






'''simple docstring'''







import argparse

import torch

from diffusers import UNetaDConditionModel


def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				,      snake_case__=None				,      snake_case__=None				,      snake_case__=None				):
      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():
                  __A		 =   old_checkpoint[path]
                  __A		 =   old_tensor.shape[0] // 3

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

                  __A		 =   old_tensor.shape[0] // config["num_head_channels"] // 3

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

                  __A		 =   query.reshape(snake_case__				)
                  __A		 =   key.reshape(snake_case__				)
                  __A		 =   value.reshape(snake_case__				)

      for path in paths:
            __A		 =   path["new"]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        __A		 =   new_path.replace(replacement["old"]				,      replacement["new"]				)

        # proj_attn.weight has to be converted from conv 1D to linear
            __A		 =   old_checkpoint[path["old"]]
            __A		 =   ["proj_attn.weight"]
            __A		 =   ["proj_out.weight", "proj_in.weight"]
            if any(k in new_path for k in names				):
                  __A		 =   weight[:, :, 0]
            elif any(k in new_path for k in names_a				) and len(weight.shape				) > 2 and ".attentions." not in new_path:
                  __A		 =   weight[:, :, 0]
            else:
                  __A		 =   weight


def     _UpperCamelCase				(  snake_case__				,      snake_case__=0				):
      __A		 =   []
      for old_item in old_list:
            __A		 =   old_item

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

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

            #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

      return mapping


def     _UpperCamelCase				(  snake_case__				,      snake_case__=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     _UpperCamelCase				(  snake_case__				,      snake_case__=0				):
      __A		 =   []
      for old_item in old_list:
            mapping.append({"old": old_item, "new": old_item}				)

      return mapping


def     _UpperCamelCase				(  snake_case__				,      snake_case__=0				):
      __A		 =   []
      for old_item in old_list:
            __A		 =   old_item.replace("in_layers.0"				,      "norm1"				)
            __A		 =   new_item.replace("in_layers.2"				,      "conv1"				)

            __A		 =   new_item.replace("out_layers.0"				,      "norm2"				)
            __A		 =   new_item.replace("out_layers.3"				,      "conv2"				)

            __A		 =   new_item.replace("emb_layers.1"				,      "time_emb_proj"				)
            __A		 =   new_item.replace("skip_connection"				,      "conv_shortcut"				)

            __A		 =   shave_segments(snake_case__				,      n_shave_prefix_segments=snake_case__				)

            if "temopral_conv" not in old_item:
                  mapping.append({"old": old_item, "new": new_item}				)

      return mapping


def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__=None				,      snake_case__=False				):
      __A		 =   {}
      __A		 =   list(checkpoint.keys()				)

      __A		 =   "model.diffusion_model."

      # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
      if sum(k.startswith("model_ema"				) for k in keys				) > 100 and extract_ema:
            print(f"Checkpoint {path} has both EMA and non-EMA weights."				)
            print(
                "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
                " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."				)
            for key in keys:
                  if key.startswith("model.diffusion_model"				):
                        __A		 =   "model_ema." + "".join(key.split("."				)[1:]				)
                        __A		 =   checkpoint.pop(snake_case__				)
      else:
            if sum(k.startswith("model_ema"				) for k in keys				) > 100:
                  print(
                      "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
                      " weights (usually better for inference), please make sure to add the `--extract_ema` flag."				)

            for key in keys:
                  __A		 =   checkpoint.pop(snake_case__				)

      __A		 =   {}

      __A		 =   unet_state_dict["time_embed.0.weight"]
      __A		 =   unet_state_dict["time_embed.0.bias"]
      __A		 =   unet_state_dict["time_embed.2.weight"]
      __A		 =   unet_state_dict["time_embed.2.bias"]

      if config["class_embed_type"] is None:
            # No parameters to port
            ...
      elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
            __A		 =   unet_state_dict["label_emb.0.0.weight"]
            __A		 =   unet_state_dict["label_emb.0.0.bias"]
            __A		 =   unet_state_dict["label_emb.0.2.weight"]
            __A		 =   unet_state_dict["label_emb.0.2.bias"]
      else:
            raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}"				)

