
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  __magic_name__    ,  __magic_name__      ):
       super().__init__()

       # make sure scheduler can always be converted to DDIM
       lowerCamelCase		:	Union[str, Any]							=      DDIMScheduler.from_config(scheduler.config      )

       self.register_modules(unet=__magic_name__    ,  scheduler=__magic_name__      )






      @torch.no_grad()
      def __call__(      self    ,  __magic_name__ = 1    ,  __magic_name__ = None    ,  __magic_name__ = 0.0    ,  __magic_name__ = 5_0    ,  __magic_name__ = None    ,  __magic_name__ = "pil"    ,  __magic_name__ = True    ,  ):

       # Sample gaussian noise to begin loop
       if isinstance(self.unet.config.sample_size    ,  __magic_name__      ):
        lowerCamelCase		:	Tuple							=      (
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
       else:
        lowerCamelCase		:	List[str]							=      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

       if isinstance(__magic_name__    ,  __magic_name__      ) and len(__magic_name__      ) != batch_size:
        raise ValueError(
            F'''You have passed a list of generators of length {len(__magic_name__      )}, but requested an effective batch'''
            F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

       lowerCamelCase		:	Dict							=      randn_tensor(__magic_name__    ,  generator=__magic_name__    ,  device=self.device    ,  dtype=self.unet.dtype      )

       # set step values
       self.scheduler.set_timesteps(__magic_name__      )

       for t in self.progress_bar(self.scheduler.timesteps      ):
        # 1. predict noise model_output
        lowerCamelCase		:	List[Any]							=      self.unet(__magic_name__    ,  __magic_name__      ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowerCamelCase		:	str							=      self.scheduler.step(
            __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  eta=__magic_name__    ,  use_clipped_model_output=__magic_name__    ,  generator=__magic_name__      ).prev_sample

       lowerCamelCase		:	Dict							=      (image / 2 + 0.5).clamp(0    ,  1      )
       lowerCamelCase		:	Union[str, Any]							=      image.cpu().permute(0    ,  2    ,  3    ,  1      ).numpy()
       if output_type == "pil":
        lowerCamelCase		:	Union[str, Any]							=      self.numpy_to_pil(__magic_name__      )

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=__magic_name__      )






import json
import sys





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 with open(lowerCamelCase,	encoding="""utf-8"""							) as f:
  lowerCamelCase		:	List[Any]							=      json.load(lowerCamelCase							)

 lowerCamelCase		:	Optional[Any]							=      ["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

 for benchmark_name in sorted(lowerCamelCase							):
  lowerCamelCase		:	List[Any]							=      results[benchmark_name]

  lowerCamelCase		:	Union[str, Any]							=      benchmark_name.split("""/"""							)[-1]
  output_md.append(F'''### Benchmark: {benchmark_file_name}'''							)

  lowerCamelCase		:	Any							=      """| metric |"""
  lowerCamelCase		:	str							=      """|--------|"""
  lowerCamelCase		:	List[Any]							=      """| new / old (diff) |"""
  for metric_name in sorted(lowerCamelCase							):
   lowerCamelCase		:	List[Any]							=      benchmark_res[metric_name]
   lowerCamelCase		:	Tuple							=      metric_vals["""new"""]
   lowerCamelCase		:	int							=      metric_vals.get("""old""",	lowerCamelCase							)
   lowerCamelCase		:	Dict							=      metric_vals.get("""diff""",	lowerCamelCase							)

   lowerCamelCase		:	Dict							=      F''' {new_val:f}''' if isinstance(lowerCamelCase,	(int, float)							) else """None"""

   if old_val is not None:
    val_str += F''' / {old_val:f}''' if isinstance(lowerCamelCase,	(int, float)							) else "None"
   if dif_val is not None:
    val_str += F''' ({dif_val:f})''' if isinstance(lowerCamelCase,	(int, float)							) else "None"

   title += " " + metric_name + " |"
   lines += "---|"
   value += val_str + " |"

  output_md += [title, lines, value, " "]

 output_md.append("""</details>"""							)

 with open(lowerCamelCase,	"""w""",	encoding="""utf-8"""							) as f:
  f.writelines("""\n""".join(lowerCamelCase							)							)


if __name__ == "__main__":
      _lowerCamelCase					   =sys.argv[1]
      _lowerCamelCase					   =sys.argv[2]

      format_json_to_md(input_json_file, output_md_file)




