
"""simple docstring"""

from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Any    , snake_case				:List[Any]    , snake_case				:Tuple		)				->     List[Any]:
 for param, grad_param in zip(model_a.parameters()    , model_b.parameters()		):
  if not param.requires_grad:
   continue
  if not did_step:
   # Grads should not be in sync
   assert (
       torch.allclose(param.grad    , grad_param.grad		) is False
   ), f'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
  else:
   # Grads should be in sync
   assert (
       torch.allclose(param.grad    , grad_param.grad		) is True
   ), f'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'






def 							A						(    snake_case				:List[str]    , snake_case				:Optional[int]    , snake_case				:Tuple    , snake_case				:List[str]    , snake_case				:int=True		)				->     Dict:
 model.train()
 __UpperCamelCase      =	model(snake_case		)
 __UpperCamelCase      =	F.mse_loss(snake_case    , target.to(output.device		)		)
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(snake_case		)






def 							A						(    snake_case				:int    , snake_case				:Optional[Any]=False		)				->     List[str]:
 set_seed(4_2		)
 __UpperCamelCase      =	RegressionModel()
 __UpperCamelCase      =	deepcopy(snake_case		)
 __UpperCamelCase      =	RegressionDataset(length=8_0		)
 __UpperCamelCase      =	DataLoader(snake_case    , batch_size=1_6		)
 model.to(accelerator.device		)
 if sched:
  __UpperCamelCase      =	AdamW(params=model.parameters()    , lr=1e-3		)
  __UpperCamelCase      =	AdamW(params=ddp_model.parameters()    , lr=1e-3		)
  __UpperCamelCase      =	LambdaLR(snake_case    , lr_lambda=lambda snake_case		: epoch**0.65		)
  __UpperCamelCase      =	LambdaLR(snake_case    , lr_lambda=lambda snake_case		: epoch**0.65		)
 # Make a copy of `model`
 if sched:
  __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.prepare(snake_case    , snake_case    , snake_case    , snake_case		)
 else:
  __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.prepare(snake_case    , snake_case		)
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader






def 							A						(    snake_case				:Union[str, Any]		)				->     str:
 # Test when on a single CPU or GPU that the context manager does nothing
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	get_training_setup(snake_case		)
 # Use a single batch
 __UpperCamelCase  ,     __UpperCamelCase      =	next(iter(snake_case		)		).values()
 for iteration in range(3		):
  # Gather the distributed inputs and targs for the base model
  __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.gather((ddp_input, ddp_target)		)
  __UpperCamelCase  ,     __UpperCamelCase      =	input.to(accelerator.device		), target.to(accelerator.device		)
  # Perform our initial ground truth step in non "DDP"
  step_model(snake_case    , snake_case    , snake_case    , snake_case		)
  # Do "gradient accumulation" (noop)
  if iteration % 2 == 0:
   # Accumulate grads locally
   with accelerator.no_sync(snake_case		):
    step_model(snake_case    , snake_case    , snake_case    , snake_case		)
  else:
   # Sync grads
   step_model(snake_case    , snake_case    , snake_case    , snake_case		)

  # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
  check_model_parameters(snake_case    , snake_case    , snake_case    , snake_case		)
  for param, ddp_param in zip(model.parameters()    , ddp_model.parameters()		):
   if not param.requires_grad:
    continue
   assert torch.allclose(
       param.grad    , ddp_param.grad		), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

  # Shuffle ddp_input on each iteration
  torch.manual_seed(1_3_3_7 + iteration		)
  __UpperCamelCase      =	ddp_input[torch.randperm(len(snake_case		)		)]






def 							A						(    snake_case				:Optional[int]		)				->     Tuple:
 # Test on distributed setup that context manager behaves properly
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	get_training_setup(snake_case		)
 # Use a single batch
 __UpperCamelCase  ,     __UpperCamelCase      =	next(iter(snake_case		)		).values()
 for iteration in range(3		):
  # Gather the distributed inputs and targs for the base model
  __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.gather((ddp_input, ddp_target)		)
  __UpperCamelCase  ,     __UpperCamelCase      =	input.to(accelerator.device		), target.to(accelerator.device		)
  # Perform our initial ground truth step in non "DDP"
  step_model(snake_case    , snake_case    , snake_case    , snake_case		)
  # Do "gradient accumulation" (noop)
  if iteration % 2 == 0:
   # Accumulate grads locally
   with accelerator.no_sync(snake_case		):
    step_model(snake_case    , snake_case    , snake_case    , snake_case		)
  else:
   # Sync grads
   step_model(snake_case    , snake_case    , snake_case    , snake_case		)

