





import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE_						       =	False

class 		UpperCamelCase__			(				unittest.TestCase       ):



					'''simple docstring'''

					def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   int=32	)					->		str:




											'''simple docstring'''


											set_seed(0	)
											SCREAMING_SNAKE_CASE		=						UNetaDModel(sample_size=lowerCamelCase__						,in_channels=3						,out_channels=3	)
											SCREAMING_SNAKE_CASE		=						torch.optim.SGD(model.parameters()						,lr=0.0001	)
											return model, optimizer






					@slow
					def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
											SCREAMING_SNAKE_CASE		=						DDPMScheduler(
											    num_train_timesteps=1000						,beta_start=0.0001						,beta_end=0.02						,beta_schedule="""linear"""						,clip_sample=lowerCamelCase__						,)
											SCREAMING_SNAKE_CASE		=						DDIMScheduler(
											    num_train_timesteps=1000						,beta_start=0.0001						,beta_end=0.02						,beta_schedule="""linear"""						,clip_sample=lowerCamelCase__						,)

											assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

											# shared batches for DDPM and DDIM
											set_seed(0	)
											SCREAMING_SNAKE_CASE		=						[torch.randn((4, 3, 32, 32)	).clip(-1						,1	).to(lowerCamelCase__	) for _ in range(4	)]
											SCREAMING_SNAKE_CASE		=						[torch.randn((4, 3, 32, 32)	).to(lowerCamelCase__	) for _ in range(4	)]
											SCREAMING_SNAKE_CASE		=						[torch.randint(0						,1000						,(4,)	).long().to(lowerCamelCase__	) for _ in range(4	)]

											# train with a DDPM scheduler
											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.get_model_optimizer(resolution=32	)
											model.train().to(lowerCamelCase__	)
											for i in range(4	):
																	optimizer.zero_grad()
																	SCREAMING_SNAKE_CASE		=						ddpm_scheduler.add_noise(clean_images[i]						,noise[i]						,timesteps[i]	)
																	SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,timesteps[i]	).sample
																	SCREAMING_SNAKE_CASE		=						torch.nn.functional.mse_loss(lowerCamelCase__						,noise[i]	)
																	loss.backward()
																	optimizer.step()
											del model, optimizer

											# recreate the model and optimizer, and retry with DDIM
											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.get_model_optimizer(resolution=32	)
											model.train().to(lowerCamelCase__	)
											for i in range(4	):
																	optimizer.zero_grad()
																	SCREAMING_SNAKE_CASE		=						ddim_scheduler.add_noise(clean_images[i]						,noise[i]						,timesteps[i]	)
																	SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,timesteps[i]	).sample
																	SCREAMING_SNAKE_CASE		=						torch.nn.functional.mse_loss(lowerCamelCase__						,noise[i]	)
																	loss.backward()
																	optimizer.step()
											del model, optimizer

											self.assertTrue(torch.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-5	)	)
											self.assertTrue(torch.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-5	)	)









import json
import sys



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


      with open(_SCREAMING_SNAKE_CASE			,						encoding="""utf-8"""     ) as f:
            SCREAMING_SNAKE_CASE		=						json.load(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

      for benchmark_name in sorted(_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						results[benchmark_name]

            SCREAMING_SNAKE_CASE		=						benchmark_name.split("""/"""     )[-1]
            output_md.append(F"""### Benchmark: {benchmark_file_name}"""     )

            SCREAMING_SNAKE_CASE		=						"""| metric |"""
            SCREAMING_SNAKE_CASE		=						"""|--------|"""
            SCREAMING_SNAKE_CASE		=						"""| new / old (diff) |"""
            for metric_name in sorted(_SCREAMING_SNAKE_CASE     ):
                  SCREAMING_SNAKE_CASE		=						benchmark_res[metric_name]
                  SCREAMING_SNAKE_CASE		=						metric_vals["""new"""]
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""old"""			,						_SCREAMING_SNAKE_CASE     )
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""diff"""			,						_SCREAMING_SNAKE_CASE     )

                  SCREAMING_SNAKE_CASE		=						F""" {new_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else """None"""

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

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

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

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

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


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

      format_json_to_md(input_json_file, output_md_file)


