

'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( _a				):
							lowerCAmelCase_				=	(LMSDiscreteScheduler,)
							lowerCAmelCase_				=	1_0







							def      snake_case				(							self     :	str  , **__lowercase     :	Union[str, Any] ):







												"""simple docstring"""







												__lowercase     						={
												    "num_train_timesteps": 1100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**SCREAMING_SNAKE_CASE__ )
												return config







							def      snake_case				(							self     :	Optional[Any] ):







												"""simple docstring"""







												for timesteps in [10, 50, 100, 1000]:
																	self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__ )







							def      snake_case				(							self     :	int ):







												"""simple docstring"""







												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  , [0.0_0_0_2, 0.0_0_2, 0.0_2] ):
																	self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  , beta_end=SCREAMING_SNAKE_CASE__ )







							def      snake_case				(							self     :	List[str] ):







												"""simple docstring"""







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







							def      snake_case				(							self     :	List[str] ):







												"""simple docstring"""







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







							def      snake_case				(							self     :	Dict ):







												"""simple docstring"""







												for t in [0, 500, 800]:
																	self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__ )







							def      snake_case				(							self     :	Any ):







												"""simple docstring"""







												__lowercase     						=self.scheduler_classes[0]
												__lowercase     						=self.get_scheduler_config()
												__lowercase     						=scheduler_class(**SCREAMING_SNAKE_CASE__ )

												scheduler.set_timesteps(self.num_inference_steps )

												__lowercase     						=self.dummy_model()
												__lowercase     						=self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(scheduler.timesteps ):
																	__lowercase     						=scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=model(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=scheduler.step(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )
																	__lowercase     						=output.prev_sample

												__lowercase     						=torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
												__lowercase     						=torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )

												assert abs(result_sum.item() - 1006.388 ) < 1E-2
												assert abs(result_mean.item() - 1.3_1 ) < 1E-3







							def      snake_case				(							self     :	Dict ):







												"""simple docstring"""







												__lowercase     						=self.scheduler_classes[0]
												__lowercase     						=self.get_scheduler_config(prediction_type='v_prediction' )
												__lowercase     						=scheduler_class(**SCREAMING_SNAKE_CASE__ )

												scheduler.set_timesteps(self.num_inference_steps )

												__lowercase     						=self.dummy_model()
												__lowercase     						=self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(scheduler.timesteps ):
																	__lowercase     						=scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=model(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=scheduler.step(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )
																	__lowercase     						=output.prev_sample

												__lowercase     						=torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
												__lowercase     						=torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )

												assert abs(result_sum.item() - 0.0_0_1_7 ) < 1E-2
												assert abs(result_mean.item() - 2.2_676E-06 ) < 1E-3







							def      snake_case				(							self     :	List[str] ):







												"""simple docstring"""







												__lowercase     						=self.scheduler_classes[0]
												__lowercase     						=self.get_scheduler_config()
												__lowercase     						=scheduler_class(**SCREAMING_SNAKE_CASE__ )

												scheduler.set_timesteps(self.num_inference_steps  , device=SCREAMING_SNAKE_CASE__ )

												__lowercase     						=self.dummy_model()
												__lowercase     						=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												__lowercase     						=sample.to(SCREAMING_SNAKE_CASE__ )

												for i, t in enumerate(scheduler.timesteps ):
																	__lowercase     						=scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=model(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=scheduler.step(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )
																	__lowercase     						=output.prev_sample

												__lowercase     						=torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
												__lowercase     						=torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )

												assert abs(result_sum.item() - 1006.388 ) < 1E-2
												assert abs(result_mean.item() - 1.3_1 ) < 1E-3







							def      snake_case				(							self     :	Any ):







												"""simple docstring"""







												__lowercase     						=self.scheduler_classes[0]
												__lowercase     						=self.get_scheduler_config()
												__lowercase     						=scheduler_class(**SCREAMING_SNAKE_CASE__  , use_karras_sigmas=SCREAMING_SNAKE_CASE__ )

												scheduler.set_timesteps(self.num_inference_steps  , device=SCREAMING_SNAKE_CASE__ )

												__lowercase     						=self.dummy_model()
												__lowercase     						=self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__ ) * scheduler.init_noise_sigma
												__lowercase     						=sample.to(SCREAMING_SNAKE_CASE__ )

												for t in scheduler.timesteps:
																	__lowercase     						=scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=model(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )

																	__lowercase     						=scheduler.step(SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__  , SCREAMING_SNAKE_CASE__ )
																	__lowercase     						=output.prev_sample

												__lowercase     						=torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
												__lowercase     						=torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )

												assert abs(result_sum.item() - 3812.9927 ) < 2E-2
												assert abs(result_mean.item() - 4.9_6_4_8 ) < 1E-3





'''simple docstring'''

from .number_theory.prime_numbers import next_prime


class 					lowerCAmelCase       :







       def __init__(							self     :	int  , __lowercase     :	int  , __lowercase     :	int | None = None  , __lowercase     :	float | None = None  , ):







            """simple docstring"""







            __lowercase     						=size_table
            __lowercase     						=[None] * self.size_table
            __lowercase     						=0.7_5 if lim_charge is None else lim_charge
            __lowercase     						=1 if charge_factor is None else charge_factor
            __lowercase     						=[]
            __lowercase     						={}







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self._keys







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            return sum(1 for slot in self.values if slot is not None ) / (
                self.size_table * self.charge_factor
            )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            return key % self.size_table







       def      snake_case				(							self     :	int  , __lowercase     :	Any ):







            """simple docstring"""







            print(f'''step {step_ord}''' )
            print(list(range(len(self.values ) ) ) )
            print(self.values )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=1
            __lowercase     						=values
            for value in values:
                 self.insert_data(__lowercase )
                 self._step_by_step(__lowercase )
                 i += 1







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Optional[int]  , __lowercase     :	List[str] ):







            """simple docstring"""







            __lowercase     						=data
            __lowercase     						=data







       def      snake_case				(							self     :	int  , __lowercase     :	Any  , __lowercase     :	Optional[Any]=None ):







            """simple docstring"""







            __lowercase     						=self.hash_function(key + 1 )

            while self.values[new_key] is not None and self.values[new_key] != key:
                 if self.values.count(__lowercase ) > 0:
                      __lowercase     						=self.hash_function(new_key + 1 )
                 else:
                      __lowercase     						=None
                      break

            return new_key







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=[value for value in self.values if value is not None]
            __lowercase     						=next_prime(self.size_table  , factor=2 )
            self._keys.clear()
            __lowercase     						=[None] * self.size_table  # hell's pointers D: don't DRY ;/
            for value in survivor_values:
                 self.insert_data(__lowercase )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=self.hash_function(__lowercase )

            if self.values[key] is None:
                 self._set_value(__lowercase  , __lowercase )

            elif self.values[key] == data:
                 pass

            else:
                 __lowercase     						=self._collision_resolution(__lowercase  , __lowercase )
                 if collision_resolution is not None:
                      self._set_value(__lowercase  , __lowercase )
                 else:
                      self.rehashing()
                      self.insert_data(__lowercase )


