





'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class       _UpperCamelCase      (			_A			):






				'''simple docstring'''





				SCREAMING_SNAKE_CASE:Union[str, Any]         =	(DDIMParallelScheduler,)
				SCREAMING_SNAKE_CASE:Any         =	(('eta', 0.0), ('num_inference_steps', 50))






				def        lowercase__   (				self       ,					**_a    ):
											"""simple docstring"""
											a__            =							{
											    '''num_train_timesteps''': 1000,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''clip_sample''': True,
											}

											config.update(**__A    )
											return config






				def        lowercase__   (				self       ,					**_a    ):
											"""simple docstring"""
											a__            =							self.scheduler_classes[0]
											a__            =							self.get_scheduler_config(**__A    )
											a__            =							scheduler_class(**__A    )

											a__            =							10, 0.0

											a__            =							self.dummy_model()
											a__            =							self.dummy_sample_deter

											scheduler.set_timesteps(__A    )

											for t in scheduler.timesteps:
																		a__            =							model(__A       ,					__A    )
																		a__            =							scheduler.step(__A       ,					__A       ,					__A       ,					__A    ).prev_sample

											return sample






				def        lowercase__   (				self    ):
											"""simple docstring"""
											for timesteps in [100, 500, 1000]:
																		self.check_over_configs(num_train_timesteps=__A    )






				def        lowercase__   (				self    ):
											"""simple docstring"""
											for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=__A    )

											a__            =							self.scheduler_classes[0]
											a__            =							self.get_scheduler_config(steps_offset=1    )
											a__            =							scheduler_class(**__A    )
											scheduler.set_timesteps(5    )
											assert torch.equal(scheduler.timesteps       ,					torch.LongTensor([801, 601, 401, 201, 1]    )    )






				def        lowercase__   (				self    ):
											"""simple docstring"""
											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=__A       ,					beta_end=__A    )






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






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






				def        lowercase__   (				self    ):
											"""simple docstring"""
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__A    )






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






				def        lowercase__   (				self    ):
											"""simple docstring"""
											for rescale_betas_zero_snr in [True, False]:
																		self.check_over_configs(rescale_betas_zero_snr=__A    )






				def        lowercase__   (				self    ):
											"""simple docstring"""
											self.check_over_configs(thresholding=__A    )
											for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=__A       ,					prediction_type=__A       ,					sample_max_value=__A       ,					)






				def        lowercase__   (				self    ):
											"""simple docstring"""
											for t in [1, 10, 49]:
																		self.check_over_forward(time_step=__A    )






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






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






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.scheduler_classes[0]
											a__            =							self.get_scheduler_config()
											a__            =							scheduler_class(**__A    )

											assert torch.sum(torch.abs(scheduler._get_variance(0       ,					0    ) - 0.0    )    ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(420       ,					400    ) - 0.1_4771    )    ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(980       ,					960    ) - 0.3_2460    )    ) < 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    ) - 0.0_0979    )    ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999       ,					998    ) - 0.02    )    ) < 1e-5






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.scheduler_classes[0]
											a__            =							self.get_scheduler_config()
											a__            =							scheduler_class(**__A    )

											a__            =							10, 0.0
											scheduler.set_timesteps(__A    )

											a__            =							self.dummy_model()
											a__            =							self.dummy_sample_deter
											a__            =							self.dummy_sample_deter + 0.1
											a__            =							self.dummy_sample_deter - 0.1

											a__            =							samplea.shape[0]
											a__            =							torch.stack([samplea, samplea, samplea]       ,					dim=0    )
											a__            =							torch.arange(__A    )[0:3, None].repeat(1       ,					__A    )

											a__            =							model(samples.flatten(0       ,					1    )       ,					timesteps.flatten(0       ,					1    )    )
											a__            =							scheduler.batch_step_no_noise(__A       ,					timesteps.flatten(0       ,					1    )       ,					samples.flatten(0       ,					1    )       ,					__A    )

											a__            =							torch.sum(torch.abs(__A    )    )
											a__            =							torch.mean(torch.abs(__A    )    )

											assert abs(result_sum.item() - 1147.7904    ) < 1e-2
											assert abs(result_mean.item() - 0.4982    ) < 1e-3






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.full_loop()

											a__            =							torch.sum(torch.abs(__A    )    )
											a__            =							torch.mean(torch.abs(__A    )    )

											assert abs(result_sum.item() - 172.0067    ) < 1e-2
											assert abs(result_mean.item() - 0.22_3967    ) < 1e-3






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.full_loop(prediction_type='v_prediction'    )

											a__            =							torch.sum(torch.abs(__A    )    )
											a__            =							torch.mean(torch.abs(__A    )    )

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






				def        lowercase__   (				self    ):
											"""simple docstring"""
											# We specify different beta, so that the first alpha is 0.99
											a__            =							self.full_loop(set_alpha_to_one=__A       ,					beta_start=0.01    )
											a__            =							torch.sum(torch.abs(__A    )    )
											a__            =							torch.mean(torch.abs(__A    )    )

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






				def        lowercase__   (				self    ):
											"""simple docstring"""
											# We specify different beta, so that the first alpha is 0.99
											a__            =							self.full_loop(set_alpha_to_one=__A       ,					beta_start=0.01    )
											a__            =							torch.sum(torch.abs(__A    )    )
											a__            =							torch.mean(torch.abs(__A    )    )

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase											=    {
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase											=    [
     """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """FalconForCausalLM""",
     """FalconModel""",
     """FalconPreTrainedModel""",
     """FalconForSequenceClassification""",
     """FalconForTokenClassification""",
     """FalconForQuestionAnswering""",
 ]


if TYPE_CHECKING:
 from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_falcon import (
      FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
      FalconPreTrainedModel,
  )


else:
 import sys

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