






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase      =				1_6
__UpperCAmelCase      =				3_2





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

 return int(x / 2**2_0			)
class 	a_:



     """simple docstring"""



     def __enter__(		self					:	Optional[int])							->	Tuple:

      """simple docstring"""
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		psutil.Process()

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		threading.Thread(target=self.peak_monitor_func)
      SCREAMING_SNAKE_CASE				    =		True
      peak_monitor_thread.start()
      return self





     def     __UpperCamelCase						(		self					:	Any)							->	List[str]:

      """simple docstring"""
      return self.process.memory_info().rss





     def     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		-1

      while True:
       SCREAMING_SNAKE_CASE				    =		max(self.cpu_mem_used()  ,  self.cpu_peak)

       # can't sleep or will not catch the peak right (this comment is here on purpose)
       # time.sleep(0.001) # 1msec

       if not self.peak_monitoring:
        break





     def __exit__(		self					:	int  ,  *lowerCAmelCase__					:	Tuple)							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False

      gc.collect()
      torch.cuda.empty_cache()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.max_memory_allocated()
      SCREAMING_SNAKE_CASE				    =		bamb(self.end - self.begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.peak - self.begin)

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_end - self.cpu_begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_peak - self.cpu_begin)
      # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __UpperCAmelCase      =				mocked_dataloaders  # noqa: F811





def 						A_					(						lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

 if os.environ.get('TESTING_MOCKED_DATALOADERS'	,  lowercase_			) == "1":
  SCREAMING_SNAKE_CASE				    =		2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 SCREAMING_SNAKE_CASE				    =		FullyShardedDataParallelPlugin(
     state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  )

 # Initialize accelerator
 if args.with_tracking:
  SCREAMING_SNAKE_CASE				    =		Accelerator(
      cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  log_with='wandb'	,  project_dir=args.logging_dir	,  fsdp_plugin=lowercase_	,  )
 else:
  SCREAMING_SNAKE_CASE				    =		Accelerator(fsdp_plugin=lowercase_			)
 accelerator.print(accelerator.distributed_type			)

 if hasattr(args.checkpointing_steps	,  'isdigit'			):
  if args.checkpointing_steps == "epoch":
   SCREAMING_SNAKE_CASE				    =		args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   SCREAMING_SNAKE_CASE				    =		int(args.checkpointing_steps			)
  else:
   raise ValueError(
       f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
 else:
  SCREAMING_SNAKE_CASE				    =		None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 SCREAMING_SNAKE_CASE				    =		config['lr']
 SCREAMING_SNAKE_CASE				    =		int(config['num_epochs']			)
 SCREAMING_SNAKE_CASE				    =		int(config['seed']			)
 SCREAMING_SNAKE_CASE				    =		int(config['batch_size']			)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  SCREAMING_SNAKE_CASE				    =		vars(lowercase_			)
  accelerator.init_trackers('fsdp_glue_no_trainer'	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained(args.model_name_or_path			)
 SCREAMING_SNAKE_CASE				    =		load_dataset('glue'	,  'mrpc'			)
 SCREAMING_SNAKE_CASE				    =		evaluate.load('glue'	,  'mrpc'			)

 def tokenize_function(lowercase_			):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE				    =		tokenizer(examples['sentence1']	,  examples['sentence2']	,  truncation=lowercase_	,  max_length=lowercase_			)
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  SCREAMING_SNAKE_CASE				    =		datasets.map(
      lowercase_	,  batched=lowercase_	,  remove_columns=['idx', 'sentence1', 'sentence2']	,  )

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 SCREAMING_SNAKE_CASE				    =		tokenized_datasets.rename_column('label'	,  'labels'			)

 # If the batch size is too big we use gradient accumulation
 SCREAMING_SNAKE_CASE				    =		1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  SCREAMING_SNAKE_CASE				    =		batch_size // MAX_GPU_BATCH_SIZE
  SCREAMING_SNAKE_CASE				    =		MAX_GPU_BATCH_SIZE

 def collate_fn(lowercase_			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  SCREAMING_SNAKE_CASE				    =		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   SCREAMING_SNAKE_CASE				    =		1_6
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE				    =		8
  else:
   SCREAMING_SNAKE_CASE				    =		None

  return tokenizer.pad(
      lowercase_	,  padding='longest'	,  max_length=lowercase_	,  pad_to_multiple_of=lowercase_	,  return_tensors='pt'	,  )