      __A		 =   unet_state_dict["input_blocks.0.0.weight"]
      __A		 =   unet_state_dict["input_blocks.0.0.bias"]

      __A		 =   [v for v in unet_state_dict if v.startswith("input_blocks.0.1"				)]
      __A		 =   renew_attention_paths(snake_case__				)
      __A		 =   {"old": "input_blocks.0.1", "new": "transformer_in"}
      assign_to_checkpoint(snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

      __A		 =   unet_state_dict["out.0.weight"]
      __A		 =   unet_state_dict["out.0.bias"]
      __A		 =   unet_state_dict["out.2.weight"]
      __A		 =   unet_state_dict["out.2.bias"]

      # Retrieves the keys for the input blocks only
      __A		 =   len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "input_blocks" in layer}				)
      __A		 =   {
          layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
          for layer_id in range(snake_case__				)
      }

      # Retrieves the keys for the middle blocks only
      __A		 =   len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "middle_block" in layer}				)
      __A		 =   {
          layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
          for layer_id in range(snake_case__				)
      }

      # Retrieves the keys for the output blocks only
      __A		 =   len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "output_blocks" in layer}				)
      __A		 =   {
          layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
          for layer_id in range(snake_case__				)
      }

      for i in range(1				,      snake_case__				):
            __A		 =   (i - 1) // (config["layers_per_block"] + 1)
            __A		 =   (i - 1) % (config["layers_per_block"] + 1)

            __A		 =   [
                key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
            ]
            __A		 =   [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
            __A		 =   [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key]

            if f"input_blocks.{i}.op.weight" in unet_state_dict:
                  __A		 =   unet_state_dict.pop(
                      f"input_blocks.{i}.op.weight"				)
                  __A		 =   unet_state_dict.pop(
                      f"input_blocks.{i}.op.bias"				)

            __A		 =   renew_resnet_paths(snake_case__				)
            __A		 =   {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            assign_to_checkpoint(
                snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

            __A		 =   [key for key in resnets if "temopral_conv" in key]
            __A		 =   renew_temp_conv_paths(snake_case__				)
            __A		 =   {
                "old": f"input_blocks.{i}.0.temopral_conv",
                "new": f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
            }
            assign_to_checkpoint(
                snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

            if len(snake_case__				):
                  __A		 =   renew_attention_paths(snake_case__				)
                  __A		 =   {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
                  assign_to_checkpoint(
                      snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

            if len(snake_case__				):
                  __A		 =   renew_attention_paths(snake_case__				)
                  __A		 =   {
                      "old": f"input_blocks.{i}.2",
                      "new": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
                  }
                  assign_to_checkpoint(
                      snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

      __A		 =   middle_blocks[0]
      __A		 =   [key for key in resnet_a if "temopral_conv" in key]
      __A		 =   middle_blocks[1]
      __A		 =   middle_blocks[2]
      __A		 =   middle_blocks[3]
      __A		 =   [key for key in resnet_a if "temopral_conv" in key]

      __A		 =   renew_resnet_paths(snake_case__				)
      __A		 =   {"old": "middle_block.0", "new": "mid_block.resnets.0"}
      assign_to_checkpoint(
          snake_case__				,      snake_case__				,      snake_case__				,      config=snake_case__				,      additional_replacements=[meta_path]				)

      __A		 =   renew_temp_conv_paths(snake_case__				)
      __A		 =   {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
      assign_to_checkpoint(
          snake_case__				,      snake_case__				,      snake_case__				,      config=snake_case__				,      additional_replacements=[meta_path]				)

      __A		 =   renew_resnet_paths(snake_case__				)
      __A		 =   {"old": "middle_block.3", "new": "mid_block.resnets.1"}
      assign_to_checkpoint(
          snake_case__				,      snake_case__				,      snake_case__				,      config=snake_case__				,      additional_replacements=[meta_path]				)

      __A		 =   renew_temp_conv_paths(snake_case__				)
      __A		 =   {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
      assign_to_checkpoint(
          snake_case__				,      snake_case__				,      snake_case__				,      config=snake_case__				,      additional_replacements=[meta_path]				)