  # DDP model and model should only be in sync when not (iteration % 2 == 0)
  for param, ddp_param in zip(model.parameters()    , ddp_model.parameters()		):
   if not param.requires_grad:
    continue
   if iteration % 2 == 0:
    # Grads should not be in sync
    assert (
        torch.allclose(param.grad    , ddp_param.grad		) is False
    ), f'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
   else:
    # Grads should be in sync
    assert (
        torch.allclose(param.grad    , ddp_param.grad		) is True
    ), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
  torch.manual_seed(1_3_3_7 + iteration		)
  __UpperCamelCase      =	ddp_input[torch.randperm(len(snake_case		)		)]






def 							A						(    snake_case				:Optional[Any]=False    , snake_case				:Optional[Any]=False		)				->     List[Any]:
 __UpperCamelCase      =	Accelerator(
     split_batches=snake_case    , dispatch_batches=snake_case    , gradient_accumulation_steps=2		)
 # Test that context manager behaves properly
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	get_training_setup(snake_case		)
 for iteration, batch in enumerate(snake_case		):
  __UpperCamelCase  ,     __UpperCamelCase      =	batch.values()
  # Gather the distributed inputs and targs for the base model
  __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.gather((ddp_input, ddp_target)		)
  __UpperCamelCase  ,     __UpperCamelCase      =	input.to(accelerator.device		), target.to(accelerator.device		)
  # Perform our initial ground truth step in non "DDP"
  step_model(snake_case    , snake_case    , snake_case    , snake_case    , snake_case		)
  # Do "gradient accumulation" (noop)
  with accelerator.accumulate(snake_case		):
   step_model(snake_case    , snake_case    , snake_case    , snake_case		)

  # DDP model and model should only be in sync when not (iteration % 2 == 0)
  for param, ddp_param in zip(model.parameters()    , ddp_model.parameters()		):
   if not param.requires_grad:
    continue
   if ((iteration + 1) % 2 == 0) or (iteration == len(snake_case		) - 1):
    # Grads should be in sync
    assert (
        torch.allclose(param.grad    , ddp_param.grad		) is True
    ), f'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
   else:
    # Grads should not be in sync
    assert (
        torch.allclose(param.grad    , ddp_param.grad		) is False
    ), f'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
  torch.manual_seed(1_3_3_7 + iteration		)
  __UpperCamelCase      =	ddp_input[torch.randperm(len(snake_case		)		)]
 GradientState._reset_state()






def 							A						(    snake_case				:int=False    , snake_case				:Tuple=False		)				->     str:
 __UpperCamelCase      =	Accelerator(
     split_batches=snake_case    , dispatch_batches=snake_case    , gradient_accumulation_steps=2		)
 # Test that context manager behaves properly
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	get_training_setup(snake_case    , snake_case		)
 for iteration, batch in enumerate(snake_case		):
  __UpperCamelCase  ,     __UpperCamelCase      =	batch.values()
  # Gather the distributed inputs and targs for the base model
  __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.gather((ddp_input, ddp_target)		)
  __UpperCamelCase  ,     __UpperCamelCase      =	input.to(accelerator.device		), target.to(accelerator.device		)
  # Perform our initial ground truth step in non "DDP"
  model.train()
  ddp_model.train()
  step_model(snake_case    , snake_case    , snake_case    , snake_case    , snake_case		)
  opt.step()

  if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case		)):
   if split_batches:
    sched.step()
   else:
    for _ in range(accelerator.num_processes		):
     sched.step()
  opt.zero_grad()
  # Perform gradient accumulation under wrapper
  with accelerator.accumulate(snake_case		):
   step_model(snake_case    , snake_case    , snake_case    , snake_case		)
   ddp_opt.step()
   ddp_sched.step()
   ddp_opt.zero_grad()

  # Learning rates should be the same
  assert (
      opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
  ), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
  __UpperCamelCase      =	(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case		))
  if accelerator.num_processes > 1:
   check_model_parameters(snake_case    , snake_case    , snake_case    , snake_case		)
  # Shuffle ddp_input on each iteration
  torch.manual_seed(1_3_3_7 + iteration		)
 GradientState._reset_state()






def 							A						(    )				->     str:
 __UpperCamelCase      =	Accelerator()

