import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def       A__   (						snake_case_			:		Any							,					snake_case_			:		Union[str, Any]=1   ):
 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_			:		int							,					snake_case_			:		Union[str, Any]=0   ):
 SCREAMING_SNAKE_CASE__: Any=   []
 for old_item in old_list:
  SCREAMING_SNAKE_CASE__: Dict=   old_item.replace('''in_layers.0'''							,					'''norm1'''   )
  SCREAMING_SNAKE_CASE__: Any=   new_item.replace('''in_layers.2'''							,					'''conv1'''   )

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

  SCREAMING_SNAKE_CASE__: List[Any]=   new_item.replace('''emb_layers.1'''							,					'''time_emb_proj'''   )
  SCREAMING_SNAKE_CASE__: Optional[int]=   new_item.replace('''skip_connection'''							,					'''conv_shortcut'''   )

  SCREAMING_SNAKE_CASE__: Any=   shave_segments(_A							,					n_shave_prefix_segments=_A   )

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

 return mapping







def       A__   (						snake_case_			:		Dict							,					snake_case_			:		Any=0   ):
 SCREAMING_SNAKE_CASE__: Any=   []
 for old_item in old_list:
  SCREAMING_SNAKE_CASE__: List[Any]=   old_item

  SCREAMING_SNAKE_CASE__: Union[str, Any]=   new_item.replace('''norm.weight'''							,					'''group_norm.weight'''   )
  SCREAMING_SNAKE_CASE__: Tuple=   new_item.replace('''norm.bias'''							,					'''group_norm.bias'''   )

  SCREAMING_SNAKE_CASE__: int=   new_item.replace('''proj_out.weight'''							,					'''proj_attn.weight'''   )
  SCREAMING_SNAKE_CASE__: Tuple=   new_item.replace('''proj_out.bias'''							,					'''proj_attn.bias'''   )

  SCREAMING_SNAKE_CASE__: Dict=   shave_segments(_A							,					n_shave_prefix_segments=_A   )

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

 return mapping







def       A__   (						snake_case_			:		List[str]							,					snake_case_			:		str							,					snake_case_			:		str							,					snake_case_			:		Dict=None							,					snake_case_			:		str=None							,					snake_case_			:		Optional[int]=None   ):
 assert isinstance(_A							,					_A   ), "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():
   SCREAMING_SNAKE_CASE__: Optional[int]=   old_checkpoint[path]
   SCREAMING_SNAKE_CASE__: List[str]=   old_tensor.shape[0] // 3

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

   SCREAMING_SNAKE_CASE__: Tuple=   old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

   SCREAMING_SNAKE_CASE__: Union[str, Any]=   query.reshape(_A   )
   SCREAMING_SNAKE_CASE__: Union[str, Any]=   key.reshape(_A   )
   SCREAMING_SNAKE_CASE__: Any=   value.reshape(_A   )

 for path in paths:
  SCREAMING_SNAKE_CASE__: List[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

  # Global renaming happens here
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   new_path.replace('''middle_block.0'''							,					'''mid_block.resnets.0'''   )
  SCREAMING_SNAKE_CASE__: str=   new_path.replace('''middle_block.1'''							,					'''mid_block.attentions.0'''   )
  SCREAMING_SNAKE_CASE__: Any=   new_path.replace('''middle_block.2'''							,					'''mid_block.resnets.1'''   )

  if additional_replacements is not None:
   for replacement in additional_replacements:
    SCREAMING_SNAKE_CASE__: Optional[Any]=   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:
   SCREAMING_SNAKE_CASE__: Tuple=   old_checkpoint[path['''old''']][:, :, 0]
  else:
   SCREAMING_SNAKE_CASE__: List[str]=   old_checkpoint[path['''old''']]







def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		List[Any]   ):
 SCREAMING_SNAKE_CASE__: Tuple=   {}

