






import json
import sys







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase     )					->      str:
     with open(UpperCAmelCase_   ,       encoding="utf-8"     ) as f:
          UpperCamelCase__     :  int										=    json.load(UpperCAmelCase_     )

     UpperCamelCase__     :  str										=    ['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

     for benchmark_name in sorted(UpperCAmelCase_     ):
          UpperCamelCase__     :  List[str]										=    results[benchmark_name]

          UpperCamelCase__     :  Optional[Any]										=    benchmark_name.split("/"     )[-1]
          output_md.append(f'### Benchmark: {benchmark_file_name}'     )

          UpperCamelCase__     :  List[Any]										=    '''| metric |'''
          UpperCamelCase__     :  str										=    '''|--------|'''
          UpperCamelCase__     :  Dict										=    '''| new / old (diff) |'''
          for metric_name in sorted(UpperCAmelCase_     ):
               UpperCamelCase__     :  List[Any]										=    benchmark_res[metric_name]
               UpperCamelCase__     :  List[str]										=    metric_vals['''new''']
               UpperCamelCase__     :  List[str]										=    metric_vals.get("old"   ,       UpperCAmelCase_     )
               UpperCamelCase__     :  Union[str, Any]										=    metric_vals.get("diff"   ,       UpperCAmelCase_     )

               UpperCamelCase__     :  Any										=    f' {new_val:f}' if isinstance(UpperCAmelCase_   ,       (int, float)     ) else '''None'''

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

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

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

     output_md.append("</details>"     )

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


if __name__ == "__main__":
 lowerCamelCase      :				List[str]							=sys.argv[1]
 lowerCamelCase      :				Optional[int]							=sys.argv[2]

 format_json_to_md(input_json_file, output_md_file)

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.schedule[t - 1] if t > 0 else 0

                     # 1. Select temporarily increased noise level sigma_hat
                     # 2. Add new noise to move from sample_i to sample_hat
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(sigma_hat / 2) * model((sample_hat + 1) / 2						,       sigma_hat / 2				).sample

                     # 4. Evaluate dx/dt at sigma_hat
                     # 5. Take Euler step from sigma to sigma_prev
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

                     if sigma_prev != 0:
                            # 6. Apply 2nd order correction
                            # The model inputs and output are adjusted by following eq. (213) in [1].
                            SCREAMING_SNAKE_CASE_							:				int		=(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,       sigma_prev / 2				).sample
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.step_correct(
                                __A						,       __A						,       __A						,       __A						,       step_output.prev_sample						,       step_output['''derivative''']						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=step_output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
