





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 			lowerCamelCase_		(					lowerCAmelCase:		Any )->						Union[str, Any]:
  _snake_case      : Tuple														=		fname.split(os.path.sep )[-1]
  return re.search(R'^(.*)_\d+\.jpg$'			,							lowerCAmelCase ).groups()[0]



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  def __init__(					self					:    Any					, UpperCamelCase					:    str					, UpperCamelCase					:    Any=None					, UpperCamelCase					:    Dict=None							):


    '''simple docstring'''





    _snake_case      : str														=		file_names
    _snake_case      : Dict														=		image_transform
    _snake_case      : Optional[int]														=		label_to_id







  def __len__(					self					:    Union[str, Any]							):


    '''simple docstring'''





    return len(self.file_names							)







  def __getitem__(					self					:    Optional[Any]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Any														=		self.file_names[idx]
    _snake_case      : int														=		PIL.Image.open(UpperCamelCase							)
    _snake_case      : Dict														=		raw_image.convert('RGB'							)
    if self.image_transform is not None:
      _snake_case      : Dict														=		self.image_transform(UpperCamelCase							)
    _snake_case      : int														=		extract_label(UpperCamelCase							)
    if self.label_to_id is not None:
      _snake_case      : Dict														=		self.label_to_id[label]
    return {"image": image, "label": label}




def 			lowerCamelCase_		(					lowerCAmelCase:		Dict			,							lowerCAmelCase:		List[Any] )->						Union[str, Any]:
  # Initialize accelerator
  _snake_case      : Dict														=		Accelerator(cpu=args.cpu			,							mixed_precision=args.mixed_precision )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _snake_case      : List[Any]														=		config['lr']
  _snake_case      : Union[str, Any]														=		int(config['num_epochs'] )
  _snake_case      : Optional[Any]														=		int(config['seed'] )
  _snake_case      : int														=		int(config['batch_size'] )
  _snake_case      : List[Any]														=		config['image_size']
  if not isinstance(lowerCAmelCase			,							(list, tuple) ):
    _snake_case      : Dict														=		(image_size, image_size)

  # Grab all the image filenames
  _snake_case      : Optional[int]														=		[os.path.join(args.data_dir			,							lowerCAmelCase ) for fname in os.listdir(args.data_dir ) if fname.endswith('.jpg' )]

  # Build the label correspondences
  _snake_case      : Dict														=		[extract_label(lowerCAmelCase ) for fname in file_names]
  _snake_case      : int														=		list(set(lowerCAmelCase ) )
  id_to_label.sort()
  _snake_case      : List[str]														=		{lbl: i for i, lbl in enumerate(lowerCAmelCase )}

  # Set the seed before splitting the data.
  np.random.seed(lowerCAmelCase )
  torch.manual_seed(lowerCAmelCase )
  torch.cuda.manual_seed_all(lowerCAmelCase )

  # Split our filenames between train and validation
  _snake_case      : Tuple														=		np.random.permutation(len(lowerCAmelCase ) )
  _snake_case      : Dict														=		int(0.8 * len(lowerCAmelCase ) )
  _snake_case      : Optional[int]														=		random_perm[:cut]
  _snake_case      : Optional[Any]														=		random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _snake_case      : int														=		Compose([RandomResizedCrop(lowerCAmelCase			,							scale=(0.5, 1.0) ), ToTensor()] )
  _snake_case      : List[Any]														=		PetsDataset(
      [file_names[i] for i in train_split]			,							image_transform=lowerCAmelCase			,							label_to_id=lowerCAmelCase )

  # For evaluation, we use a deterministic Resize
  _snake_case      : Optional[Any]														=		Compose([Resize(lowerCAmelCase ), ToTensor()] )
  _snake_case      : List[Any]														=		PetsDataset([file_names[i] for i in eval_split]			,							image_transform=lowerCAmelCase			,							label_to_id=lowerCAmelCase )

  # Instantiate dataloaders.
  _snake_case      : Any														=		DataLoader(lowerCAmelCase			,							shuffle=lowerCAmelCase			,							batch_size=lowerCAmelCase			,							num_workers=4 )
  _snake_case      : int														=		DataLoader(lowerCAmelCase			,							shuffle=lowerCAmelCase			,							batch_size=lowerCAmelCase			,							num_workers=4 )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _snake_case      : Optional[Any]														=		create_model('resnet50d'			,							pretrained=lowerCAmelCase			,							num_classes=len(lowerCAmelCase ) )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _snake_case      : Optional[Any]														=		model.to(accelerator.device )