 SCREAMING_SNAKE_CASE__: str=   checkpoint['''time_embed.0.weight''']
 SCREAMING_SNAKE_CASE__: Dict=   checkpoint['''time_embed.0.bias''']
 SCREAMING_SNAKE_CASE__: Optional[Any]=   checkpoint['''time_embed.2.weight''']
 SCREAMING_SNAKE_CASE__: int=   checkpoint['''time_embed.2.bias''']

 SCREAMING_SNAKE_CASE__: str=   checkpoint['''input_blocks.0.0.weight''']
 SCREAMING_SNAKE_CASE__: int=   checkpoint['''input_blocks.0.0.bias''']

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   checkpoint['''out.0.weight''']
 SCREAMING_SNAKE_CASE__: Dict=   checkpoint['''out.0.bias''']
 SCREAMING_SNAKE_CASE__: List[str]=   checkpoint['''out.2.weight''']
 SCREAMING_SNAKE_CASE__: Optional[Any]=   checkpoint['''out.2.bias''']

 # Retrieves the keys for the input blocks only
 SCREAMING_SNAKE_CASE__: Optional[int]=   len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''input_blocks''' in layer}   )
 SCREAMING_SNAKE_CASE__: Dict=   {
     layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
     for layer_id in range(_A   )
 }

 # Retrieves the keys for the middle blocks only
 SCREAMING_SNAKE_CASE__: str=   len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''middle_block''' in layer}   )
 SCREAMING_SNAKE_CASE__: Tuple=   {
     layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
     for layer_id in range(_A   )
 }

 # Retrieves the keys for the output blocks only
 SCREAMING_SNAKE_CASE__: List[str]=   len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''output_blocks''' in layer}   )
 SCREAMING_SNAKE_CASE__: str=   {
     layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
     for layer_id in range(_A   )
 }

 for i in range(1							,					_A   ):
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   (i - 1) // (config['''num_res_blocks'''] + 1)
  SCREAMING_SNAKE_CASE__: Optional[Any]=   (i - 1) % (config['''num_res_blocks'''] + 1)

  SCREAMING_SNAKE_CASE__: Dict=   [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
  SCREAMING_SNAKE_CASE__: Any=   [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:
   SCREAMING_SNAKE_CASE__: Tuple=   checkpoint[
       F'input_blocks.{i}.0.op.weight'
   ]
   SCREAMING_SNAKE_CASE__: List[Any]=   checkpoint[
       F'input_blocks.{i}.0.op.bias'
   ]
   continue

  SCREAMING_SNAKE_CASE__: Optional[Any]=   renew_resnet_paths(_A   )
  SCREAMING_SNAKE_CASE__: List[Any]=   {'''old''': F'input_blocks.{i}.0', '''new''': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
  SCREAMING_SNAKE_CASE__: int=   {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
  assign_to_checkpoint(
      _A							,					_A							,					_A							,					additional_replacements=[meta_path, resnet_op]							,					config=_A   )

  if len(_A   ):
   SCREAMING_SNAKE_CASE__: Optional[Any]=   renew_attention_paths(_A   )
   SCREAMING_SNAKE_CASE__: Union[str, Any]=   {
       '''old''': F'input_blocks.{i}.1',
       '''new''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
   }
   SCREAMING_SNAKE_CASE__: Any=   {
       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(
       _A							,					_A							,					_A							,					additional_replacements=[meta_path]							,					attention_paths_to_split=_A							,					config=_A							,					)

 SCREAMING_SNAKE_CASE__: Dict=   middle_blocks[0]
 SCREAMING_SNAKE_CASE__: int=   middle_blocks[1]
 SCREAMING_SNAKE_CASE__: Optional[int]=   middle_blocks[2]

 SCREAMING_SNAKE_CASE__: List[str]=   renew_resnet_paths(_A   )
 assign_to_checkpoint(_A							,					_A							,					_A							,					config=_A   )