 __UpperCamelCase      =	RegressionDataset(length=8_0		)
 __UpperCamelCase      =	DataLoader(snake_case    , batch_size=1_6		)
 __UpperCamelCase      =	RegressionDataset(length=9_6		)
 __UpperCamelCase      =	DataLoader(snake_case    , batch_size=1_6		)
 __UpperCamelCase  ,     __UpperCamelCase      =	accelerator.prepare(snake_case    , snake_case		)
 assert accelerator.gradient_state.active_dataloader is None
 for iteration, _ in enumerate(snake_case		):
  assert id(accelerator.gradient_state.active_dataloader		) == id(snake_case		)
  if iteration < len(snake_case		) - 1:
   assert not accelerator.gradient_state.end_of_dataloader
   if iteration == 1:
    for batch_num, _ in enumerate(snake_case		):
     assert id(accelerator.gradient_state.active_dataloader		) == id(snake_case		)
     if batch_num < len(snake_case		) - 1:
      assert not accelerator.gradient_state.end_of_dataloader
     else:
      assert accelerator.gradient_state.end_of_dataloader
  else:
   assert accelerator.gradient_state.end_of_dataloader
 assert accelerator.gradient_state.active_dataloader is None






def 							A						(    )				->     Any:
 __UpperCamelCase      =	Accelerator()
 __UpperCamelCase      =	accelerator.state
 if state.local_process_index == 0:
  print('**Test `accumulate` gradient accumulation with dataloader break**'		)
 test_dataloader_break()
 if state.distributed_type == DistributedType.NO:
  if state.local_process_index == 0:
   print('**Test NOOP `no_sync` context manager**'		)
  test_noop_sync(snake_case		)
 if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
  if state.local_process_index == 0:
   print('**Test Distributed `no_sync` context manager**'		)
  test_distributed_sync(snake_case		)
 if state.distributed_type == DistributedType.MULTI_GPU:
  for split_batch in [True, False]:
   for dispatch_batches in [True, False]:
    if state.local_process_index == 0:
     print(
         '**Test `accumulate` gradient accumulation, '    , f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    , )
    test_gradient_accumulation(snake_case    , snake_case		)

    # Currently will break on torch 2.0 +, need to investigate why
 if is_torch_version('<'    , '2.0'		) or state.distributed_type == DistributedType.NO:
  if state.local_process_index == 0:
   print(
       '**Test `accumulate` gradient accumulation with optimizer and scheduler, '    , '`split_batches=False`, `dispatch_batches=False`**'    , )
  test_gradient_accumulation_with_opt_and_scheduler()
  if state.distributed_type == DistributedType.MULTI_GPU:
   for split_batch in [True, False]:
    for dispatch_batches in [True, False]:
     if not split_batch and not dispatch_batches:
      continue
     if state.local_process_index == 0:
      print(
          '**Test `accumulate` gradient accumulation with optimizer and scheduler, '    , f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    , )
     test_gradient_accumulation_with_opt_and_scheduler(snake_case    , snake_case		)






def 							A						(    snake_case				:int		)				->     Dict:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()





"""simple docstring"""

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 							A						(    snake_case				:List[Any]    , snake_case				:Dict=1		)				->     Optional[int]:
 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						(    snake_case				:Dict    , snake_case				:int=0		)				->     Optional[int]:
 __UpperCamelCase      =	[]
 for old_item in old_list:
  __UpperCamelCase      =	old_item.replace('in_layers.0'    , 'norm1'		)
  __UpperCamelCase      =	new_item.replace('in_layers.2'    , 'conv1'		)

  __UpperCamelCase      =	new_item.replace('out_layers.0'    , 'norm2'		)
  __UpperCamelCase      =	new_item.replace('out_layers.3'    , 'conv2'		)

  __UpperCamelCase      =	new_item.replace('emb_layers.1'    , 'time_emb_proj'		)
  __UpperCamelCase      =	new_item.replace('skip_connection'    , 'conv_shortcut'		)

  __UpperCamelCase      =	shave_segments(snake_case    , n_shave_prefix_segments=snake_case		)

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

 return mapping






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Tuple=0		)				->     Tuple:
 __UpperCamelCase      =	[]
 for old_item in old_list:
  __UpperCamelCase      =	old_item

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

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

  __UpperCamelCase      =	shave_segments(snake_case    , n_shave_prefix_segments=snake_case		)

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

 return mapping






def 							A						(    snake_case				:int    , snake_case				:List[str]    , snake_case				:List[str]    , snake_case				:Any=None    , snake_case				:Optional[int]=None    , snake_case				:Union[str, Any]=None		)				->     Optional[int]:
 assert isinstance(snake_case    , snake_case		), "Paths should be a list of dicts containing 'old' and 'new' keys."

