






'''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      :       List[str]      =	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      :       List[str]      =	parser.parse_args()

       __A      :       int      =	torch.load(args.checkpoint_path, map_location='cpu')
       __A      :       Optional[Any]      =	UNetaDConditionModel()

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

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







from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       from ..models.auto.modeling_auto import (
           MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
           MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
           MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
           MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
       )


__A      :       int      =	logging.get_logger(__name__)


__A      :       Optional[int]      =	Dict[str, Any]
__A      :       Any      =	List[Prediction]






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







     def __init__(self    :	Optional[Any]				,				*lowercase__    :	Dict				,				**lowercase__    :	Union[str, Any]					)  ->       Tuple:
           super().__init__(*lowercase__				,				**lowercase__					)

           if self.framework == "tf":
                 raise ValueError(f"The {self.__class__} is only available in PyTorch."					)

           requires_backends(self				,				"vision"					)
           self.check_model_type(
               dict(
                   MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)






     def     _UpperCamelCase   (self    :	List[Any]				,				**lowercase__    :	Dict					)  ->       List[Any]:
           __A		   =      {}
           __A		   =      {}
           if "subtask" in kwargs:
                 __A		   =      kwargs["subtask"]
                 __A		   =      kwargs["subtask"]
           if "threshold" in kwargs:
                 __A		   =      kwargs["threshold"]
           if "mask_threshold" in kwargs:
                 __A		   =      kwargs["mask_threshold"]
           if "overlap_mask_area_threshold" in kwargs:
                 __A		   =      kwargs["overlap_mask_area_threshold"]

           return preprocess_kwargs, {}, postprocess_kwargs






     def __call__(self    :	int				,				lowercase__    :	Any				,				**lowercase__    :	Dict					)  ->       Union[Predictions, List[Prediction]]:
           return super().__call__(lowercase__				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	int				,				lowercase__    :	Union[str, Any]=None					)  ->       Any:
           __A		   =      load_image(lowercase__					)
           __A		   =      [(image.height, image.width)]
           if self.model.config.__class__.__name__ == "OneFormerConfig":
                 if subtask is None:
                       __A		   =      {}
                 else:
                       __A		   =      {"task_inputs": [subtask]}
                 __A		   =      self.image_processor(images=[image]				,				return_tensors="pt"				,				**lowercase__					)
                 __A		   =      self.tokenizer(
                     inputs["task_inputs"]				,				padding="max_length"				,				max_length=self.model.config.task_seq_len				,				return_tensors=self.framework				,				)["input_ids"]
           else:
                 __A		   =      self.image_processor(images=[image]				,				return_tensors="pt"					)
           __A		   =      target_size
           return inputs






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Optional[int]					)  ->       Dict:
           __A		   =      model_inputs.pop("target_size"					)
           __A		   =      self.model(**lowercase__					)
           __A		   =      target_size
           return model_outputs






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Tuple=0.9				,				lowercase__    :	str=0.5				,				lowercase__    :	Tuple=0.5					)  ->       Tuple:
           __A		   =      None
           if subtask in {"panoptic", None} and hasattr(self.image_processor				,				"post_process_panoptic_segmentation"					):
                 __A		   =      self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor				,				"post_process_instance_segmentation"					):
                 __A		   =      self.image_processor.post_process_instance_segmentation

           if fn is not None:
                 __A		   =      fn(
                     lowercase__				,				threshold=lowercase__				,				mask_threshold=lowercase__				,				overlap_mask_area_threshold=lowercase__				,				target_sizes=model_outputs["target_size"]				,				)[0]

                 __A		   =      []
                 __A		   =      outputs["segmentation"]

                 for segment in outputs["segments_info"]:
                       __A		   =      (segmentation == segment["id"]) * 255
                       __A		   =      Image.fromarray(mask.numpy().astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[segment["label_id"]]
                       __A		   =      segment["score"]
                       annotation.append({"score": score, "label": label, "mask": mask}					)

           elif subtask in {"semantic", None} and hasattr(self.image_processor				,				"post_process_semantic_segmentation"					):
                 __A		   =      self.image_processor.post_process_semantic_segmentation(
                     lowercase__				,				target_sizes=model_outputs["target_size"]					)[0]

                 __A		   =      []
                 __A		   =      outputs.numpy()
                 __A		   =      np.unique(lowercase__					)

                 for label in labels:
                       __A		   =      (segmentation == label) * 255
                       __A		   =      Image.fromarray(mask.astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[label]
                       annotation.append({"score": None, "label": label, "mask": mask}					)
           else:
                 raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model					)}"					)
           return annotation


