



"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			(DDPMScheduler,)



      def 						snake_case_ (					self					:     Optional[int]					,     **A					:     Dict						):
           _UpperCAmelCase      :     str        =   {
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
               "variance_type": "fixed_small",
               "clip_sample": True,
           }

           config.update(**A						)
           return config



      def 						snake_case_ (					self					:     List[Any]						):
           for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=A						)



      def 						snake_case_ (					self					:     Union[str, Any]						):
           for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,     [0.002, 0.02, 0.2, 2]						):
                self.check_over_configs(beta_start=A					,     beta_end=A						)



      def 						snake_case_ (					self					:     Any						):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=A						)



      def 						snake_case_ (					self					:     Any						):
           for variance in ["fixed_small", "fixed_large", "other"]:
                self.check_over_configs(variance_type=A						)



      def 						snake_case_ (					self					:     Dict						):
           for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=A						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           self.check_over_configs(thresholding=A						)
           for threshold in [0.5, 1.0, 2.0]:
                for prediction_type in ["epsilon", "sample", "v_prediction"]:
                     self.check_over_configs(
                         thresholding=A					,     prediction_type=A					,     sample_max_value=A					,     )



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



      def 						snake_case_ (					self					:     Optional[int]						):
           for t in [0, 5_0_0, 9_9_9]:
                self.check_over_forward(time_step=A						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     str        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     List[str]        =   self.get_scheduler_config()
           _UpperCAmelCase      :     Tuple        =   scheduler_class(**A						)

           assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_8_7						) - 0.00_979						)						) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.02						)						) < 1e-5



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     List[Any]        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     Optional[int]        =   self.get_scheduler_config()
           _UpperCAmelCase      :     int        =   scheduler_class(**A						)

           _UpperCAmelCase      :     List[str]        =   len(A						)

           _UpperCAmelCase      :     List[Any]        =   self.dummy_model()
           _UpperCAmelCase      :     Union[str, Any]        =   self.dummy_sample_deter
           _UpperCAmelCase      :     Optional[Any]        =   torch.manual_seed(0						)

           for t in reversed(range(A						)						):
                # 1. predict noise residual
                _UpperCAmelCase      :     Optional[int]        =   model(A					,     A						)

                # 2. predict previous mean of sample x_t-1
                _UpperCAmelCase      :     Dict        =   scheduler.step(A					,     A					,     A					,     generator=A						).prev_sample

                # if t > 0:
                #     noise = self.dummy_sample_deter
                #     variance = scheduler.get_variance(t) ** (0.5) * noise
                #
                # sample = pred_prev_sample + variance
                _UpperCAmelCase      :     Optional[int]        =   pred_prev_sample

           _UpperCAmelCase      :     List[Any]        =   torch.sum(torch.abs(A						)						)
           _UpperCAmelCase      :     Tuple        =   torch.mean(torch.abs(A						)						)

           assert abs(result_sum.item() - 258.9_606						) < 1e-2
           assert abs(result_mean.item() - 0.3_372						) < 1e-3



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     List[str]        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     List[str]        =   self.get_scheduler_config(prediction_type="v_prediction"						)
           _UpperCAmelCase      :     int        =   scheduler_class(**A						)

           _UpperCAmelCase      :     Union[str, Any]        =   len(A						)

           _UpperCAmelCase      :     List[Any]        =   self.dummy_model()
           _UpperCAmelCase      :     List[str]        =   self.dummy_sample_deter
           _UpperCAmelCase      :     int        =   torch.manual_seed(0						)

           for t in reversed(range(A						)						):
                # 1. predict noise residual
                _UpperCAmelCase      :     Union[str, Any]        =   model(A					,     A						)

                # 2. predict previous mean of sample x_t-1
                _UpperCAmelCase      :     Tuple        =   scheduler.step(A					,     A					,     A					,     generator=A						).prev_sample

                # if t > 0:
                #     noise = self.dummy_sample_deter
                #     variance = scheduler.get_variance(t) ** (0.5) * noise
                #
                # sample = pred_prev_sample + variance
                _UpperCAmelCase      :     Tuple        =   pred_prev_sample

           _UpperCAmelCase      :     Dict        =   torch.sum(torch.abs(A						)						)
           _UpperCAmelCase      :     Optional[int]        =   torch.mean(torch.abs(A						)						)

           assert abs(result_sum.item() - 202.0_296						) < 1e-2
           assert abs(result_mean.item() - 0.2_631						) < 1e-3



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     str        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     str        =   self.get_scheduler_config()
           _UpperCAmelCase      :     Tuple        =   scheduler_class(**A						)

           _UpperCAmelCase      :     Optional[Any]        =   [1_0_0, 8_7, 5_0, 1, 0]

           scheduler.set_timesteps(timesteps=A						)

           _UpperCAmelCase      :     Tuple        =   scheduler.timesteps

           for i, timestep in enumerate(A						):
                if i == len(A						) - 1:
                     _UpperCAmelCase      :     Optional[int]        =   -1
                else:
                     _UpperCAmelCase      :     List[Any]        =   timesteps[i + 1]

                _UpperCAmelCase      :     Optional[int]        =   scheduler.previous_timestep(A						)
                _UpperCAmelCase      :     Optional[int]        =   prev_t.item()

                self.assertEqual(A					,     A						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Optional[int]        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     Dict        =   self.get_scheduler_config()
           _UpperCAmelCase      :     Tuple        =   scheduler_class(**A						)

           _UpperCAmelCase      :     List[Any]        =   [1_0_0, 8_7, 5_0, 5_1, 0]

           with self.assertRaises(A					,     msg="`custom_timesteps` must be in descending order."						):
                scheduler.set_timesteps(timesteps=A						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     Dict        =   self.get_scheduler_config()
           _UpperCAmelCase      :     Optional[int]        =   scheduler_class(**A						)

           _UpperCAmelCase      :     List[Any]        =   [1_0_0, 8_7, 5_0, 1, 0]
           _UpperCAmelCase      :     Tuple        =   len(A						)

           with self.assertRaises(A					,     msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."						):
                scheduler.set_timesteps(num_inference_steps=A					,     timesteps=A						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Dict        =   self.scheduler_classes[0]
           _UpperCAmelCase      :     Optional[int]        =   self.get_scheduler_config()
           _UpperCAmelCase      :     List[str]        =   scheduler_class(**A						)

           _UpperCAmelCase      :     Tuple        =   [scheduler.config.num_train_timesteps]

           with self.assertRaises(
               A					,     msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"					,     ):
                scheduler.set_timesteps(timesteps=A						)





"""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    : Union[str, Any]							=							get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 _lowerCAmelCase    : Tuple							=							json.load(f)






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           return FSMTTokenizer.from_pretrained(A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   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 						snake_case_ (					self					:     Any					,     A					:     Dict					,     A					:     List[str]						):
           # 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      :     Any        =   f'facebook/wmt19-{pair}'
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer(A						)
           _UpperCAmelCase      :     Optional[int]        =   self.get_model(A						)

           _UpperCAmelCase      :     int        =   bleu_data[pair]["src"]
           _UpperCAmelCase      :     Optional[int]        =   bleu_data[pair]["tgt"]

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