  # Freezing the base model
  for param in model.parameters():
    _snake_case      : str														=		False
  for param in model.get_classifier().parameters():
    _snake_case      : Optional[Any]														=		True

  # We normalize the batches of images to be a bit faster.
  _snake_case      : Optional[int]														=		torch.tensor(model.default_cfg['mean'] )[None, :, None, None].to(accelerator.device )
  _snake_case      : Union[str, Any]														=		torch.tensor(model.default_cfg['std'] )[None, :, None, None].to(accelerator.device )

  # Instantiate optimizer
  _snake_case      : Tuple														=		torch.optim.Adam(params=model.parameters()			,							lr=lr / 25 )

  # Instantiate learning rate scheduler
  _snake_case      : Union[str, Any]														=		OneCycleLR(optimizer=lowerCAmelCase			,							max_lr=lowerCAmelCase			,							epochs=lowerCAmelCase			,							steps_per_epoch=len(lowerCAmelCase ) )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case      : Any														=		accelerator.prepare(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  # Now we train the model
  for epoch in range(lowerCAmelCase ):
    model.train()
    for step, batch in enumerate(lowerCAmelCase ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _snake_case      : Tuple														=		{k: v.to(accelerator.device ) for k, v in batch.items()}
      _snake_case      : Any														=		(batch['image'] - mean) / std
      _snake_case      : Dict														=		model(lowerCAmelCase )
      _snake_case      : Dict														=		torch.nn.functional.cross_entropy(lowerCAmelCase			,							batch['label'] )
      accelerator.backward(lowerCAmelCase )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()

    model.eval()
    _snake_case      : Optional[int]														=		0
    _snake_case      : Tuple														=		0
    for _, batch in enumerate(lowerCAmelCase ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _snake_case      : Optional[int]														=		{k: v.to(accelerator.device ) for k, v in batch.items()}
      _snake_case      : Any														=		(batch['image'] - mean) / std
      with torch.no_grad():
        _snake_case      : int														=		model(lowerCAmelCase )
      _snake_case      : Optional[int]														=		outputs.argmax(dim=-1 )
      _snake_case							,    _snake_case      : List[Any]														=		accelerator.gather_for_metrics((predictions, batch['label']) )
      _snake_case      : Union[str, Any]														=		predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    _snake_case      : List[str]														=		accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"""epoch {epoch}: {1_00 * eval_metric:.2f}""" )


def 			lowerCamelCase_		(					)->						Dict:
  _snake_case      : List[Any]														=		argparse.ArgumentParser(description='Simple example of training script.' )
  parser.add_argument('--data_dir'			,							required=lowerCAmelCase			,							help='The data folder on disk.' )
  parser.add_argument(
      '--mixed_precision'			,							type=lowerCAmelCase			,							default=lowerCAmelCase			,							choices=['no', 'fp16', 'bf16', 'fp8']			,							help='Whether to use mixed precision. Choose'
      'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
      'and an Nvidia Ampere GPU.'			,							)
  parser.add_argument(
      '--checkpointing_steps'			,							type=lowerCAmelCase			,							default=lowerCAmelCase			,							help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'			,							)
  parser.add_argument('--cpu'			,							action='store_true'			,							help='If passed, will train on the CPU.' )
  _snake_case      : Optional[Any]														=		parser.parse_args()
  _snake_case      : Union[str, Any]														=		{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
  training_function(lowerCAmelCase			,							lowerCAmelCase )


if __name__ == "__main__":
      main()











import argparse

import torch

from diffusers import UNetaDConditionModel


def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str			,							lowerCAmelCase:		Optional[Any]=None			,							lowerCAmelCase:		List[Any]=None			,							lowerCAmelCase:		Optional[int]=None )->						int:
  assert isinstance(lowerCAmelCase			,							lowerCAmelCase ), "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():
      _snake_case      : Any														=		old_checkpoint[path]
      _snake_case      : Dict														=		old_tensor.shape[0] // 3

