,   labels=UpperCAmelCase      )
											return processor
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  Any=""    ,   UpperCAmelCase       :  Dict=0    ,   UpperCAmelCase       :  Any=1    ,   UpperCAmelCase       :  List[Any]=None    ,   UpperCAmelCase       :  Union[str, Any]=False    ,   UpperCAmelCase       :  List[Any]=False    ,   UpperCAmelCase       :  int=False    ,   )  ->					Tuple:
											lowerCAmelCase      :List[str]				  = self._read_tsv(UpperCAmelCase      )
											if skip_first_row:
																	lowerCAmelCase      :Tuple				  = lines[1:]
											lowerCAmelCase      :List[str]				  = []
											lowerCAmelCase      :Dict				  = []
											lowerCAmelCase      :Optional[int]				  = []
											for i, line in enumerate(UpperCAmelCase      ):
																	texts.append(line[column_text]      )
																	labels.append(line[column_label]      )
																	if column_id is not None:
																							ids.append(line[column_id]      )
																	else:
																							lowerCAmelCase      :Optional[Any]				  = f"""{split_name}-{i}""" if split_name else str(UpperCAmelCase      )
																							ids.append(UpperCAmelCase      )

											return self.add_examples(
											    UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   overwrite_labels=UpperCAmelCase    ,   overwrite_examples=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Dict=False    ,   UpperCAmelCase       :  int=False      )  ->					Tuple:
											if labels is not None and len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
																	raise ValueError(
																	    f"""Text and labels have mismatched lengths {len(UpperCAmelCase      )} and {len(UpperCAmelCase      )}"""      )
											if ids is not None and len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
																	raise ValueError(f"""Text and ids have mismatched lengths {len(UpperCAmelCase      )} and {len(UpperCAmelCase      )}"""      )
											if ids is None:
																	lowerCAmelCase      :List[str]				  = [None] * len(UpperCAmelCase      )
											if labels is None:
																	lowerCAmelCase      :Optional[int]				  = [None] * len(UpperCAmelCase      )
											lowerCAmelCase      :int				  = []
											lowerCAmelCase      :List[Any]				  = set()
											for text_or_text_and_label, label, guid in zip(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      ):
																	if isinstance(UpperCAmelCase    ,   (tuple, list)      ) and label is None:
																							lowerCAmelCase      :Optional[Any]				  = text_or_text_and_label
																	else:
																							lowerCAmelCase      :List[str]				  = text_or_text_and_label
																	added_labels.add(UpperCAmelCase      )
																	examples.append(InputExample(guid=UpperCAmelCase    ,   text_a=UpperCAmelCase    ,   text_b=UpperCAmelCase    ,   label=UpperCAmelCase      )      )

											# Update examples
											if overwrite_examples:
																	lowerCAmelCase      :List[Any]				  = examples
											else:
																	self.examples.extend(UpperCAmelCase      )

											# Update labels
											if overwrite_labels:
																	lowerCAmelCase      :Optional[int]				  = list(UpperCAmelCase      )
											else:
																	lowerCAmelCase      :int				  = list(set(self.labels      ).union(UpperCAmelCase      )      )

											return self.examples
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  Any    ,   UpperCAmelCase       :  Optional[int]=None    ,   UpperCAmelCase       :  Tuple=False    ,   UpperCAmelCase       :  Optional[int]=0    ,   UpperCAmelCase       :  Tuple=True    ,   UpperCAmelCase       :  Dict=None    ,   )  ->					Optional[Any]:
											if max_length is None:
																	lowerCAmelCase      :Optional[int]				  = tokenizer.max_len

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

											lowerCAmelCase      :int				  = []
											for ex_index, example in enumerate(self.examples      ):
																	if ex_index % 1_0000 == 0:
																							logger.info(f"""Tokenizing example {ex_index}"""      )

																	lowerCAmelCase      :Tuple				  = tokenizer.encode(
																	    example.text_a    ,   add_special_tokens=UpperCAmelCase    ,   max_length=min(UpperCAmelCase    ,   tokenizer.max_len      )    ,   )
																	all_input_ids.append(UpperCAmelCase      )

											lowerCAmelCase      :Optional[Any]				  = max(len(UpperCAmelCase      ) for input_ids in all_input_ids      )

											lowerCAmelCase      :int				  = []
											for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase    ,   self.examples      )      ):
																	if ex_index % 1_0000 == 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.
																	lowerCAmelCase      :Union[str, Any]				  = [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase      )

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

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

																	if self.mode == "classification":
																							lowerCAmelCase      :List[str]				  = label_map[example.label]
																	elif self.mode == "regression":
																							lowerCAmelCase      :Union[str, Any]				  = 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(UpperCAmelCase      ) for x in input_ids]      )}"""      )
																							logger.info(f"""attention_mask: {" ".join([str(UpperCAmelCase      ) for x in attention_mask]      )}"""      )
																							logger.info(f"""label: {example.label} (id = {label})"""      )