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE				    =		DataLoader(
     tokenized_datasets['train']	,  shuffle=lowercase_	,  collate_fn=lowercase_	,  batch_size=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		DataLoader(
     tokenized_datasets['validation']	,  shuffle=lowercase_	,  collate_fn=lowercase_	,  batch_size=lowercase_			)

 set_seed(lowercase_			)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 SCREAMING_SNAKE_CASE				    =		AutoModelForSequenceClassification.from_pretrained(
     args.model_name_or_path	,  return_dict=lowercase_	,  low_cpu_mem_usage=lowercase_			)

 # New Code #
 # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
 SCREAMING_SNAKE_CASE				    =		accelerator.prepare(lowercase_			)
 accelerator.print(lowercase_			)

 # Instantiate optimizer
 # New Code #
 # For FSDP feature, at present it doesn't support multiple parameter groups,
 # so we need to create a single parameter group for the whole model
 SCREAMING_SNAKE_CASE				    =		torch.optim.AdamW(params=model.parameters()	,  lr=lowercase_	,  weight_decay=2e-4			)

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE				    =		get_linear_schedule_with_warmup(
     optimizer=lowercase_	,  num_warmup_steps=1_0	,  num_training_steps=(len(lowercase_			) * num_epochs) // gradient_accumulation_steps	,  )

 # New Code #
 # For FSDP feature, prepare everything except the model as we have already prepared the model
 # before creating the optimizer
 # 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.
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		accelerator.prepare(
     lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
   accelerator.load_state(args.resume_from_checkpoint			)
   SCREAMING_SNAKE_CASE				    =		os.path.basename(args.resume_from_checkpoint			)
  else:
   # Get the most recent checkpoint
   SCREAMING_SNAKE_CASE				    =		[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
   dirs.sort(key=os.path.getctime			)
   SCREAMING_SNAKE_CASE				    =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  SCREAMING_SNAKE_CASE				    =		os.path.splitext(lowercase_			)[0]

  if "epoch" in training_difference:
   num_epochs -= int(training_difference.replace('epoch_'	,  ''			)			)
   SCREAMING_SNAKE_CASE				    =		None
  else:
   SCREAMING_SNAKE_CASE				    =		int(training_difference.replace('step_'	,  ''			)			)
   num_epochs -= resume_step // len(lowercase_			)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   SCREAMING_SNAKE_CASE				    =		(num_epochs * len(lowercase_			)) - resume_step

    # Now we train the model
 for epoch in range(lowercase_			):
  # New Code #
  # context manager to track the peak memory usage during the training epoch
  with TorchTracemalloc() as tracemalloc:
   model.train()
   if args.with_tracking:
    SCREAMING_SNAKE_CASE				    =		0
   for step, batch in enumerate(lowercase_			):
    # We need to skip steps until we reach the resumed step
    if args.resume_from_checkpoint and epoch == 0:
     if resume_step is not None and step < resume_step:
      pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device			)
    SCREAMING_SNAKE_CASE				    =		model(**lowercase_			)
    SCREAMING_SNAKE_CASE				    =		outputs.loss
    SCREAMING_SNAKE_CASE				    =		loss / gradient_accumulation_steps
    # We keep track of the loss at each epoch
    if args.with_tracking:
     total_loss += loss.detach().float()
    accelerator.backward(lowercase_			)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # accelerator.print(lr_scheduler.get_lr())

    overall_step += 1

    if isinstance(lowercase_	,  lowercase_			):
     SCREAMING_SNAKE_CASE				    =		f'''step_{overall_step}'''
     if overall_step % checkpointing_steps == 0:
      if args.output_dir is not None:
       SCREAMING_SNAKE_CASE				    =		os.path.join(args.output_dir	,  lowercase_			)
      accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the train (max): {}'.format(
          tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

  # New Code #
  # context manager to track the peak memory usage during the evaluation
  with TorchTracemalloc() as tracemalloc:
   model.eval()
   for step, batch in enumerate(lowercase_			):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device			)
    with torch.no_grad():
     SCREAMING_SNAKE_CASE				    =		model(**lowercase_			)
    SCREAMING_SNAKE_CASE				    =		outputs.logits.argmax(dim=-1			)
    SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		accelerator.gather_for_metrics((predictions, batch['labels'])			)
    metric.add_batch(
        predictions=lowercase_	,  references=lowercase_	,  )