      _snake_case      : Dict														=		(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

      _snake_case      : Union[str, Any]														=		old_tensor.shape[0] // config['num_head_channels'] // 3

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

      _snake_case      : List[Any]														=		query.reshape(lowerCAmelCase )
      _snake_case      : int														=		key.reshape(lowerCAmelCase )
      _snake_case      : Union[str, Any]														=		value.reshape(lowerCAmelCase )

  for path in paths:
    _snake_case      : str														=		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:
        _snake_case      : Dict														=		new_path.replace(replacement['old']			,							replacement['new'] )

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


def 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		List[Any]=0 )->						Optional[int]:
  _snake_case      : Any														=		[]
  for old_item in old_list:
    _snake_case      : str														=		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 			lowerCamelCase_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Tuple=1 )->						Union[str, Any]:
  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_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Optional[Any]=0 )->						List[Any]:
  _snake_case      : Optional[Any]														=		[]
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str=0 )->						str:
  _snake_case      : int														=		[]
  for old_item in old_list:
    _snake_case      : List[Any]														=		old_item.replace('in_layers.0'			,							'norm1' )
    _snake_case      : Any														=		new_item.replace('in_layers.2'			,							'conv1' )

    _snake_case      : List[Any]														=		new_item.replace('out_layers.0'			,							'norm2' )
    _snake_case      : List[str]														=		new_item.replace('out_layers.3'			,							'conv2' )

    _snake_case      : int														=		new_item.replace('emb_layers.1'			,							'time_emb_proj' )
    _snake_case      : Dict														=		new_item.replace('skip_connection'			,							'conv_shortcut' )

    _snake_case      : int														=		shave_segments(lowerCAmelCase			,							n_shave_prefix_segments=lowerCAmelCase )

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

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[int]			,							lowerCAmelCase:		Optional[int]=None			,							lowerCAmelCase:		Dict=False )->						Optional[Any]:
  _snake_case      : Union[str, Any]														=		{}
  _snake_case      : Union[str, Any]														=		list(checkpoint.keys() )

  _snake_case      : Optional[int]														=		'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_00 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' ):
        _snake_case      : Optional[int]														=		'model_ema.' + ''.join(key.split('.' )[1:] )
        _snake_case      : Any														=		checkpoint.pop(lowerCAmelCase )
  else:
    if sum(k.startswith('model_ema' ) for k in keys ) > 1_00:
      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:
      _snake_case      : str														=		checkpoint.pop(lowerCAmelCase )

  _snake_case      : Union[str, Any]														=		{}

  _snake_case      : Any														=		unet_state_dict['time_embed.0.weight']
  _snake_case      : Optional[Any]														=		unet_state_dict['time_embed.0.bias']
  _snake_case      : str														=		unet_state_dict['time_embed.2.weight']
  _snake_case      : Dict														=		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":
    _snake_case      : str														=		unet_state_dict['label_emb.0.0.weight']
    _snake_case      : Tuple														=		unet_state_dict['label_emb.0.0.bias']
    _snake_case      : str														=		unet_state_dict['label_emb.0.2.weight']
    _snake_case      : Optional[Any]														=		unet_state_dict['label_emb.0.2.bias']
  else:
    raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}""" )

  _snake_case      : List[str]														=		unet_state_dict['input_blocks.0.0.weight']
  _snake_case      : Tuple														=		unet_state_dict['input_blocks.0.0.bias']

  _snake_case      : List[Any]														=		[v for v in unet_state_dict if v.startswith('input_blocks.0.1' )]
  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : str														=		{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Optional[int]														=		unet_state_dict['out.0.weight']
  _snake_case      : Union[str, Any]														=		unet_state_dict['out.0.bias']
  _snake_case      : Any														=		unet_state_dict['out.2.weight']
  _snake_case      : int														=		unet_state_dict['out.2.bias']

  # Retrieves the keys for the input blocks only
  _snake_case      : Union[str, Any]														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'input_blocks' in layer} )
  _snake_case      : List[str]														=		{
      layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

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

  # Retrieves the keys for the output blocks only
  _snake_case      : Optional[int]														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'output_blocks' in layer} )
  _snake_case      : Tuple														=		{
      layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

