




"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     (DDPMScheduler,)
			def    A_       (    self    , **lowercase    ):
							_lowerCamelCase	:     Dict       =       {
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**lowercase    )
							return config
			def    A_       (    self    ):
							for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase    )
			def    A_       (    self    ):
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    , [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=lowercase    , beta_end=lowercase    )
			def    A_       (    self    ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowercase    )
			def    A_       (    self    ):
							for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowercase    )
			def    A_       (    self    ):
							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowercase    )
			def    A_       (    self    ):
							self.check_over_configs(thresholding=lowercase    )
							for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(
															    thresholding=lowercase    , prediction_type=lowercase    , sample_max_value=lowercase    , )
			def    A_       (    self    ):
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=lowercase    )
			def    A_       (    self    ):
							for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       self.scheduler_classes[0]
							_lowerCamelCase	:     List[str]       =       self.get_scheduler_config()
							_lowerCamelCase	:     Optional[int]       =       scheduler_class(**lowercase    )

							assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_09_79    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1E-5
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.scheduler_classes[0]
							_lowerCamelCase	:     Dict       =       self.get_scheduler_config()
							_lowerCamelCase	:     int       =       scheduler_class(**lowercase    )

							_lowerCamelCase	:     Optional[int]       =       len(lowercase    )

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

							for t in reversed(range(lowercase    )    ):
											# 1. predict noise residual
											_lowerCamelCase	:     Tuple       =       model(lowercase    , lowercase    )

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

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

							_lowerCamelCase	:     List[Any]       =       torch.sum(torch.abs(lowercase    )    )
							_lowerCamelCase	:     Tuple       =       torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 2_58.96_06    ) < 1E-2
							assert abs(result_mean.item() - 0.33_72    ) < 1E-3
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.scheduler_classes[0]
							_lowerCamelCase	:     Optional[int]       =       self.get_scheduler_config(prediction_type='v_prediction'    )
							_lowerCamelCase	:     Optional[Any]       =       scheduler_class(**lowercase    )

							_lowerCamelCase	:     Tuple       =       len(lowercase    )

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

							for t in reversed(range(lowercase    )    ):
											# 1. predict noise residual
											_lowerCamelCase	:     str       =       model(lowercase    , lowercase    )

											# 2. predict previous mean of sample x_t-1
											_lowerCamelCase	:     Optional[Any]       =       scheduler.step(lowercase    , lowercase    , lowercase    , generator=lowercase    ).prev_sample

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

							_lowerCamelCase	:     List[Any]       =       torch.sum(torch.abs(lowercase    )    )
							_lowerCamelCase	:     List[str]       =       torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 2_02.02_96    ) < 1E-2
							assert abs(result_mean.item() - 0.26_31    ) < 1E-3
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       self.scheduler_classes[0]
							_lowerCamelCase	:     Optional[int]       =       self.get_scheduler_config()
							_lowerCamelCase	:     Union[str, Any]       =       scheduler_class(**lowercase    )

							_lowerCamelCase	:     Optional[Any]       =       [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=lowercase    )

							_lowerCamelCase	:     Union[str, Any]       =       scheduler.timesteps

							for i, timestep in enumerate(lowercase    ):
											if i == len(lowercase    ) - 1:
															_lowerCamelCase	:     Dict       =       -1
											else:
															_lowerCamelCase	:     Dict       =       timesteps[i + 1]

											_lowerCamelCase	:     Union[str, Any]       =       scheduler.previous_timestep(lowercase    )
											_lowerCamelCase	:     Optional[int]       =       prev_t.item()

											self.assertEqual(lowercase    , lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.scheduler_classes[0]
							_lowerCamelCase	:     Any       =       self.get_scheduler_config()
							_lowerCamelCase	:     Optional[Any]       =       scheduler_class(**lowercase    )

							_lowerCamelCase	:     Dict       =       [100, 87, 50, 51, 0]

							with self.assertRaises(lowercase    , msg='`custom_timesteps` must be in descending order.'    ):
											scheduler.set_timesteps(timesteps=lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.scheduler_classes[0]
							_lowerCamelCase	:     List[Any]       =       self.get_scheduler_config()
							_lowerCamelCase	:     int       =       scheduler_class(**lowercase    )

							_lowerCamelCase	:     Tuple       =       [100, 87, 50, 1, 0]
							_lowerCamelCase	:     Optional[Any]       =       len(lowercase    )

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






			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.scheduler_classes[0]
							_lowerCamelCase	:     str       =       self.get_scheduler_config()
							_lowerCamelCase	:     Dict       =       scheduler_class(**lowercase    )

							_lowerCamelCase	:     str       =       [scheduler.config.num_train_timesteps]

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





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """dpr"""
			def __init__(    self    , lowercase=30522    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-12    , lowercase=0    , lowercase="absolute"    , lowercase = 0    , **lowercase    , ):
							super().__init__(pad_token_id=lowercase    , **lowercase    )

							_lowerCamelCase	:     int       =       vocab_size
							_lowerCamelCase	:     Optional[int]       =       hidden_size
							_lowerCamelCase	:     str       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     Tuple       =       hidden_act
							_lowerCamelCase	:     int       =       intermediate_size
							_lowerCamelCase	:     str       =       hidden_dropout_prob
							_lowerCamelCase	:     Tuple       =       attention_probs_dropout_prob
							_lowerCamelCase	:     Union[str, Any]       =       max_position_embeddings
							_lowerCamelCase	:     str       =       type_vocab_size
							_lowerCamelCase	:     int       =       initializer_range
							_lowerCamelCase	:     Optional[int]       =       layer_norm_eps
							_lowerCamelCase	:     Dict       =       projection_dim
							_lowerCamelCase	:     int       =       position_embedding_type