 # Splits the attention layers into three variables.
 if attention_paths_to_split is not None:
  for path, path_map in attention_paths_to_split.items():
   __UpperCamelCase      =	old_checkpoint[path]
   __UpperCamelCase      =	old_tensor.shape[0] // 3

   __UpperCamelCase      =	(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

   __UpperCamelCase      =	old_tensor.shape[0] // config['num_head_channels'] // 3

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

   __UpperCamelCase      =	query.reshape(snake_case		)
   __UpperCamelCase      =	key.reshape(snake_case		)
   __UpperCamelCase      =	value.reshape(snake_case		)

 for path in paths:
  __UpperCamelCase      =	path['new']

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

  # Global renaming happens here
  __UpperCamelCase      =	new_path.replace('middle_block.0'    , 'mid_block.resnets.0'		)
  __UpperCamelCase      =	new_path.replace('middle_block.1'    , 'mid_block.attentions.0'		)
  __UpperCamelCase      =	new_path.replace('middle_block.2'    , 'mid_block.resnets.1'		)

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

        # proj_attn.weight has to be converted from conv 1D to linear
  if "proj_attn.weight" in new_path:
   __UpperCamelCase      =	old_checkpoint[path['old']][:, :, 0]
  else:
   __UpperCamelCase      =	old_checkpoint[path['old']]






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Dict		)				->     Optional[Any]:
 __UpperCamelCase      =	{}

 __UpperCamelCase      =	checkpoint['time_embed.0.weight']
 __UpperCamelCase      =	checkpoint['time_embed.0.bias']
 __UpperCamelCase      =	checkpoint['time_embed.2.weight']
 __UpperCamelCase      =	checkpoint['time_embed.2.bias']

 __UpperCamelCase      =	checkpoint['input_blocks.0.0.weight']
 __UpperCamelCase      =	checkpoint['input_blocks.0.0.bias']

 __UpperCamelCase      =	checkpoint['out.0.weight']
 __UpperCamelCase      =	checkpoint['out.0.bias']
 __UpperCamelCase      =	checkpoint['out.2.weight']
 __UpperCamelCase      =	checkpoint['out.2.bias']

 # Retrieves the keys for the input blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'input_blocks' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 # Retrieves the keys for the middle blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'middle_block' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 # Retrieves the keys for the output blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'output_blocks' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 for i in range(1    , snake_case		):
  __UpperCamelCase      =	(i - 1) // (config['num_res_blocks'] + 1)
  __UpperCamelCase      =	(i - 1) % (config['num_res_blocks'] + 1)

  __UpperCamelCase      =	[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
  __UpperCamelCase      =	[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

  if f'input_blocks.{i}.0.op.weight' in checkpoint:
   __UpperCamelCase      =	checkpoint[
       f'input_blocks.{i}.0.op.weight'
   ]
   __UpperCamelCase      =	checkpoint[
       f'input_blocks.{i}.0.op.bias'
   ]
   continue

  __UpperCamelCase      =	renew_resnet_paths(snake_case		)
  __UpperCamelCase      =	{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
  __UpperCamelCase      =	{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
  assign_to_checkpoint(
      snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path, resnet_op]    , config=snake_case		)

  if len(snake_case		):
   __UpperCamelCase      =	renew_attention_paths(snake_case		)
   __UpperCamelCase      =	{
       'old': f'input_blocks.{i}.1',
       'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
   }
   __UpperCamelCase      =	{
       f'input_blocks.{i}.1.qkv.bias': {
           'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
           'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
           'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
       },
       f'input_blocks.{i}.1.qkv.weight': {
           'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
           'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
           'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
       },
   }
   assign_to_checkpoint(
       snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , attention_paths_to_split=snake_case    , config=snake_case    , )