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

											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)

																	lowerCAmelCase      :Optional[int]				  = tf.data.Dataset.from_generator(
																	    UpperCAmelCase    ,   ({'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

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

																	lowerCAmelCase      :str				  = TensorDataset(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
																	return dataset
											else:
																	raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )







"""simple docstring"""


import argparse
import json

import torch

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


def 					UpperCAmelCase			(				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 					UpperCAmelCase			(				a__    ,			a__=0   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = []
						for old_item in old_list:
												lowerCAmelCase      :Union[str, Any]				  = old_item
												lowerCAmelCase      :str				  = new_item.replace('block.'    ,			'resnets.'   )
												lowerCAmelCase      :int				  = new_item.replace('conv_shorcut'    ,			'conv1'   )
												lowerCAmelCase      :int				  = new_item.replace('in_shortcut'    ,			'conv_shortcut'   )
												lowerCAmelCase      :Union[str, Any]				  = new_item.replace('temb_proj'    ,			'time_emb_proj'   )

												lowerCAmelCase      :List[str]				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )

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

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__=0    ,			a__=False   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = []
						for old_item in old_list:
												lowerCAmelCase      :int				  = old_item

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

												lowerCAmelCase      :Optional[Any]				  = new_item.replace('proj_out'    ,			'proj_attn'   )
												lowerCAmelCase      :List[str]				  = new_item.replace('norm'    ,			'group_norm'   )

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

						return mapping


def 					UpperCAmelCase			(				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."

						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():
																		lowerCAmelCase      :Union[str, Any]				  = old_checkpoint[path]
																		lowerCAmelCase      :List[Any]				  = old_tensor.shape[0] // 3

																		lowerCAmelCase      :int				  = (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

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

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

																		lowerCAmelCase      :str				  = query.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Dict				  = key.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Union[str, Any]				  = value.reshape(a__   ).squeeze()

						for path in paths:
												lowerCAmelCase      :Union[str, Any]				  = path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase      :Dict				  = new_path.replace('down.'    ,			'down_blocks.'   )
												lowerCAmelCase      :int				  = new_path.replace('up.'    ,			'up_blocks.'   )

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

												if "attentions" in new_path:
																		lowerCAmelCase      :Tuple				  = old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase      :Optional[int]				  = old_checkpoint[path['old']]


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['temb.dense.0.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['temb.dense.0.bias']
						lowerCAmelCase      :str				  = checkpoint['temb.dense.1.weight']
						lowerCAmelCase      :int				  = checkpoint['temb.dense.1.bias']

						lowerCAmelCase      :Dict				  = checkpoint['norm_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['norm_out.bias']

						lowerCAmelCase      :Optional[int]				  = checkpoint['conv_in.weight']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_in.bias']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['conv_out.bias']

						lowerCAmelCase      :Tuple				  = len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :Dict				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

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

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

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

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

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

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Optional[Any]				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :str				  = [key for key in checkpoint if 'mid.attn_1' in key]

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

						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[Any]				  = num_up_blocks - 1 - i

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

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

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

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

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

						lowerCAmelCase      :Tuple				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						return new_checkpoint


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['encoder.norm_out.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['encoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['encoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['encoder.conv_in.bias']
						lowerCAmelCase      :Dict				  = checkpoint['encoder.conv_out.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['encoder.conv_out.bias']

						lowerCAmelCase      :Dict				  = checkpoint['decoder.norm_out.weight']
						lowerCAmelCase      :List[str]				  = checkpoint['decoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['decoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['decoder.conv_in.bias']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['decoder.conv_out.weight']
						lowerCAmelCase      :int				  = checkpoint['decoder.conv_out.bias']

						lowerCAmelCase      :List[str]				  = len({'.'.join(layer.split('.'   )[:3]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :int				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

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

						for i in range(a__   ):
												lowerCAmelCase      :Tuple				  = (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :List[Any]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.weight"""
																		]
																		lowerCAmelCase      :Optional[int]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.bias"""
																		]

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

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

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

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Any				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Tuple				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.attn_1' in key]

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

						lowerCAmelCase      :List[str]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[int]				  = num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in up_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Optional[Any]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(a__   )
																		}

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

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

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

						lowerCAmelCase      :Optional[Any]				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						lowerCAmelCase      :Optional[int]				  = checkpoint['quant_conv.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['quant_conv.bias']
						if "quantize.embedding.weight" in checkpoint:
												lowerCAmelCase      :Optional[Any]				  = checkpoint['quantize.embedding.weight']
						lowerCAmelCase      :Any				  = checkpoint['post_quant_conv.weight']
						lowerCAmelCase      :str				  = checkpoint['post_quant_conv.bias']

						return new_checkpoint


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

		__SCREAMING_SNAKE_CASE      =      parser.parse_args()
		__SCREAMING_SNAKE_CASE      =      torch.load(args.checkpoint_path)

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

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

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

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

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

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