






"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class     A_				(   A_	):
				UpperCAmelCase__										=					(KDPMaAncestralDiscreteScheduler,)
				UpperCAmelCase__										=					1_0







				def      _snake_case       (				self				:    List[Any]    ,     **__lowerCamelCase				:    List[str]						)					->       Union[str, Any]:
										__magic_name__      =  {
										    "num_train_timesteps": 1_1_0_0,
										    "beta_start": 0.0001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**lowerCamelCase_						)
										return config







				def      _snake_case       (				self				:    Tuple						)					->       Optional[int]:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=lowerCamelCase_						)







				def      _snake_case       (				self				:    Optional[int]						)					->       Tuple:
										for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]    ,     [0.0002, 0.002, 0.02]						):
																self.check_over_configs(beta_start=lowerCamelCase_    ,     beta_end=lowerCamelCase_						)







				def      _snake_case       (				self				:    List[str]						)					->       List[str]:
										for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=lowerCamelCase_						)







				def      _snake_case       (				self				:    str						)					->       Any:
										if torch_device == "mps":
																return
										__magic_name__      =  self.scheduler_classes[0]
										__magic_name__      =  self.get_scheduler_config()
										__magic_name__      =  scheduler_class(**lowerCamelCase_						)

										scheduler.set_timesteps(self.num_inference_steps						)

										__magic_name__      =  torch.manual_seed(0						)

										__magic_name__      =  self.dummy_model()
										__magic_name__      =  self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__      =  sample.to(lowerCamelCase_						)

										for i, t in enumerate(scheduler.timesteps						):
																__magic_name__      =  scheduler.scale_model_input(lowerCamelCase_    ,     lowerCamelCase_						)

																__magic_name__      =  model(lowerCamelCase_    ,     lowerCamelCase_						)

																__magic_name__      =  scheduler.step(lowerCamelCase_    ,     lowerCamelCase_    ,     lowerCamelCase_    ,     generator=lowerCamelCase_						)
																__magic_name__      =  output.prev_sample

										__magic_name__      =  torch.sum(torch.abs(lowerCamelCase_						)						)
										__magic_name__      =  torch.mean(torch.abs(lowerCamelCase_						)						)

										assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7						) < 1e-2
										assert abs(result_mean.item() - 18.0331						) < 5e-3







				def      _snake_case       (				self				:    Optional[Any]						)					->       Optional[int]:
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=lowerCamelCase_						)







				def      _snake_case       (				self				:    str						)					->       Union[str, Any]:
										if torch_device == "mps":
																return
										__magic_name__      =  self.scheduler_classes[0]
										__magic_name__      =  self.get_scheduler_config(prediction_type="v_prediction"						)
										__magic_name__      =  scheduler_class(**lowerCamelCase_						)

										scheduler.set_timesteps(self.num_inference_steps						)

										__magic_name__      =  self.dummy_model()
										__magic_name__      =  self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__      =  sample.to(lowerCamelCase_						)

										__magic_name__      =  torch.manual_seed(0						)

										for i, t in enumerate(scheduler.timesteps						):
																__magic_name__      =  scheduler.scale_model_input(lowerCamelCase_    ,     lowerCamelCase_						)

																__magic_name__      =  model(lowerCamelCase_    ,     lowerCamelCase_						)

																__magic_name__      =  scheduler.step(lowerCamelCase_    ,     lowerCamelCase_    ,     lowerCamelCase_    ,     generator=lowerCamelCase_						)
																__magic_name__      =  output.prev_sample

										__magic_name__      =  torch.sum(torch.abs(lowerCamelCase_						)						)
										__magic_name__      =  torch.mean(torch.abs(lowerCamelCase_						)						)

										assert abs(result_sum.item() - 3_2_8.9_9_7_0						) < 1e-2
										assert abs(result_mean.item() - 0.4284						) < 1e-3







				def      _snake_case       (				self				:    Optional[Any]						)					->       Dict:
										if torch_device == "mps":
																return
										__magic_name__      =  self.scheduler_classes[0]
										__magic_name__      =  self.get_scheduler_config()
										__magic_name__      =  scheduler_class(**lowerCamelCase_						)

										scheduler.set_timesteps(self.num_inference_steps    ,     device=lowerCamelCase_						)
										__magic_name__      =  torch.manual_seed(0						)

										__magic_name__      =  self.dummy_model()
										__magic_name__      =  self.dummy_sample_deter.to(lowerCamelCase_						) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
																__magic_name__      =  scheduler.scale_model_input(lowerCamelCase_    ,     lowerCamelCase_						)

																__magic_name__      =  model(lowerCamelCase_    ,     lowerCamelCase_						)

																__magic_name__      =  scheduler.step(lowerCamelCase_    ,     lowerCamelCase_    ,     lowerCamelCase_    ,     generator=lowerCamelCase_						)
																__magic_name__      =  output.prev_sample

										__magic_name__      =  torch.sum(torch.abs(lowerCamelCase_						)						)
										__magic_name__      =  torch.mean(torch.abs(lowerCamelCase_						)						)

										assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8						) < 1e-1
										assert abs(result_mean.item() - 18.0331						) < 1e-3




"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A       			=      {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      ["""RealmTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      [
     """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RealmEmbedder""",
     """RealmForOpenQA""",
     """RealmKnowledgeAugEncoder""",
     """RealmPreTrainedModel""",
     """RealmReader""",
     """RealmScorer""",
     """load_tf_weights_in_realm""",
 ]
 __A       			=      ["""RealmRetriever"""]


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 __A       			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