   SCREAMING_SNAKE_CASE				    =		metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f'''epoch {epoch}:'''	,  lowercase_			)
   if args.with_tracking:
    accelerator.log(
        {
            'accuracy': eval_metric['accuracy'],
            'f1': eval_metric['f1'],
            'train_loss': total_loss.item() / len(lowercase_			),
        }	,  step=lowercase_	,  )

   if checkpointing_steps == "epoch":
    SCREAMING_SNAKE_CASE				    =		f'''epoch_{epoch}'''
    if args.output_dir is not None:
     SCREAMING_SNAKE_CASE				    =		os.path.join(args.output_dir	,  lowercase_			)
    accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

 if args.with_tracking:
  accelerator.end_training()





def 						A_					(						)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		argparse.ArgumentParser(description='Simple example of training script.'			)
 parser.add_argument(
     '--mixed_precision'	,  type=lowercase_	,  default=lowercase_	,  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('--cpu'	,  action='store_true'	,  help='If passed, will train on the CPU.'			)
 parser.add_argument(
     '--checkpointing_steps'	,  type=lowercase_	,  default=lowercase_	,  help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	,  )
 parser.add_argument(
     '--resume_from_checkpoint'	,  type=lowercase_	,  default=lowercase_	,  help='If the training should continue from a checkpoint folder.'	,  )
 parser.add_argument(
     '--with_tracking'	,  action='store_true'	,  help='Whether to load in all available experiment trackers from the environment and use them for logging.'	,  )
 parser.add_argument(
     '--output_dir'	,  type=lowercase_	,  default='.'	,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,  )
 parser.add_argument(
     '--logging_dir'	,  type=lowercase_	,  default='logs'	,  help='Location on where to store experiment tracking logs`'	,  )
 parser.add_argument(
     '--model_name_or_path'	,  type=lowercase_	,  help='Path to pretrained model or model identifier from huggingface.co/models.'	,  required=lowercase_	,  )
 SCREAMING_SNAKE_CASE				    =		parser.parse_args()
 SCREAMING_SNAKE_CASE				    =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
 training_function(lowercase_	,  lowercase_			)


if __name__ == "__main__":
      main()







import argparse
import json

import torch

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







def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=1						)       ->       int:

 """simple docstring"""






 if n_shave_prefix_segments >= 0:
  return ".".join(path.split('.'						)[n_shave_prefix_segments:]						)
 else:
  return ".".join(path.split('.'						)[:n_shave_prefix_segments]						)







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     str=0						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = []
 for old_item in old_list:
  __lowerCAmelCase:  Optional[Any]        = old_item
  __lowerCAmelCase:  List[str]        = new_item.replace('block.'       ,   'resnets.'						)
  __lowerCAmelCase:  Dict        = new_item.replace('conv_shorcut'       ,   'conv1'						)
  __lowerCAmelCase:  Optional[Any]        = new_item.replace('in_shortcut'       ,   'conv_shortcut'						)
  __lowerCAmelCase:  str        = new_item.replace('temb_proj'       ,   'time_emb_proj'						)

  __lowerCAmelCase:  Any        = shave_segments(SCREAMING_SNAKE_CASE       ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE						)

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

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Dict=0       ,   SCREAMING_SNAKE_CASE						:     str=False						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  str        = []
 for old_item in old_list:
  __lowerCAmelCase:  Optional[int]        = old_item

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

  __lowerCAmelCase:  List[str]        = new_item.replace('proj_out'       ,   'proj_attn'						)
  __lowerCAmelCase:  Tuple        = new_item.replace('norm'       ,   'group_norm'						)

  __lowerCAmelCase:  int        = shave_segments(SCREAMING_SNAKE_CASE       ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE						)
  mapping.append({'old': old_item, 'new': new_item}						)

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Any=None       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]=None       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=None						)       ->       Union[str, Any]:

 """simple docstring"""






 assert isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						), "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:  List[Any]        = old_checkpoint[path]
   __lowerCAmelCase:  Tuple        = old_tensor.shape[0] // 3

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

   __lowerCAmelCase:  Any        = old_tensor.shape[0] // config.get('num_head_channels'       ,   1						) // 3

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

   __lowerCAmelCase:  Union[str, Any]        = query.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  Dict        = key.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  List[Any]        = value.reshape(SCREAMING_SNAKE_CASE						).squeeze()

 for path in paths:
  __lowerCAmelCase:  Optional[Any]        = path['new']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  __lowerCAmelCase:  Optional[int]        = new_path.replace('down.'       ,   'down_blocks.'						)
  __lowerCAmelCase:  List[Any]        = new_path.replace('up.'       ,   'up_blocks.'						)

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

  if "attentions" in new_path:
   __lowerCAmelCase:  Optional[Any]        = old_checkpoint[path['old']].squeeze()
  else:
   __lowerCAmelCase:  List[Any]        = old_checkpoint[path['old']]







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Tuple						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Tuple        = {}

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

 __lowerCAmelCase:  Tuple        = checkpoint['norm_out.weight']
 __lowerCAmelCase:  Dict        = checkpoint['norm_out.bias']

