





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 							_UpperCamelCase    (		lowerCAmelCase_   ):
       _UpperCamelCase :    Tuple   			=      (HeunDiscreteScheduler,)
       _UpperCamelCase :    Optional[Any]   			=      1_0

       def lowercase       (						self:       List[Any]    ,      **_SCREAMING_SNAKE_CASE:       Tuple						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "num_train_timesteps": 1100,
                  "beta_start": 0.00_01,
                  "beta_end": 0.02,
                  "beta_schedule": "linear",
              }

              config.update(**_SCREAMING_SNAKE_CASE						)
              return config

       def lowercase       (						self:       List[Any]						)				->       int:




              """simple docstring"""


              for timesteps in [10, 50, 100, 1000]:
                     self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       Tuple						)				->       Tuple:




              """simple docstring"""


              for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,      [0.00_02, 0.0_02, 0.02]						):
                     self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE    ,      beta_end=_SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       Any						)				->       List[Any]:




              """simple docstring"""


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

       def lowercase       (						self:       Optional[int]						)				->       Union[str, Any]:




              """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:       List[Any]						)				->       List[Any]:




              """simple docstring"""


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

       def lowercase       (						self:       Optional[int]						)				->       int:




              """simple docstring"""


              UpperCamelCase_      				=     self.scheduler_classes[0]
              UpperCamelCase_      				=     self.get_scheduler_config()
              UpperCamelCase_      				=     scheduler_class(**_SCREAMING_SNAKE_CASE						)

              scheduler.set_timesteps(self.num_inference_steps						)

              UpperCamelCase_      				=     self.dummy_model()
              UpperCamelCase_      				=     self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase_      				=     sample.to(_SCREAMING_SNAKE_CASE						)

              for i, t in enumerate(scheduler.timesteps						):
                     UpperCamelCase_      				=     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     scheduler.step(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                     UpperCamelCase_      				=     output.prev_sample

              UpperCamelCase_      				=     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 0.12_33						) < 1e-2
                     assert abs(result_mean.item() - 0.00_02						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.12_33						) < 1e-2
                     assert abs(result_mean.item() - 0.00_02						) < 1e-3

       def lowercase       (						self:       Any						)				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     self.scheduler_classes[0]
              UpperCamelCase_      				=     self.get_scheduler_config(prediction_type="v_prediction"						)
              UpperCamelCase_      				=     scheduler_class(**_SCREAMING_SNAKE_CASE						)

              scheduler.set_timesteps(self.num_inference_steps						)

              UpperCamelCase_      				=     self.dummy_model()
              UpperCamelCase_      				=     self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase_      				=     sample.to(_SCREAMING_SNAKE_CASE						)

              for i, t in enumerate(scheduler.timesteps						):
                     UpperCamelCase_      				=     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     scheduler.step(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                     UpperCamelCase_      				=     output.prev_sample

              UpperCamelCase_      				=     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     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() - 0.00_02						) < 1e-3

       def lowercase       (						self:       Dict						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     self.scheduler_classes[0]
              UpperCamelCase_      				=     self.get_scheduler_config()
              UpperCamelCase_      				=     scheduler_class(**_SCREAMING_SNAKE_CASE						)

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

              UpperCamelCase_      				=     self.dummy_model()
              UpperCamelCase_      				=     self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE						) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     UpperCamelCase_      				=     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     scheduler.step(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                     UpperCamelCase_      				=     output.prev_sample

              UpperCamelCase_      				=     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     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() - 0.12_33						) < 1e-2
                     assert abs(result_mean.item() - 0.00_02						) < 1e-3
              elif str(_SCREAMING_SNAKE_CASE						).startswith("mps"						):
                     # Larger tolerance on mps
                     assert abs(result_mean.item() - 0.00_02						) < 1e-2
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.12_33						) < 1e-2
                     assert abs(result_mean.item() - 0.00_02						) < 1e-3



       def lowercase       (						self:       int						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_      				=     self.scheduler_classes[0]
              UpperCamelCase_      				=     self.get_scheduler_config()
              UpperCamelCase_      				=     scheduler_class(**_SCREAMING_SNAKE_CASE    ,      use_karras_sigmas=_SCREAMING_SNAKE_CASE						)

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

              UpperCamelCase_      				=     self.dummy_model()
              UpperCamelCase_      				=     self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE						) * scheduler.init_noise_sigma
              UpperCamelCase_      				=     sample.to(_SCREAMING_SNAKE_CASE						)

              for t in scheduler.timesteps:
                     UpperCamelCase_      				=     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

                     UpperCamelCase_      				=     scheduler.step(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                     UpperCamelCase_      				=     output.prev_sample

              UpperCamelCase_      				=     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

              assert abs(result_sum.item() - 0.0_00_15						) < 1e-2
              assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07						) < 1e-2








import io
import json
import subprocess


_UpperCAmelCase				       = [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

_UpperCAmelCase				       = 8




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						Union[str, Any]:
       UpperCamelCase_      				=     {}
       for src, tgt in pairs:
              UpperCamelCase_      				=     F'''{src}-{tgt}'''
              UpperCamelCase_      				=     F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
              UpperCamelCase_      				=     subprocess.run(UpperCamelCase_    ,						stdout=subprocess.PIPE     ).stdout.decode("utf-8"     ).splitlines()
              UpperCamelCase_      				=     F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
              UpperCamelCase_      				=     subprocess.run(UpperCamelCase_    ,						stdout=subprocess.PIPE     ).stdout.decode("utf-8"     ).splitlines()
              UpperCamelCase_      				=     {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
       return text


_UpperCAmelCase				       = get_all_data(pairs, n_objs)
_UpperCAmelCase				       = './fsmt_val_data.json'
with io.open(filename, 'w', encoding='utf-8') as f:
     _UpperCAmelCase				       = json.dump(text, f, indent=2, ensure_ascii=False)

