
import io
import json
import subprocess


A__       :   Any   						=					[
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

A__       :   Dict   						=					8



def      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Union[str, Any]    ):
			lowerCAmelCase__ :    Union[str, Any]				      =			{}
			for src, tgt in pairs:
						lowerCAmelCase__ :    Any				      =			f'''{src}-{tgt}'''
						lowerCAmelCase__ :    int				      =			f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
						lowerCAmelCase__ :    Tuple				      =			subprocess.run(__UpperCamelCase	,stdout=subprocess.PIPE    ).stdout.decode('''utf-8'''    ).splitlines()
						lowerCAmelCase__ :    Tuple				      =			f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
						lowerCAmelCase__ :    int				      =			subprocess.run(__UpperCamelCase	,stdout=subprocess.PIPE    ).stdout.decode('''utf-8'''    ).splitlines()
						lowerCAmelCase__ :    Any				      =			{'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
			return text


A__       :   str   						=					get_all_data(pairs, n_objs)
A__       :   Tuple   						=					"""./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
							A__       :   Optional[int]   						=					json.dump(text, f, indent=2, ensure_ascii=False)





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 						lowercase       (  __UpperCamelCase					):
					__a					=    (HeunDiscreteScheduler,)
					__a					=    10
					def lowercase_			(		self ,	**SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			{
								    '''num_train_timesteps''': 1100,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**SCREAMING_SNAKE_CASE__    )
								return config
					def lowercase_			(		self    ):
								"""simple docstring"""







								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,	[0.0_002, 0.002, 0.02]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ ,	beta_end=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE__ ,	clip_sample=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Optional[Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Optional[int]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    Optional[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    List[Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Tuple				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Union[str, Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Any				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Union[str, Any]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    str				      =			self.get_scheduler_config(prediction_type='''v_prediction'''    )
								lowerCAmelCase__ :    Any				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    int				      =			self.dummy_model()
								lowerCAmelCase__ :    List[str]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[int]				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			output.prev_sample

								lowerCAmelCase__ :    List[Any]				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07    ) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Union[str, Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

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

								lowerCAmelCase__ :    Optional[Any]				      =			self.dummy_model()
								lowerCAmelCase__ :    Dict				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Dict				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Dict				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if str(SCREAMING_SNAKE_CASE__    ).startswith('''cpu'''    ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								elif str(SCREAMING_SNAKE_CASE__    ).startswith('''mps'''    ):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_002    ) < 1E-2
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    str				      =			scheduler_class(**SCREAMING_SNAKE_CASE__ ,	use_karras_sigmas=SCREAMING_SNAKE_CASE__    )

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

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Any				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    Union[str, Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    int				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Dict				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								assert abs(result_sum.item() - 0.00_015    ) < 1E-2
								assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07    ) < 1E-2