 __lowerCAmelCase:  Dict        = checkpoint['conv_in.weight']
 __lowerCAmelCase:  List[str]        = checkpoint['conv_in.bias']
 __lowerCAmelCase:  str        = checkpoint['conv_out.weight']
 __lowerCAmelCase:  Tuple        = checkpoint['conv_out.bias']

 __lowerCAmelCase:  List[Any]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'down' in layer}						)
 __lowerCAmelCase:  Union[str, Any]        = {
     layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
 }

 __lowerCAmelCase:  Optional[int]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'up' in layer}						)
 __lowerCAmelCase:  List[str]        = {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)}

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Any        = (i - 1) // (config['layers_per_block'] + 1)

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

   if num_blocks > 0:
    for j in range(config['layers_per_block']						):
     __lowerCAmelCase:  int        = renew_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  if any('attn' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Optional[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Union[str, Any]        = {
       layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

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

 __lowerCAmelCase:  Union[str, Any]        = [key for key in checkpoint if 'mid.block_1' in key]
 __lowerCAmelCase:  List[Any]        = [key for key in checkpoint if 'mid.block_2' in key]
 __lowerCAmelCase:  Optional[Any]        = [key for key in checkpoint if 'mid.attn_1' in key]

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

 __lowerCAmelCase:  int        = renew_resnet_paths(SCREAMING_SNAKE_CASE						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,   )

 __lowerCAmelCase:  Tuple        = renew_attention_paths(SCREAMING_SNAKE_CASE       ,   in_mid=SCREAMING_SNAKE_CASE						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,   )

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Dict        = num_up_blocks - 1 - i

  if any('upsample' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Tuple        = 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[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Tuple        = {
       layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_blocks > 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_resnet_paths(blocks[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[replace_indices]						)

  if any('attn' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  str        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Optional[Any]        = {
       layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_attn > 0:
    for j in range(config['layers_per_block'] + 1						):
     __lowerCAmelCase:  str        = {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
     __lowerCAmelCase:  Dict        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[replace_indices]						)

 __lowerCAmelCase:  Union[str, Any]        = {k.replace('mid_new_2'       ,   'mid_block'						): v for k, v in new_checkpoint.items()}
 return new_checkpoint







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  int        = {}

 __lowerCAmelCase:  List[Any]        = checkpoint['encoder.norm_out.weight']
 __lowerCAmelCase:  str        = checkpoint['encoder.norm_out.bias']

 __lowerCAmelCase:  Any        = checkpoint['encoder.conv_in.weight']
 __lowerCAmelCase:  str        = checkpoint['encoder.conv_in.bias']
 __lowerCAmelCase:  str        = checkpoint['encoder.conv_out.weight']
 __lowerCAmelCase:  List[str]        = checkpoint['encoder.conv_out.bias']

 __lowerCAmelCase:  Tuple        = checkpoint['decoder.norm_out.weight']
 __lowerCAmelCase:  int        = checkpoint['decoder.norm_out.bias']

 __lowerCAmelCase:  Optional[int]        = checkpoint['decoder.conv_in.weight']
 __lowerCAmelCase:  List[Any]        = checkpoint['decoder.conv_in.bias']
 __lowerCAmelCase:  int        = checkpoint['decoder.conv_out.weight']
 __lowerCAmelCase:  Tuple        = checkpoint['decoder.conv_out.bias']

 __lowerCAmelCase:  Optional[int]        = 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(SCREAMING_SNAKE_CASE						)
 }

 __lowerCAmelCase:  str        = len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'up' in layer}						)
 __lowerCAmelCase:  Optional[int]        = {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)}

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  List[str]        = (i - 1) // (config['layers_per_block'] + 1)

  if any('downsample' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Tuple        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.weight'''
   ]
   __lowerCAmelCase:  Any        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any('block' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Tuple        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  List[str]        = {
       layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

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

  if any('attn' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  List[str]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Optional[int]        = {
       layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

   if num_attn > 0:
    for j in range(config['layers_per_block']						):
     __lowerCAmelCase:  int        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE						)

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

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

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

 __lowerCAmelCase:  Any        = renew_attention_paths(SCREAMING_SNAKE_CASE       ,   in_mid=SCREAMING_SNAKE_CASE						)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,   )

 for i in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Dict        = num_up_blocks - 1 - i

  if any('upsample' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  List[Any]        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.weight'''
   ]
   __lowerCAmelCase:  Dict        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.bias'''
   ]

  if any('block' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Tuple        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Optional[int]        = {
       layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

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

  if any('attn' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Any        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Dict        = {
       layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
   }

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

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

 return new_checkpoint


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

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

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

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

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

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

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

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





