




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 		UpperCamelCase__     (_UpperCAmelCase					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				(DDIMScheduler,)
    lowerCamelCase_				:     Optional[int]     					=				(("""eta""", 0.0), ("""num_inference_steps""", 5_0))







    def    _lowercase					(   self					,  **UpperCamelCase__					)					->				List[Any]:
           lowerCamelCase       :				List[str]      			= {
               "num_train_timesteps": 1000,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
               "clip_sample": True,
           }

           config.update(**lowercase__					)
           return config







    def    _lowercase					(   self					,  **UpperCamelCase__					)					->				Optional[Any]:
           lowerCamelCase       :				List[str]      			= self.scheduler_classes[0]
           lowerCamelCase       :				Optional[Any]      			= self.get_scheduler_config(**lowercase__					)
           lowerCamelCase       :				Union[str, Any]      			= scheduler_class(**lowercase__					)

           lowerCamelCase       :				List[str]      			= 10, 0.0

           lowerCamelCase       :				List[Any]      			= self.dummy_model()
           lowerCamelCase       :				Tuple      			= self.dummy_sample_deter

           scheduler.set_timesteps(lowercase__					)

           for t in scheduler.timesteps:
                  lowerCamelCase       :				List[Any]      			= model(lowercase__					,  lowercase__					)
                  lowerCamelCase       :				Dict      			= scheduler.step(lowercase__					,  lowercase__					,  lowercase__					,  lowercase__					).prev_sample

           return sample







    def    _lowercase					(   self					)					->				Dict:






           for timesteps in [100, 500, 1000]:
                  self.check_over_configs(num_train_timesteps=lowercase__					)







    def    _lowercase					(   self					)					->				Tuple:






           for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=lowercase__					)

           lowerCamelCase       :				List[Any]      			= self.scheduler_classes[0]
           lowerCamelCase       :				Any      			= self.get_scheduler_config(steps_offset=1					)
           lowerCamelCase       :				Union[str, Any]      			= scheduler_class(**lowercase__					)
           scheduler.set_timesteps(5					)
           assert torch.equal(scheduler.timesteps					,  torch.LongTensor([801, 601, 401, 201, 1]					)					)







    def    _lowercase					(   self					)					->				Tuple:






           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,  [0.002, 0.02, 0.2, 2]					):
                  self.check_over_configs(beta_start=lowercase__					,  beta_end=lowercase__					)







    def    _lowercase					(   self					)					->				int:






           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=lowercase__					)







    def    _lowercase					(   self					)					->				Any:






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







    def    _lowercase					(   self					)					->				str:






           for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=lowercase__					)







    def    _lowercase					(   self					)					->				List[str]:






           for timestep_spacing in ["trailing", "leading"]:
                  self.check_over_configs(timestep_spacing=lowercase__					)







    def    _lowercase					(   self					)					->				Tuple:






           for rescale_betas_zero_snr in [True, False]:
                  self.check_over_configs(rescale_betas_zero_snr=lowercase__					)







    def    _lowercase					(   self					)					->				List[Any]:
           self.check_over_configs(thresholding=lowercase__					)
           for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "v_prediction"]:
                         self.check_over_configs(
                             thresholding=lowercase__					,  prediction_type=lowercase__					,  sample_max_value=lowercase__					,  )







    def    _lowercase					(   self					)					->				str:






           for t in [1, 10, 49]:
                  self.check_over_forward(time_step=lowercase__					)







    def    _lowercase					(   self					)					->				Any:






           for t, num_inference_steps in zip([1, 10, 50]					,  [10, 50, 500]					):
                  self.check_over_forward(time_step=lowercase__					,  num_inference_steps=lowercase__					)







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






           for t, eta in zip([1, 10, 49]					,  [0.0, 0.5, 1.0]					):
                  self.check_over_forward(time_step=lowercase__					,  eta=lowercase__					)







    def    _lowercase					(   self					)					->				Dict:
           lowerCamelCase       :				Any      			= self.scheduler_classes[0]
           lowerCamelCase       :				str      			= self.get_scheduler_config()
           lowerCamelCase       :				List[Any]      			= scheduler_class(**lowercase__					)

           assert torch.sum(torch.abs(scheduler._get_variance(0					,  0					) - 0.0					)					) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(420					,  400					) - 0.14771					)					) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(980					,  960					) - 0.32460					)					) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(0					,  0					) - 0.0					)					) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487					,  486					) - 0.00979					)					) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999					,  998					) - 0.02					)					) < 1e-5







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				str      			= self.full_loop()

           lowerCamelCase       :				Optional[int]      			= torch.sum(torch.abs(lowercase__					)					)
           lowerCamelCase       :				Dict      			= torch.mean(torch.abs(lowercase__					)					)

           assert abs(result_sum.item() - 172.0067					) < 1e-2
           assert abs(result_mean.item() - 0.223967					) < 1e-3







    def    _lowercase					(   self					)					->				Dict:
           lowerCamelCase       :				Dict      			= self.full_loop(prediction_type="v_prediction"					)

           lowerCamelCase       :				Tuple      			= torch.sum(torch.abs(lowercase__					)					)
           lowerCamelCase       :				Dict      			= torch.mean(torch.abs(lowercase__					)					)

           assert abs(result_sum.item() - 52.5302					) < 1e-2
           assert abs(result_mean.item() - 0.0684					) < 1e-3







    def    _lowercase					(   self					)					->				Any:
           lowerCamelCase       :				int      			= self.full_loop(set_alpha_to_one=lowercase__					,  beta_start=0.01					)
           lowerCamelCase       :				Optional[Any]      			= torch.sum(torch.abs(lowercase__					)					)
           lowerCamelCase       :				Union[str, Any]      			= torch.mean(torch.abs(lowercase__					)					)

           assert abs(result_sum.item() - 149.8295					) < 1e-2
           assert abs(result_mean.item() - 0.1951					) < 1e-3







    def    _lowercase					(   self					)					->				Union[str, Any]:
           lowerCamelCase       :				Dict      			= self.full_loop(set_alpha_to_one=lowercase__					,  beta_start=0.01					)
           lowerCamelCase       :				List[str]      			= torch.sum(torch.abs(lowercase__					)					)
           lowerCamelCase       :				Optional[Any]      			= torch.mean(torch.abs(lowercase__					)					)

           assert abs(result_sum.item() - 149.0784					) < 1e-2
           assert abs(result_mean.item() - 0.1941					) < 1e-3












# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/






