'''simple docstring'''

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       a__				( UpperCamelCase_      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[int]	=	(KDPMaDiscreteScheduler,)
				__UpperCamelCase    :		List[Any]	=	10




				def 					_snake_case  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{
										    'num_train_timesteps': 11_00,
										    'beta_start': 0.0_0_0_1,
										    'beta_end': 0.0_2,
										    'beta_schedule': 'linear',
										}

										config.update(**__a		)
										return config




				def 					_snake_case  (self		):






										for timesteps in [10, 50, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=__a		)




				def 					_snake_case  (self		):






										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,			[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
																self.check_over_configs(beta_start=__a       ,			beta_end=__a		)




				def 					_snake_case  (self		):






										for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=__a		)




				def 					_snake_case  (self		):






										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=__a		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config(prediction_type='''v_prediction'''		)
										__lowerCAmelCase							       =				scheduler_class(**__a		)

										scheduler.set_timesteps(self.num_inference_steps		)

										__lowerCAmelCase							       =				self.dummy_model()
										__lowerCAmelCase							       =				self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =				sample.to(__a		)

										for i, t in enumerate(scheduler.timesteps		):
																__lowerCAmelCase							       =				scheduler.scale_model_input(__a       ,			__a		)

																__lowerCAmelCase							       =				model(__a       ,			__a		)

																__lowerCAmelCase							       =				scheduler.step(__a       ,			__a       ,			__a		)
																__lowerCAmelCase							       =				output.prev_sample

										__lowerCAmelCase							       =				torch.sum(torch.abs(__a		)		)
										__lowerCAmelCase							       =				torch.mean(torch.abs(__a		)		)

										if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6_934e-07		) < 1e-2
																assert abs(result_mean.item() - 6.1_112e-10		) < 1e-3
										else:
																# CUDA
																assert abs(result_sum.item() - 4.693_428_650_170_972e-07		) < 1e-2
																assert abs(result_mean.item() - 0.0_0_0_2		) < 1e-3




				def 					_snake_case  (self		):






										if torch_device == "mps":
																return
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**__a		)

										scheduler.set_timesteps(self.num_inference_steps		)

										__lowerCAmelCase							       =				self.dummy_model()
										__lowerCAmelCase							       =				self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =				sample.to(__a		)

										for i, t in enumerate(scheduler.timesteps		):
																__lowerCAmelCase							       =				scheduler.scale_model_input(__a       ,			__a		)

																__lowerCAmelCase							       =				model(__a       ,			__a		)

																__lowerCAmelCase							       =				scheduler.step(__a       ,			__a       ,			__a		)
																__lowerCAmelCase							       =				output.prev_sample

										__lowerCAmelCase							       =				torch.sum(torch.abs(__a		)		)
										__lowerCAmelCase							       =				torch.mean(torch.abs(__a		)		)

										if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 2_0.4_1_2_5		) < 1e-2
																assert abs(result_mean.item() - 0.0_2_6_6		) < 1e-3
										else:
																# CUDA
																assert abs(result_sum.item() - 2_0.4_1_2_5		) < 1e-2
																assert abs(result_mean.item() - 0.0_2_6_6		) < 1e-3




				def 					_snake_case  (self		):






										if torch_device == "mps":
																return
										__lowerCAmelCase							       =				self.scheduler_classes[0]
										__lowerCAmelCase							       =				self.get_scheduler_config()
										__lowerCAmelCase							       =				scheduler_class(**__a		)

										scheduler.set_timesteps(self.num_inference_steps       ,			device=__a		)

										__lowerCAmelCase							       =				self.dummy_model()
										__lowerCAmelCase							       =				self.dummy_sample_deter.to(__a		) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
																__lowerCAmelCase							       =				scheduler.scale_model_input(__a       ,			__a		)

																__lowerCAmelCase							       =				model(__a       ,			__a		)

																__lowerCAmelCase							       =				scheduler.step(__a       ,			__a       ,			__a		)
																__lowerCAmelCase							       =				output.prev_sample

										__lowerCAmelCase							       =				torch.sum(torch.abs(__a		)		)
										__lowerCAmelCase							       =				torch.mean(torch.abs(__a		)		)

										if str(__a		).startswith('''cpu'''		):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 2_0.4_1_2_5		) < 1e-2
																assert abs(result_mean.item() - 0.0_2_6_6		) < 1e-3
										else:
																# CUDA
																assert abs(result_sum.item() - 2_0.4_1_2_5		) < 1e-2
																assert abs(result_mean.item() - 0.0_2_6_6		) < 1e-3





'''simple docstring'''

import json
import sys

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						with open(lowerCamelCase,				encoding='''utf-8''') as f:
												__lowerCAmelCase							       =				json.load(lowerCamelCase)

						__lowerCAmelCase							       =				['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

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

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

												__lowerCAmelCase							       =				'''| metric |'''
												__lowerCAmelCase							       =				'''|--------|'''
												__lowerCAmelCase							       =				'''| new / old (diff) |'''
												for metric_name in sorted(lowerCamelCase):
																		__lowerCAmelCase							       =				benchmark_res[metric_name]
																		__lowerCAmelCase							       =				metric_vals['''new''']
																		__lowerCAmelCase							       =				metric_vals.get('''old''',				lowerCamelCase)
																		__lowerCAmelCase							       =				metric_vals.get('''diff''',				lowerCamelCase)

																		__lowerCAmelCase							       =				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__":
	_UpperCAmelCase				: str							    =				sys.argv[1]
	_UpperCAmelCase				: Optional[Any]							    =				sys.argv[2]

	format_json_to_md(input_json_file, output_md_file)