      __A		 =   renew_attention_paths(snake_case__				)
      __A		 =   {"old": "middle_block.1", "new": "mid_block.attentions.0"}
      assign_to_checkpoint(
          snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

      __A		 =   renew_attention_paths(snake_case__				)
      __A		 =   {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
      assign_to_checkpoint(
          snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

      for i in range(snake_case__				):
            __A		 =   i // (config["layers_per_block"] + 1)
            __A		 =   i % (config["layers_per_block"] + 1)
            __A		 =   [shave_segments(snake_case__				,      2				) for name in output_blocks[i]]
            __A		 =   {}

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

            if len(snake_case__				) > 1:
                  __A		 =   [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
                  __A		 =   [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
                  __A		 =   [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]

                  __A		 =   renew_resnet_paths(snake_case__				)
                  __A		 =   renew_resnet_paths(snake_case__				)

                  __A		 =   {"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__				)

                  __A		 =   [key for key in resnets if "temopral_conv" in key]
                  __A		 =   renew_temp_conv_paths(snake_case__				)
                  __A		 =   {
                      "old": f"output_blocks.{i}.0.temopral_conv",
                      "new": f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
                  }
                  assign_to_checkpoint(
                      snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

                  __A		 =   {k: sorted(snake_case__				) for k, v in output_block_list.items()}
                  if ["conv.bias", "conv.weight"] in output_block_list.values():
                        __A		 =   list(output_block_list.values()				).index(["conv.bias", "conv.weight"]				)
                        __A		 =   unet_state_dict[
                            f"output_blocks.{i}.{index}.conv.weight"
                        ]
                        __A		 =   unet_state_dict[
                            f"output_blocks.{i}.{index}.conv.bias"
                        ]

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

                  if len(snake_case__				):
                        __A		 =   renew_attention_paths(snake_case__				)
                        __A		 =   {
                            "old": f"output_blocks.{i}.1",
                            "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        assign_to_checkpoint(
                            snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)

                  if len(snake_case__				):
                        __A		 =   renew_attention_paths(snake_case__				)
                        __A		 =   {
                            "old": f"output_blocks.{i}.2",
                            "new": f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
                        }
                        assign_to_checkpoint(
                            snake_case__				,      snake_case__				,      snake_case__				,      additional_replacements=[meta_path]				,      config=snake_case__				)
            else:
                  __A		 =   renew_resnet_paths(snake_case__				,      n_shave_prefix_segments=1				)
                  for path in resnet_0_paths:
                        __A		 =   ".".join(["output_blocks", str(snake_case__				), path["old"]]				)
                        __A		 =   ".".join(["up_blocks", str(snake_case__				), "resnets", str(snake_case__				), path["new"]]				)
                        __A		 =   unet_state_dict[old_path]

                  __A		 =   [l for l in output_block_layers if "temopral_conv" in l]
                  for path in temopral_conv_paths:
                        __A		 =   path.split("temopral_conv."				)[-1]
                        __A		 =   ".".join(["output_blocks", str(snake_case__				), str(snake_case__				), "temopral_conv", pruned_path]				)
                        __A		 =   ".".join(["up_blocks", str(snake_case__				), "temp_convs", str(snake_case__				), pruned_path]				)
                        __A		 =   unet_state_dict[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('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
       __A   :							Optional[Any]     	=						parser.parse_args()

       __A   :							Tuple     	=						torch.load(args.checkpoint_path, map_location='cpu')
       __A   :							Tuple     	=						UNetaDConditionModel()

       __A   :							Tuple     	=						convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       __A   :							Optional[Any]     	=						set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       __A   :							int     	=						set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --










'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]       ):
                 self.check_over_configs(beta_start=lowercase__					,					beta_end=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           __A		 =   torch.manual_seed(0       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 3_28.99_70       ) < 1e-2
           assert abs(result_mean.item() - 0.42_84       ) < 1e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps					,					device=lowercase__       )
           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter.to(lowercase__       ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


