





"""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_     :      Dict								=logging.get_logger(__name__)


A_     :      Any								=Dict[str, Any]
A_     :      List[str]								=List[Prediction]







@add_end_docstrings(lowerCAmelCase__		)
class 	__a   (  lowerCAmelCase__		):



     def __init__(       self					,     *a__					,     **a__   ):
            super().__init__(*a__					,     **a__   )

            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 						snake_case_			(       self					,     **a__   ):
            _lowerCamelCase    	=					{}
            _lowerCamelCase    	=					{}
            if "subtask" in kwargs:
                   _lowerCamelCase    	=					kwargs['subtask']
                   _lowerCamelCase    	=					kwargs['subtask']
            if "threshold" in kwargs:
                   _lowerCamelCase    	=					kwargs['threshold']
            if "mask_threshold" in kwargs:
                   _lowerCamelCase    	=					kwargs['mask_threshold']
            if "overlap_mask_area_threshold" in kwargs:
                   _lowerCamelCase    	=					kwargs['overlap_mask_area_threshold']

            return preprocess_kwargs, {}, postprocess_kwargs



     def __call__(       self					,     a__					,     **a__   ):
            return super().__call__(a__					,     **a__   )



     def 						snake_case_			(       self					,     a__					,     a__=None   ):
            _lowerCamelCase    	=					load_image(a__   )
            _lowerCamelCase    	=					[(image.height, image.width)]
            if self.model.config.__class__.__name__ == "OneFormerConfig":
                   if subtask is None:
                          _lowerCamelCase    	=					{}
                   else:
                          _lowerCamelCase    	=					{'task_inputs': [subtask]}
                   _lowerCamelCase    	=					self.image_processor(images=[image]					,     return_tensors='pt'					,     **a__   )
                   _lowerCamelCase    	=					self.tokenizer(
                       inputs['task_inputs']					,     padding='max_length'					,     max_length=self.model.config.task_seq_len					,     return_tensors=self.framework					,     )['input_ids']
            else:
                   _lowerCamelCase    	=					self.image_processor(images=[image]					,     return_tensors='pt'   )
            _lowerCamelCase    	=					target_size
            return inputs



     def 						snake_case_			(       self					,     a__   ):
            _lowerCamelCase    	=					model_inputs.pop('target_size'   )
            _lowerCamelCase    	=					self.model(**a__   )
            _lowerCamelCase    	=					target_size
            return model_outputs



     def 						snake_case_			(       self					,     a__					,     a__=None					,     a__=0.9					,     a__=0.5					,     a__=0.5   ):
            _lowerCamelCase    	=					None
            if subtask in {"panoptic", None} and hasattr(self.image_processor					,     'post_process_panoptic_segmentation'   ):
                   _lowerCamelCase    	=					self.image_processor.post_process_panoptic_segmentation
            elif subtask in {"instance", None} and hasattr(self.image_processor					,     'post_process_instance_segmentation'   ):
                   _lowerCamelCase    	=					self.image_processor.post_process_instance_segmentation

            if fn is not None:
                   _lowerCamelCase    	=					fn(
                       a__					,     threshold=a__					,     mask_threshold=a__					,     overlap_mask_area_threshold=a__					,     target_sizes=model_outputs['target_size']					,     )[0]

                   _lowerCamelCase    	=					[]
                   _lowerCamelCase    	=					outputs['segmentation']

                   for segment in outputs["segments_info"]:
                          _lowerCamelCase    	=					(segmentation == segment['id']) * 2_55
                          _lowerCamelCase    	=					Image.fromarray(mask.numpy().astype(np.uinta   )					,     mode='L'   )
                          _lowerCamelCase    	=					self.model.config.idalabel[segment['label_id']]
                          _lowerCamelCase    	=					segment['score']
                          annotation.append({'score': score, 'label': label, 'mask': mask}   )

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

                   _lowerCamelCase    	=					[]
                   _lowerCamelCase    	=					outputs.numpy()
                   _lowerCamelCase    	=					np.unique(a__   )

                   for label in labels:
                          _lowerCamelCase    	=					(segmentation == label) * 2_55
                          _lowerCamelCase    	=					Image.fromarray(mask.astype(np.uinta   )					,     mode='L'   )
                          _lowerCamelCase    	=					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













"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple						,  snake_case   :					List[Any]		)->		str:
       _lowerCamelCase    	=					UNetaDModel(**snake_case		)
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.2.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.2.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.3.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.3.bias'].data

       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-4]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-3]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-2]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-1]].data

       _lowerCamelCase    	=					4

       def set_attention_weights(snake_case   :					List[Any]						,  snake_case   :					Any						,  snake_case   :					List[Any]		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

       def set_resnet_weights(snake_case   :					Tuple						,  snake_case   :					Optional[int]						,  snake_case   :					Tuple		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Dense_0.bias'].data

              if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

       for i, block in enumerate(new_model_architecture.downsample_blocks		):
              _lowerCamelCase    	=					hasattr(snake_case						,  'attentions'		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]						,  snake_case						,  snake_case		)
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]						,  snake_case						,  snake_case		)
                            module_index += 1

              if hasattr(snake_case						,  'downsamplers'		) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down						,  snake_case						,  snake_case		)
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.Conv_0.bias'].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,  snake_case						,  snake_case		)
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]						,  snake_case						,  snake_case		)
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,  snake_case						,  snake_case		)
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks		):
              _lowerCamelCase    	=					hasattr(snake_case						,  'attentions'		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]						,  snake_case						,  snake_case		)
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]						,  snake_case						,  snake_case		)  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(snake_case						,  'resnet_up'		) and block.resnet_up is not None:
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up						,  snake_case						,  snake_case		)
                     module_index += 1

       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
       module_index += 1
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
      A_     :      Any								=argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_path""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
          type=str,
          required=False,
          help="""Path to the checkpoint to convert.""",
      )

      parser.add_argument(
          """--config_file""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
          type=str,
          required=False,
          help="""The config json file corresponding to the architecture.""",
      )

      parser.add_argument(
          """--dump_path""",
          default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
          type=str,
          required=False,
          help="""Path to the output model.""",
      )

      A_     :      int								=parser.parse_args()

      A_     :      Dict								=torch.load(args.checkpoint_path, map_location="""cpu""")

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

      A_     :      str								=convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

      A_     :      str								=UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            A_     :      List[Any]								=ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

            A_     :      Optional[Any]								=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)