 SCREAMING_SNAKE_CASE__: str=   renew_resnet_paths(_A   )
 assign_to_checkpoint(_A							,					_A							,					_A							,					config=_A   )

 SCREAMING_SNAKE_CASE__: Tuple=   renew_attention_paths(_A   )
 SCREAMING_SNAKE_CASE__: Optional[Any]=   {
     '''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(
     _A							,					_A							,					_A							,					attention_paths_to_split=_A							,					config=_A   )

 for i in range(_A   ):
  SCREAMING_SNAKE_CASE__: Any=   i // (config['''num_res_blocks'''] + 1)
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   i % (config['''num_res_blocks'''] + 1)
  SCREAMING_SNAKE_CASE__: str=   [shave_segments(_A							,					2   ) for name in output_blocks[i]]
  SCREAMING_SNAKE_CASE__: List[Any]=   {}

  for layer in output_block_layers:
   SCREAMING_SNAKE_CASE__: Any=   layer.split('''.'''   )[0], shave_segments(_A							,					1   )
   if layer_id in output_block_list:
    output_block_list[layer_id].append(_A   )
   else:
    SCREAMING_SNAKE_CASE__: Optional[Any]=   [layer_name]

  if len(_A   ) > 1:
   SCREAMING_SNAKE_CASE__: Optional[int]=   [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
   SCREAMING_SNAKE_CASE__: int=   [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

   SCREAMING_SNAKE_CASE__: Optional[Any]=   renew_resnet_paths(_A   )
   SCREAMING_SNAKE_CASE__: Any=   renew_resnet_paths(_A   )

   SCREAMING_SNAKE_CASE__: Optional[Any]=   {'''old''': F'output_blocks.{i}.0', '''new''': F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
   assign_to_checkpoint(_A							,					_A							,					_A							,					additional_replacements=[meta_path]							,					config=_A   )

   if ["conv.weight", "conv.bias"] in output_block_list.values():
    SCREAMING_SNAKE_CASE__: List[Any]=   list(output_block_list.values()   ).index(['''conv.weight''', '''conv.bias''']   )
    SCREAMING_SNAKE_CASE__: Optional[int]=   checkpoint[
        F'output_blocks.{i}.{index}.conv.weight'
    ]
    SCREAMING_SNAKE_CASE__: List[str]=   checkpoint[
        F'output_blocks.{i}.{index}.conv.bias'
    ]

    # Clear attentions as they have been attributed above.
    if len(_A   ) == 2:
     SCREAMING_SNAKE_CASE__: str=   []

   if len(_A   ):
    SCREAMING_SNAKE_CASE__: List[str]=   renew_attention_paths(_A   )
    SCREAMING_SNAKE_CASE__: Union[str, Any]=   {
        '''old''': F'output_blocks.{i}.1',
        '''new''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
    }
    SCREAMING_SNAKE_CASE__: Optional[int]=   {
        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(
        _A							,					_A							,					_A							,					additional_replacements=[meta_path]							,					attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions   ) else None							,					config=_A							,					)
  else:
   SCREAMING_SNAKE_CASE__: Tuple=   renew_resnet_paths(_A							,					n_shave_prefix_segments=1   )
   for path in resnet_0_paths:
    SCREAMING_SNAKE_CASE__: List[str]=   '''.'''.join(['''output_blocks''', str(_A   ), path['''old''']]   )
    SCREAMING_SNAKE_CASE__: Tuple=   '''.'''.join(['''up_blocks''', str(_A   ), '''resnets''', str(_A   ), path['''new''']]   )

    SCREAMING_SNAKE_CASE__: Union[str, Any]=   checkpoint[old_path]

 return new_checkpoint


if __name__ == "__main__":
      lowercase_  : str								=					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.')

      lowercase_  : Tuple								=					parser.parse_args()

      lowercase_  : int								=					torch.load(args.checkpoint_path)

      with open(args.config_file) as f:
            lowercase_  : Optional[Any]								=					json.loads(f.read())

      lowercase_  : Union[str, Any]								=					convert_ldm_checkpoint(checkpoint, config)