 __UpperCamelCase      =	middle_blocks[0]
 __UpperCamelCase      =	middle_blocks[1]
 __UpperCamelCase      =	middle_blocks[2]

 __UpperCamelCase      =	renew_resnet_paths(snake_case		)
 assign_to_checkpoint(snake_case    , snake_case    , snake_case    , config=snake_case		)

 __UpperCamelCase      =	renew_resnet_paths(snake_case		)
 assign_to_checkpoint(snake_case    , snake_case    , snake_case    , config=snake_case		)

 __UpperCamelCase      =	renew_attention_paths(snake_case		)
 __UpperCamelCase      =	{
     'middle_block.1.qkv.bias': {
         'key': 'mid_block.attentions.0.key.bias',
         'query': 'mid_block.attentions.0.query.bias',
         'value': 'mid_block.attentions.0.value.bias',
     },
     'middle_block.1.qkv.weight': {
         'key': 'mid_block.attentions.0.key.weight',
         'query': 'mid_block.attentions.0.query.weight',
         'value': 'mid_block.attentions.0.value.weight',
     },
 }
 assign_to_checkpoint(
     snake_case    , snake_case    , snake_case    , attention_paths_to_split=snake_case    , config=snake_case		)

 for i in range(snake_case		):
  __UpperCamelCase      =	i // (config['num_res_blocks'] + 1)
  __UpperCamelCase      =	i % (config['num_res_blocks'] + 1)
  __UpperCamelCase      =	[shave_segments(snake_case    , 2		) for name in output_blocks[i]]
  __UpperCamelCase      =	{}

  for layer in output_block_layers:
   __UpperCamelCase  ,     __UpperCamelCase      =	layer.split('.'		)[0], shave_segments(snake_case    , 1		)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(snake_case		)
   else:
    __UpperCamelCase      =	[layer_name]

  if len(snake_case		) > 1:
   __UpperCamelCase      =	[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
   __UpperCamelCase      =	[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

   __UpperCamelCase      =	renew_resnet_paths(snake_case		)
   __UpperCamelCase      =	renew_resnet_paths(snake_case		)

   __UpperCamelCase      =	{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
   assign_to_checkpoint(snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , config=snake_case		)

   if ["conv.weight", "conv.bias"] in output_block_list.values():
    __UpperCamelCase      =	list(output_block_list.values()		).index(['conv.weight', 'conv.bias']		)
    __UpperCamelCase      =	checkpoint[
        f'output_blocks.{i}.{index}.conv.weight'
    ]
    __UpperCamelCase      =	checkpoint[
        f'output_blocks.{i}.{index}.conv.bias'
    ]

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

   if len(snake_case		):
    __UpperCamelCase      =	renew_attention_paths(snake_case		)
    __UpperCamelCase      =	{
        'old': f'output_blocks.{i}.1',
        'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
    }
    __UpperCamelCase      =	{
        f'output_blocks.{i}.1.qkv.bias': {
            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
        },
        f'output_blocks.{i}.1.qkv.weight': {
            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
        },
    }
    assign_to_checkpoint(
        snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , attention_paths_to_split=to_split if any('qkv' in key for key in attentions		) else None    , config=snake_case    , )
  else:
   __UpperCamelCase      =	renew_resnet_paths(snake_case    , n_shave_prefix_segments=1		)
   for path in resnet_0_paths:
    __UpperCamelCase      =	'.'.join(['output_blocks', str(snake_case		), path['old']]		)
    __UpperCamelCase      =	'.'.join(['up_blocks', str(snake_case		), 'resnets', str(snake_case		), path['new']]		)

    __UpperCamelCase      =	checkpoint[old_path]

 return new_checkpoint


if __name__ == "__main__":
      UpperCamelCase     :    Any       =			argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
      )

      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the architecture.",
      )

      parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

      UpperCamelCase     :    Dict       =			parser.parse_args()

      UpperCamelCase     :    Optional[Any]       =			torch.load(args.checkpoint_path)

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

      UpperCamelCase     :    Dict       =			convert_ldm_checkpoint(checkpoint, config)

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

      UpperCamelCase     :    Optional[Any]       =			UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            UpperCamelCase     :    List[Any]       =			DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
            UpperCamelCase     :    Optional[int]       =			VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

            UpperCamelCase     :    Dict       =			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)



