

'''simple docstring'''







import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCamelCase		       =    get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
      __lowerCamelCase		       =    json.load(f)


@require_torch
class 					A__      (      unittest.TestCase			):



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           return FSMTTokenizer.from_pretrained(UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           A_						=      FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__       ).to(UpperCamelCase__       )
           if torch_device == "cuda":
                  model.half()
           return model







    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ]       )
    @slow
    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           # note: this test is not testing the best performance since it only evals a small batch
           # but it should be enough to detect a regression in the output quality
           A_						=      f'''facebook/wmt19-{pair}'''
           A_						=      self.get_tokenizer(UpperCamelCase__       )
           A_						=      self.get_model(UpperCamelCase__       )

           A_						=      bleu_data[pair]["""src"""]
           A_						=      bleu_data[pair]["""tgt"""]

           A_						=      tokenizer(UpperCamelCase__		,				return_tensors="""pt"""		,				truncation=UpperCamelCase__		,				padding="""longest"""       ).to(UpperCamelCase__       )
           A_						=      model.generate(
               input_ids=batch.input_ids		,				num_beams=8		,				)
           A_						=      tokenizer.batch_decode(
               UpperCamelCase__		,				skip_special_tokens=UpperCamelCase__		,				clean_up_tokenization_spaces=UpperCamelCase__       )
           A_						=      calculate_bleu(UpperCamelCase__		,				UpperCamelCase__       )
           print(UpperCamelCase__       )
           self.assertGreaterEqual(scores["""bleu"""]		,				UpperCamelCase__       )



'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 					A__      (      _snake_case			):
    lowercase						=		(IPNDMScheduler,)
    lowercase						=		(("num_inference_steps", 50),)



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      {"""num_train_timesteps""": 1000}
           config.update(**UpperCamelCase__       )
           return config



    def 					snake_case_			(					self		,				UpperCamelCase__=0		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )
           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )
           A_						=      self.dummy_sample
           A_						=      0.1 * sample
           A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config(**UpperCamelCase__       )
                  A_						=      scheduler_class(**UpperCamelCase__       )
                  scheduler.set_timesteps(UpperCamelCase__       )
                  # copy over dummy past residuals
                  A_						=      dummy_past_residuals[:]

                  if time_step is None:
                         A_						=      scheduler.timesteps[len(scheduler.timesteps       ) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__       )
                         A_						=      scheduler_class.from_pretrained(UpperCamelCase__       )
                         new_scheduler.set_timesteps(UpperCamelCase__       )
                         # copy over dummy past residuals
                         A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"



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





           '''simple docstring'''
           pass



    def 					snake_case_			(					self		,				UpperCamelCase__=0		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )
           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )
           A_						=      self.dummy_sample
           A_						=      0.1 * sample
           A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config()
                  A_						=      scheduler_class(**UpperCamelCase__       )
                  scheduler.set_timesteps(UpperCamelCase__       )

                  # copy over dummy past residuals (must be after setting timesteps)
                  A_						=      dummy_past_residuals[:]

                  if time_step is None:
                         A_						=      scheduler.timesteps[len(scheduler.timesteps       ) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__       )
                         A_						=      scheduler_class.from_pretrained(UpperCamelCase__       )
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(UpperCamelCase__       )

                         # copy over dummy past residual (must be after setting timesteps)
                         A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      self.scheduler_classes[0]
           A_						=      self.get_scheduler_config(**UpperCamelCase__       )
           A_						=      scheduler_class(**UpperCamelCase__       )

           A_						=      10
           A_						=      self.dummy_model()
           A_						=      self.dummy_sample_deter
           scheduler.set_timesteps(UpperCamelCase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                  A_						=      model(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ).prev_sample

           for i, t in enumerate(scheduler.timesteps       ):
                  A_						=      model(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ).prev_sample

           return sample



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





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )

           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config()
                  A_						=      scheduler_class(**UpperCamelCase__       )

                  A_						=      self.dummy_sample
                  A_						=      0.1 * sample

                  if num_inference_steps is not None and hasattr(UpperCamelCase__		,				"""set_timesteps"""       ):
                         scheduler.set_timesteps(UpperCamelCase__       )
                  elif num_inference_steps is not None and not hasattr(UpperCamelCase__		,				"""set_timesteps"""       ):
                         A_						=      num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                  A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.timesteps[5]
                  A_						=      scheduler.timesteps[6]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  self.assertEqual(output_a.shape		,				sample.shape       )
                  self.assertEqual(output_a.shape		,				output_a.shape       )

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

                  self.assertEqual(output_a.shape		,				sample.shape       )
                  self.assertEqual(output_a.shape		,				output_a.shape       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__		,				time_step=UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           for t, num_inference_steps in zip([1, 5, 10]		,				[10, 50, 100]       ):
                  self.check_over_forward(num_inference_steps=UpperCamelCase__		,				time_step=UpperCamelCase__       )







    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      self.full_loop()
           A_						=      torch.mean(torch.abs(UpperCamelCase__       )       )

           assert abs(result_mean.item() - 2540529       ) < 10
