






"""simple docstring"""







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      (DEISMultistepScheduler,)
    SCREAMING_SNAKE_CASE		=      (('''num_inference_steps''', 25),)






    def     _UpperCamelCase							(			self     ,**A ):
          UpperCAmelCase          =	{
              """num_train_timesteps""": 1_000,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """solver_order""": 2,
          }

          config.update(**A )
          return config






    def     _UpperCamelCase							(			self     ,A=0     ,**A ):
          UpperCAmelCase          =	dict(self.forward_default_kwargs )
          UpperCAmelCase          =	kwargs.pop("""num_inference_steps"""     ,A )
          UpperCAmelCase          =	self.dummy_sample
          UpperCAmelCase          =	0.1 * sample
          UpperCAmelCase          =	[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase          =	self.get_scheduler_config(**A )
                UpperCAmelCase          =	scheduler_class(**A )
                scheduler.set_timesteps(A )
                # copy over dummy past residuals
                UpperCAmelCase          =	dummy_past_residuals[: scheduler.config.solver_order]

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

                UpperCAmelCase      , UpperCAmelCase          =	sample, sample
                for t in range(A     ,time_step + scheduler.config.solver_order + 1 ):
                      UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample
                      UpperCAmelCase          =	new_scheduler.step(A     ,A     ,A     ,**A ).prev_sample

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






    def     _UpperCamelCase							(			self ):
          pass






    def     _UpperCamelCase							(			self     ,A=0     ,**A ):
          UpperCAmelCase          =	dict(self.forward_default_kwargs )
          UpperCAmelCase          =	kwargs.pop("""num_inference_steps"""     ,A )
          UpperCAmelCase          =	self.dummy_sample
          UpperCAmelCase          =	0.1 * sample
          UpperCAmelCase          =	[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase          =	self.get_scheduler_config()
                UpperCAmelCase          =	scheduler_class(**A )
                scheduler.set_timesteps(A )

                # copy over dummy past residuals (must be after setting timesteps)
                UpperCAmelCase          =	dummy_past_residuals[: scheduler.config.solver_order]

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

                      # copy over dummy past residual (must be after setting timesteps)
                      UpperCAmelCase          =	dummy_past_residuals[: new_scheduler.config.solver_order]

                UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample
                UpperCAmelCase          =	new_scheduler.step(A     ,A     ,A     ,**A ).prev_sample

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






    def     _UpperCamelCase							(			self     ,A=None     ,**A ):
          if scheduler is None:
                UpperCAmelCase          =	self.scheduler_classes[0]
                UpperCAmelCase          =	self.get_scheduler_config(**A )
                UpperCAmelCase          =	scheduler_class(**A )

          UpperCAmelCase          =	self.scheduler_classes[0]
          UpperCAmelCase          =	self.get_scheduler_config(**A )
          UpperCAmelCase          =	scheduler_class(**A )

          UpperCAmelCase          =	10
          UpperCAmelCase          =	self.dummy_model()
          UpperCAmelCase          =	self.dummy_sample_deter
          scheduler.set_timesteps(A )

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

          return sample






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	dict(self.forward_default_kwargs )

          UpperCAmelCase          =	kwargs.pop("""num_inference_steps"""     ,A )

          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase          =	self.get_scheduler_config()
                UpperCAmelCase          =	scheduler_class(**A )

                UpperCAmelCase          =	self.dummy_sample
                UpperCAmelCase          =	0.1 * sample

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

                # copy over dummy past residuals (must be done after set_timesteps)
                UpperCAmelCase          =	[residual + 0.2, residual + 0.15, residual + 0.10]
                UpperCAmelCase          =	dummy_past_residuals[: scheduler.config.solver_order]

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

                UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample
                UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample

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






    def     _UpperCamelCase							(			self ):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCAmelCase          =	DEISMultistepScheduler(**self.get_scheduler_config() )
          UpperCAmelCase          =	self.full_loop(scheduler=A )
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.23916 ) < 1e-3

          UpperCAmelCase          =	DPMSolverSinglestepScheduler.from_config(scheduler.config )
          UpperCAmelCase          =	DPMSolverMultistepScheduler.from_config(scheduler.config )
          UpperCAmelCase          =	UniPCMultistepScheduler.from_config(scheduler.config )
          UpperCAmelCase          =	DEISMultistepScheduler.from_config(scheduler.config )

          UpperCAmelCase          =	self.full_loop(scheduler=A )
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.23916 ) < 1e-3






    def     _UpperCamelCase							(			self ):
          for timesteps in [25, 50, 100, 999, 1_000]:
                self.check_over_configs(num_train_timesteps=A )






    def     _UpperCamelCase							(			self ):
          self.check_over_configs(thresholding=A )
          for order in [1, 2, 3]:
                for solver_type in ["logrho"]:
                      for threshold in [0.5, 1.0, 2.0]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      thresholding=A     ,prediction_type=A     ,sample_max_value=A     ,algorithm_type="""deis"""     ,solver_order=A     ,solver_type=A     ,)






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






    def     _UpperCamelCase							(			self ):
          for algorithm_type in ["deis"]:
                for solver_type in ["logrho"]:
                      for order in [1, 2, 3]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      solver_order=A     ,solver_type=A     ,prediction_type=A     ,algorithm_type=A     ,)
                                  UpperCAmelCase          =	self.full_loop(
                                      solver_order=A     ,solver_type=A     ,prediction_type=A     ,algorithm_type=A     ,)
                                  assert not torch.isnan(A ).any(), "Samples have nan numbers"






    def     _UpperCamelCase							(			self ):
          self.check_over_configs(lower_order_final=A )
          self.check_over_configs(lower_order_final=A )






    def     _UpperCamelCase							(			self ):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
                self.check_over_forward(num_inference_steps=A     ,time_step=0 )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.full_loop()
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.23916 ) < 1e-3






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.full_loop(prediction_type="""v_prediction""" )
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.091 ) < 1e-3






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.scheduler_classes[0]
          UpperCAmelCase          =	self.get_scheduler_config(thresholding=A     ,dynamic_thresholding_ratio=0 )
          UpperCAmelCase          =	scheduler_class(**A )

          UpperCAmelCase          =	10
          UpperCAmelCase          =	self.dummy_model()
          UpperCAmelCase          =	self.dummy_sample_deter.half()
          scheduler.set_timesteps(A )

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

          assert sample.dtype == torch.floataa











"""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


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







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          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     _UpperCamelCase							(			self     ,A     ,A ):
          # 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
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

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

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