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

      lowercase_  : Dict								=					UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            lowercase_  : int								=					DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
            lowercase_  : Any								=					VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

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


'''simple docstring'''

import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					np.ndarray	,      _A       :					float = 1e-12	,      _A       :					int = 1_00	,      ):
   '''simple docstring'''
   assert np.shape(_A     )[0] == np.shape(_A     )[1]
   # Ensure proper dimensionality.
   assert np.shape(_A     )[0] == np.shape(_A     )[0]
   # Ensure inputs are either both complex or both real
   assert np.iscomplexobj(_A     ) == np.iscomplexobj(_A     )
   UpperCAmelCase__				:					int 					=	np.iscomplexobj(_A     )
   if is_complex:
      # Ensure complex input_matrix is Hermitian
      assert np.array_equal(_A	,      input_matrix.conj().T     )

   # Set convergence to False. Will define convergence when we exceed max_iterations
   # or when we have small changes from one iteration to next.

   UpperCAmelCase__				:					Any 					=	False
   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1e12

   while not convergence:
      # Multiple matrix by the vector.
      UpperCAmelCase__				:					str 					=	np.dot(_A	,      _A     )
      # Normalize the resulting output vector.
      UpperCAmelCase__				:					int 					=	w / np.linalg.norm(_A     )
      # Find rayleigh quotient
      # (faster than usual b/c we know vector is normalized already)
      UpperCAmelCase__				:					int 					=	vector.conj().T if is_complex else vector.T
      UpperCAmelCase__				:					Any 					=	np.dot(_A	,      np.dot(_A	,      _A     )     )

      # Check convergence.
      UpperCAmelCase__				:					List[Any] 					=	np.abs(lambda_ - lambda_previous     ) / lambda_
      iterations += 1

      if error <= error_tol or iterations >= max_iterations:
         UpperCAmelCase__				:					Optional[Any] 					=	True

      UpperCAmelCase__				:					Optional[int] 					=	lambda_

   if is_complex:
      UpperCAmelCase__				:					Tuple 					=	np.real(lambda_     )

   return lambda_, vector







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
   UpperCAmelCase__				:					int 					=	np.array([41, 4, 20]     )
   UpperCAmelCase__				:					str 					=	real_input_matrix.astype(np.complexaaa     )
   UpperCAmelCase__				:					Union[str, Any] 					=	np.triu(1J * complex_input_matrix	,      1     )
   complex_input_matrix += imag_matrix
   complex_input_matrix += -1 * imag_matrix.T
   UpperCAmelCase__				:					Tuple 					=	np.array([41, 4, 20]     ).astype(np.complexaaa     )

   for problem_type in ["real", "complex"]:
      if problem_type == "real":
         UpperCAmelCase__				:					Optional[int] 					=	real_input_matrix
         UpperCAmelCase__				:					int 					=	real_vector
      elif problem_type == "complex":
         UpperCAmelCase__				:					int 					=	complex_input_matrix
         UpperCAmelCase__				:					Tuple 					=	complex_vector

      # Our implementation.
      UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	power_iteration(_A	,      _A     )

      # Numpy implementation.

      # Get eigenvalues and eigenvectors using built-in numpy
      # eigh (eigh used for symmetric or hermetian matrices).
      UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	np.linalg.eigh(_A     )
      # Last eigenvalue is the maximum one.
      UpperCAmelCase__				:					Dict 					=	eigen_values[-1]
      # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
      UpperCAmelCase__				:					Union[str, Any] 					=	eigen_vectors[:, -1]

      # Check our implementation and numpy gives close answers.
      assert np.abs(eigen_value - eigen_value_max     ) <= 1e-6
      # Take absolute values element wise of each eigenvector.
      # as they are only unique to a minus sign.
      assert np.linalg.norm(np.abs(_A     ) - np.abs(_A     )     ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()