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

    _snake_case      : str														=		[
        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
    ]
    _snake_case      : Optional[int]														=		[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
    _snake_case      : Union[str, Any]														=		[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:
      _snake_case      : str														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.weight""" )
      _snake_case      : Optional[int]														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.bias""" )

    _snake_case      : Optional[Any]														=		renew_resnet_paths(lowerCAmelCase )
    _snake_case      : Union[str, Any]														=		{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    assign_to_checkpoint(
        lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    _snake_case      : List[Any]														=		[key for key in resnets if 'temopral_conv' in key]
    _snake_case      : Union[str, Any]														=		renew_temp_conv_paths(lowerCAmelCase )
    _snake_case      : List[str]														=		{
        'old': F"""input_blocks.{i}.0.temopral_conv""",
        'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
    }
    assign_to_checkpoint(
        lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    if len(lowerCAmelCase ):
      _snake_case      : List[Any]														=		renew_attention_paths(lowerCAmelCase )
      _snake_case      : str														=		{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    if len(lowerCAmelCase ):
      _snake_case      : Tuple														=		renew_attention_paths(lowerCAmelCase )
      _snake_case      : Optional[int]														=		{
          'old': F"""input_blocks.{i}.2""",
          'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Tuple														=		middle_blocks[0]
  _snake_case      : Union[str, Any]														=		[key for key in resnet_a if 'temopral_conv' in key]
  _snake_case      : Optional[Any]														=		middle_blocks[1]
  _snake_case      : List[str]														=		middle_blocks[2]
  _snake_case      : List[str]														=		middle_blocks[3]
  _snake_case      : Tuple														=		[key for key in resnet_a if 'temopral_conv' in key]

  _snake_case      : str														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : Dict														=		{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : Tuple														=		renew_temp_conv_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : List[Any]														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : List[str]														=		{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : Any														=		renew_temp_conv_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : List[Any]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : int														=		{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  for i in range(lowerCAmelCase ):
    _snake_case      : Union[str, Any]														=		i // (config['layers_per_block'] + 1)
    _snake_case      : Optional[int]														=		i % (config['layers_per_block'] + 1)
    _snake_case      : Dict														=		[shave_segments(lowerCAmelCase			,							2 ) for name in output_blocks[i]]
    _snake_case      : Optional[Any]														=		{}

    for layer in output_block_layers:
      _snake_case							,    _snake_case      : str														=		layer.split('.' )[0], shave_segments(lowerCAmelCase			,							1 )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase )
      else:
        _snake_case      : Union[str, Any]														=		[layer_name]

    if len(lowerCAmelCase ) > 1:
      _snake_case      : Union[str, Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
      _snake_case      : Optional[Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
      _snake_case      : int														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

      _snake_case      : Tuple														=		renew_resnet_paths(lowerCAmelCase )
      _snake_case      : int														=		renew_resnet_paths(lowerCAmelCase )

      _snake_case      : Optional[int]														=		{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      _snake_case      : Union[str, Any]														=		[key for key in resnets if 'temopral_conv' in key]
      _snake_case      : Dict														=		renew_temp_conv_paths(lowerCAmelCase )
      _snake_case      : int														=		{
          'old': F"""output_blocks.{i}.0.temopral_conv""",
          'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      _snake_case      : Dict														=		{k: sorted(lowerCAmelCase ) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        _snake_case      : List[Any]														=		list(output_block_list.values() ).index(['conv.bias', 'conv.weight'] )
        _snake_case      : Union[str, Any]														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.weight"""
        ]
        _snake_case      : Dict														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase ) == 2:
          _snake_case      : Optional[int]														=		[]

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

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

      _snake_case      : Optional[int]														=		[l for l in output_block_layers if 'temopral_conv' in l]
      for path in temopral_conv_paths:
        _snake_case      : Optional[Any]														=		path.split('temopral_conv.' )[-1]
        _snake_case      : str														=		'.'.join(['output_blocks', str(lowerCAmelCase ), str(lowerCAmelCase ), 'temopral_conv', pruned_path] )
        _snake_case      : Optional[int]														=		'.'.join(['up_blocks', str(lowerCAmelCase ), 'temp_convs', str(lowerCAmelCase ), pruned_path] )
        _snake_case      : Tuple														=		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 --




