


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=1      )->						Union[str, Any]:




      """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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=0      )->						int:




      """simple docstring"""

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

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

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

            snake_case_ =   shave_segments(SCREAMING_SNAKE_CASE      ,      n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

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

      return mapping






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=0      )->						Any:




      """simple docstring"""

      snake_case_ =   []
      for old_item in old_list:
            snake_case_ =   old_item

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

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

            snake_case_ =   shave_segments(SCREAMING_SNAKE_CASE      ,      n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

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

      return mapping






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Optional[Any]:




      """simple docstring"""

      assert isinstance(SCREAMING_SNAKE_CASE      ,      SCREAMING_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():
                  snake_case_ =   old_checkpoint[path]
                  snake_case_ =   old_tensor.shape[0] // 3

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

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

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

                  snake_case_ =   query.reshape(SCREAMING_SNAKE_CASE      )
                  snake_case_ =   key.reshape(SCREAMING_SNAKE_CASE      )
                  snake_case_ =   value.reshape(SCREAMING_SNAKE_CASE      )

      for path in paths:
            snake_case_ =   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
            snake_case_ =   new_path.replace('''middle_block.0'''      ,      '''mid_block.resnets.0'''      )
            snake_case_ =   new_path.replace('''middle_block.1'''      ,      '''mid_block.attentions.0'''      )
            snake_case_ =   new_path.replace('''middle_block.2'''      ,      '''mid_block.resnets.1'''      )

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        snake_case_ =   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:
                  snake_case_ =   old_checkpoint[path['''old''']][:, :, 0]
            else:
                  snake_case_ =   old_checkpoint[path['''old''']]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   {}

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

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

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

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

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

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

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

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

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

            if len(SCREAMING_SNAKE_CASE      ):
                  snake_case_ =   renew_attention_paths(SCREAMING_SNAKE_CASE      )
                  snake_case_ =   {
                      '''old''': f'''input_blocks.{i}.1''',
                      '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
                  }
                  snake_case_ =   {
                      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(
                      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      additional_replacements=[meta_path]      ,      attention_paths_to_split=SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      ,      )

      snake_case_ =   middle_blocks[0]
      snake_case_ =   middle_blocks[1]
      snake_case_ =   middle_blocks[2]

      snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )
      assign_to_checkpoint(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      )

      snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )
      assign_to_checkpoint(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      )

      snake_case_ =   renew_attention_paths(SCREAMING_SNAKE_CASE      )
      snake_case_ =   {
          '''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(
          SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      attention_paths_to_split=SCREAMING_SNAKE_CASE      ,      config=SCREAMING_SNAKE_CASE      )

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

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

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

                  snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )
                  snake_case_ =   renew_resnet_paths(SCREAMING_SNAKE_CASE      )

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

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

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

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

                        snake_case_ =   checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
   UpperCAmelCase    =					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    =					parser.parse_args()

   UpperCAmelCase    =					torch.load(args.checkpoint_path)

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

   UpperCAmelCase    =					convert_ldm_checkpoint(checkpoint, config)

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

   UpperCAmelCase    =					UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      UpperCAmelCase    =					DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
      UpperCAmelCase    =					VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

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



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      return int((input_a, input_a).count(0      ) == 0      )






def  __lowerCAmelCase					()->						None:




      """simple docstring"""

      assert and_gate(0      ,      0      ) == 0
      assert and_gate(0      ,      1      ) == 0
      assert and_gate(1      ,      0      ) == 0
      assert and_gate(1      ,      1      ) == 1


if __name__ == "__main__":
   test_and_gate()
   print(and_gate(1, 0))
   print(and_gate(0, 0))
   print(and_gate(0, 1))
   print(and_gate(1, 1))