


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def    a   (							A__      ,    A__=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   (							A__      ,    A__=0							)	-> List[str]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Any						     =		[]
      for old_item in old_list:
            SCREAMING_SNAKE_CASE__       :  int						     =		old_item.replace('''in_layers.0'''      ,    '''norm1'''							)
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		new_item.replace('''in_layers.2'''      ,    '''conv1'''							)

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

            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		new_item.replace('''emb_layers.1'''      ,    '''time_emb_proj'''							)
            SCREAMING_SNAKE_CASE__       :  Dict						     =		new_item.replace('''skip_connection'''      ,    '''conv_shortcut'''							)

            SCREAMING_SNAKE_CASE__       :  int						     =		shave_segments(A__      ,    n_shave_prefix_segments=A__							)

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

      return mapping




def    a   (							A__      ,    A__=0							)	-> Optional[int]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		[]
      for old_item in old_list:
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		old_item

            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		new_item.replace('''norm.weight'''      ,    '''group_norm.weight'''							)
            SCREAMING_SNAKE_CASE__       :  Dict						     =		new_item.replace('''norm.bias'''      ,    '''group_norm.bias'''							)

            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		new_item.replace('''proj_out.weight'''      ,    '''proj_attn.weight'''							)
            SCREAMING_SNAKE_CASE__       :  int						     =		new_item.replace('''proj_out.bias'''      ,    '''proj_attn.bias'''							)

            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		shave_segments(A__      ,    n_shave_prefix_segments=A__							)

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

      return mapping




def    a   (							A__      ,    A__      ,    A__      ,    A__=None      ,    A__=None      ,    A__=None							)	-> Optional[int]:




      '''simple docstring'''

      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[Any]						     =		old_checkpoint[path]
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		old_tensor.shape[0] // 3

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

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

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

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

      for path in paths:
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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__       :  Any						     =		new_path.replace('''middle_block.0'''      ,    '''mid_block.resnets.0'''							)
            SCREAMING_SNAKE_CASE__       :  int						     =		new_path.replace('''middle_block.1'''      ,    '''mid_block.attentions.0'''							)
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		new_path.replace('''middle_block.2'''      ,    '''mid_block.resnets.1'''							)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        SCREAMING_SNAKE_CASE__       :  Union[str, 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__       :  int						     =		old_checkpoint[path['''old''']]




def    a   (							A__      ,    A__							)	-> Any:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		{}

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

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

      SCREAMING_SNAKE_CASE__       :  int						     =		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[int]						     =		checkpoint['''out.2.bias''']

      # Retrieves the keys for the input blocks only
      SCREAMING_SNAKE_CASE__       :  str						     =		len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''input_blocks''' in layer}							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		{
          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__       :  List[str]						     =		{
          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__       :  Dict						     =		len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''output_blocks''' in layer}							)
      SCREAMING_SNAKE_CASE__       :  Any						     =		{
          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__       :  List[str]						     =		(i - 1) // (config['''num_res_blocks'''] + 1)
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		(i - 1) % (config['''num_res_blocks'''] + 1)

            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[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__       :  List[str]						     =		checkpoint[
                      f"""input_blocks.{i}.0.op.weight"""
                  ]
                  SCREAMING_SNAKE_CASE__       :  str						     =		checkpoint[
                      f"""input_blocks.{i}.0.op.bias"""
                  ]
                  continue

            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		renew_resnet_paths(A__							)
            SCREAMING_SNAKE_CASE__       :  str						     =		{'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
            SCREAMING_SNAKE_CASE__       :  Any						     =		{'''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__       :  int						     =		renew_attention_paths(A__							)
                  SCREAMING_SNAKE_CASE__       :  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__       :  Any						     =		middle_blocks[1]
      SCREAMING_SNAKE_CASE__       :  int						     =		middle_blocks[2]

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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__       :  Dict						     =		renew_attention_paths(A__							)
      SCREAMING_SNAKE_CASE__       :  Union[str, 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__       :  Optional[int]						     =		i // (config['''num_res_blocks'''] + 1)
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		i % (config['''num_res_blocks'''] + 1)
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[shave_segments(A__      ,    2							) for name in output_blocks[i]]
            SCREAMING_SNAKE_CASE__       :  Any						     =		{}

            for layer in output_block_layers:
                  SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Tuple						     =		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__       :  Any						     =		[layer_name]

            if len(A__							) > 1:
                  SCREAMING_SNAKE_CASE__       :  Any						     =		[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

                  SCREAMING_SNAKE_CASE__       :  Any						     =		renew_resnet_paths(A__							)
                  SCREAMING_SNAKE_CASE__       :  Any						     =		renew_resnet_paths(A__							)

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

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

                  if len(A__							):
                        SCREAMING_SNAKE_CASE__       :  Any						     =		renew_attention_paths(A__							)
                        SCREAMING_SNAKE_CASE__       :  str						     =		{
                            '''old''': f"""output_blocks.{i}.1""",
                            '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                        }
                        SCREAMING_SNAKE_CASE__       :  Dict						     =		{
                            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__       :  List[Any]						     =		renew_resnet_paths(A__      ,    n_shave_prefix_segments=1							)
                  for path in resnet_0_paths:
                        SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		'''.'''.join(['''output_blocks''', str(A__							), path['''old''']]							)
                        SCREAMING_SNAKE_CASE__       :  int						     =		'''.'''.join(['''up_blocks''', str(A__							), '''resnets''', str(A__							), path['''new''']]							)

                        SCREAMING_SNAKE_CASE__       :  Tuple						     =		checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
    a_ :Dict  							=			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_ :Optional[Any]  							=			parser.parse_args()

    a_ :Any  							=			torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        a_ :List[str]  							=			json.loads(f.read())

    a_ :Tuple  							=			convert_ldm_checkpoint(checkpoint, config)

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

    a_ :int  							=			UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

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

        a_ :Optional[Any]  							=			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 cmath
import math




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> complex:
      UpperCAmelCase__			:							str							=    math.radians(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    math.radians(lowerCAmelCase    )

      # Convert voltage and current to rectangular form
      UpperCAmelCase__			:							Union[str, Any]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )
      UpperCAmelCase__			:							List[str]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )

      # Calculate apparent power
      return voltage_rect * current_rect


if __name__ == "__main__":
    import doctest

    doctest.testmod()

