
"""simple docstring"""

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					):

      '''simple docstring'''




      super().__init__()
      self.register_modules(transformer=__UpperCAmelCase     ,					vae=__UpperCAmelCase     ,					scheduler=__UpperCAmelCase				)

      # create a imagenet -> id dictionary for easier use
      __UpperCamelCase      =	{}
      if idalabel is not None:
       for key, value in idalabel.items():
        for label in value.split(','				):
         __UpperCamelCase      =	int(__UpperCAmelCase				)
       __UpperCamelCase      =	dict(sorted(self.labels.items()				)				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	list(__UpperCAmelCase				)

      for l in label:
       if l not in self.labels:
        raise ValueError(
            F'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'				)

      return [self.labels[l] for l in label]







     @torch.no_grad()
     def __call__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 4.0     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 50     ,					__UpperCAmelCase = "pil"     ,					__UpperCAmelCase = True     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	len(__UpperCAmelCase				)
      __UpperCamelCase      =	self.transformer.config.sample_size
      __UpperCamelCase      =	self.transformer.config.in_channels

      __UpperCamelCase      =	randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)     ,					generator=__UpperCAmelCase     ,					device=self.device     ,					dtype=self.transformer.dtype     ,					)
      __UpperCamelCase      =	torch.cat([latents] * 2				) if guidance_scale > 1 else latents

      __UpperCamelCase      =	torch.tensor(__UpperCAmelCase     ,					device=self.device				).reshape(-1				)
      __UpperCamelCase      =	torch.tensor([1000] * batch_size     ,					device=self.device				)
      __UpperCamelCase      =	torch.cat([class_labels, class_null]     ,					0				) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(__UpperCAmelCase				)

      for t in self.progress_bar(self.scheduler.timesteps				):
       if guidance_scale > 1:
        __UpperCamelCase      =	latent_model_input[: len(__UpperCAmelCase				) // 2]
        __UpperCamelCase      =	torch.cat([half, half]     ,					dim=0				)
       __UpperCamelCase      =	self.scheduler.scale_model_input(__UpperCAmelCase     ,					__UpperCAmelCase				)

       __UpperCamelCase      =	t
       if not torch.is_tensor(__UpperCAmelCase				):
        # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
        # This would be a good case for the `match` statement (Python 3.10+)
        __UpperCamelCase      =	latent_model_input.device.type == 'mps'
        if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
         __UpperCamelCase      =	torch.floataa if is_mps else torch.floataa
        else:
         __UpperCamelCase      =	torch.intaa if is_mps else torch.intaa
        __UpperCamelCase      =	torch.tensor([timesteps]     ,					dtype=__UpperCAmelCase     ,					device=latent_model_input.device				)
       elif len(timesteps.shape				) == 0:
        __UpperCamelCase      =	timesteps[None].to(latent_model_input.device				)
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       __UpperCamelCase      =	timesteps.expand(latent_model_input.shape[0]				)
       # predict noise model_output
       __UpperCamelCase      =	self.transformer(
           __UpperCAmelCase     ,					timestep=__UpperCAmelCase     ,					class_labels=__UpperCAmelCase				).sample

       # perform guidance
       if guidance_scale > 1:
        __UpperCamelCase  ,     __UpperCamelCase      =	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
        __UpperCamelCase  ,     __UpperCamelCase      =	torch.split(__UpperCAmelCase     ,					len(__UpperCAmelCase				) // 2     ,					dim=0				)

        __UpperCamelCase      =	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
        __UpperCamelCase      =	torch.cat([half_eps, half_eps]     ,					dim=0				)

        __UpperCamelCase      =	torch.cat([eps, rest]     ,					dim=1				)

       # learned sigma
       if self.transformer.config.out_channels // 2 == latent_channels:
        __UpperCamelCase  ,     __UpperCamelCase      =	torch.split(__UpperCAmelCase     ,					__UpperCAmelCase     ,					dim=1				)
       else:
        __UpperCamelCase      =	noise_pred

       # compute previous image: x_t -> x_t-1
       __UpperCamelCase      =	self.scheduler.step(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				).prev_sample

      if guidance_scale > 1:
       __UpperCamelCase  ,     __UpperCamelCase      =	latent_model_input.chunk(2     ,					dim=0				)
      else:
       __UpperCamelCase      =	latent_model_input

      __UpperCamelCase      =	1 / self.vae.config.scaling_factor * latents
      __UpperCamelCase      =	self.vae.decode(__UpperCAmelCase				).sample

      __UpperCamelCase      =	(samples / 2 + 0.5).clamp(0     ,					1				)

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      __UpperCamelCase      =	samples.cpu().permute(0     ,					2     ,					3     ,					1				).float().numpy()

      if output_type == "pil":
       __UpperCamelCase      =	self.numpy_to_pil(__UpperCAmelCase				)

      if not return_dict:
       return (samples,)

      return ImagePipelineOutput(images=__UpperCAmelCase				)





"""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)



