



import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Tuple					=						argparse.ArgumentParser()

     parser.add_argument(
         """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
     )
     # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
     parser.add_argument(
         """--original_config_file""",
         default=None,
         type=str,
         help="""The YAML config file corresponding to the original architecture.""",
     )
     parser.add_argument(
         """--num_in_channels""",
         default=None,
         type=int,
         help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
     )
     parser.add_argument(
         """--scheduler_type""",
         default="""pndm""",
         type=str,
         help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
     )
     parser.add_argument(
         """--pipeline_type""",
         default=None,
         type=str,
         help=(
             """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
             """. If `None` pipeline will be automatically inferred."""
         ),
     )
     parser.add_argument(
         """--image_size""",
         default=None,
         type=int,
         help=(
             """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
             """ Base. Use 768 for Stable Diffusion v2."""
         ),
     )
     parser.add_argument(
         """--prediction_type""",
         default=None,
         type=str,
         help=(
             """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
             """ Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."""
         ),
     )
     parser.add_argument(
         """--extract_ema""",
         action="""store_true""",
         help=(
             """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
             """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
             """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
         ),
     )
     parser.add_argument(
         """--upcast_attention""",
         action="""store_true""",
         help=(
             """Whether the attention computation should always be upcasted. This is necessary when running stable"""
             """ diffusion 2.1."""
         ),
     )
     parser.add_argument(
         """--from_safetensors""",
         action="""store_true""",
         help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
     )
     parser.add_argument(
         """--to_safetensors""",
         action="""store_true""",
         help="""Whether to store pipeline in safetensors format or not.""",
     )
     parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
     parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
     parser.add_argument(
         """--stable_unclip""",
         type=str,
         default=None,
         required=False,
         help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
     )
     parser.add_argument(
         """--stable_unclip_prior""",
         type=str,
         default=None,
         required=False,
         help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
     )
     parser.add_argument(
         """--clip_stats_path""",
         type=str,
         help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
         required=False,
     )
     parser.add_argument(
         """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
     )
     parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
     parser.add_argument(
         """--vae_path""",
         type=str,
         default=None,
         required=False,
         help="""Set to a path, hub id to an already converted vae to not convert it again.""",
     )
     SCREAMING_SNAKE_CASE_:Tuple					=						parser.parse_args()

     SCREAMING_SNAKE_CASE_:Optional[int]					=						download_from_original_stable_diffusion_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         prediction_type=args.prediction_type,
         model_type=args.pipeline_type,
         extract_ema=args.extract_ema,
         scheduler_type=args.scheduler_type,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         stable_unclip=args.stable_unclip,
         stable_unclip_prior=args.stable_unclip_prior,
         clip_stats_path=args.clip_stats_path,
         controlnet=args.controlnet,
         vae_path=args.vae_path,
     )

     if args.half:
          pipe.to(torch_dtype=torch.floataa)

     if args.controlnet:
          # only save the controlnet model
          pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
     else:
          pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







from random import randint, random




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase = False				,					_lowerCAmelCase = False				,					_lowerCAmelCase = 5				,					)	->  list:



    """simple docstring"""






    A			:	Any										=       [[-1] * number_of_cells]  # Create a highway without any car
    A			:	Tuple										=       0
    A			:	Dict										=       max(_lowerCAmelCase				,					0		)
    while i < number_of_cells:
        A			:	Any										=       (
            randint(0				,					_lowerCAmelCase		) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1				,					max_speed * 2		) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    A			:	List[str]										=       0
    A			:	Dict										=       highway_now[car_index + 1 :]
    for cell in range(len(_lowerCAmelCase		)		):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(_lowerCAmelCase				,					-1		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  list:



    """simple docstring"""






    A			:	str										=       len(_lowerCAmelCase		)
    # Beforce calculations, the highway is empty
    A			:	Any										=       [-1] * number_of_cells

    for car_index in range(_lowerCAmelCase		):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            A			:	str										=       min(highway_now[car_index] + 1				,					_lowerCAmelCase		)
            # Number of empty cell before the next car
            A			:	Optional[int]										=       get_distance(_lowerCAmelCase				,					_lowerCAmelCase		) - 1
            # We can't have the car causing an accident
            A			:	Any										=       min(next_highway[car_index]				,					_lowerCAmelCase		)
            if random() < probability:
                # Randomly, a driver will slow down
                A			:	Optional[Any]										=       max(next_highway[car_index] - 1				,					0		)
    return next_highway




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  list:



    """simple docstring"""






    A			:	Any										=       len(highway[0]		)

    for i in range(_lowerCAmelCase		):
        A			:	Optional[int]										=       update(highway[i]				,					_lowerCAmelCase				,					_lowerCAmelCase		)
        A			:	Tuple										=       [-1] * number_of_cells

        for car_index in range(_lowerCAmelCase		):
            A			:	Dict										=       next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                A			:	Optional[int]										=       (car_index + speed) % number_of_cells
                # Commit the change of position
                A			:	Dict										=       speed
        highway.append(_lowerCAmelCase		)

    return highway


if __name__ == "__main__":
     import doctest

     doctest.testmod()


