






'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		)->				float:
      UpperCamelCase       						=      [redshift, radiation_density, matter_density, dark_energy]
      if any(p < 0 for p in parameters					):
            raise ValueError("""All input parameters must be positive"""					)

      if any(p > 1 for p in parameters[1:4]					):
            raise ValueError("""Relative densities cannot be greater than one"""					)
      else:
            UpperCamelCase       						=      1 - (matter_density + radiation_density + dark_energy)

            UpperCamelCase       						=      (
                radiation_density * (redshift + 1) ** 4
                + matter_density * (redshift + 1) ** 3
                + curvature * (redshift + 1) ** 2
                + dark_energy
            )

            UpperCamelCase       						=      hubble_constant * e_a ** (1 / 2)
            return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     SCREAMING_SNAKE_CASE__											=     0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1E-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )








'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1					)->				Tuple:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split("""."""					)[:n_shave_prefix_segments]					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				Dict:
      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(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

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

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				List[str]:
      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(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

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

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					), "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(__UpperCamelCase					)
                  UpperCamelCase       						=      key.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      value.reshape(__UpperCamelCase					)

      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      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(__UpperCamelCase					)
      }

      # 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(__UpperCamelCase					)
      }

      # 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(__UpperCamelCase					)
      }

      for i in range(1	,		__UpperCamelCase					):
            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(__UpperCamelCase					)
            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(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path, resnet_op]	,		config=__UpperCamelCase					)

            if len(__UpperCamelCase					):
                  UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                  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(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase	,		)

      UpperCamelCase       						=      middle_blocks[0]
      UpperCamelCase       						=      middle_blocks[1]
      UpperCamelCase       						=      middle_blocks[2]

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

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

      UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
      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(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase					)

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

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

            if len(__UpperCamelCase					) > 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(__UpperCamelCase					)
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)

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

                  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(__UpperCamelCase					) == 2:
                              UpperCamelCase       						=      []

                  if len(__UpperCamelCase					):
                        UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                        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(
                            __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,		config=__UpperCamelCase	,		)
            else:
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase	,		n_shave_prefix_segments=1					)
                  for path in resnet_0_paths:
                        UpperCamelCase       						=      """.""".join(["""output_blocks""", str(__UpperCamelCase					), path["""old"""]]					)
                        UpperCamelCase       						=      """.""".join(["""up_blocks""", str(__UpperCamelCase					), """resnets""", str(__UpperCamelCase					), path["""new"""]]					)

                        UpperCamelCase       						=      checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     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.')

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     torch.load(args.checkpoint_path)

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

     SCREAMING_SNAKE_CASE__											=     convert_ldm_checkpoint(checkpoint, config)

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

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

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

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