




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					List[str]     					=    (LMSDiscreteScheduler,)
     _SCREAMING_SNAKE_CASE      :					Union[str, Any]     					=    10
     def        lowerCAmelCase__  (				self ,    **_lowerCAmelCase	):
            a			   ={
                """num_train_timesteps""": 1_100,
                """beta_start""": 0.00_01,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
            }

            config.update(**_lowerCAmelCase	)
            return config
     def        lowerCAmelCase__  (				self	):
            for timesteps in [10, 50, 100, 1_000]:
                   self.check_over_configs(num_train_timesteps=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,    [0.00_02, 0.0_02, 0.02]	):
                   self.check_over_configs(beta_start=_lowerCAmelCase ,    beta_end=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for schedule in ["linear", "scaled_linear"]:
                   self.check_over_configs(beta_schedule=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(prediction_type=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for t in [0, 500, 800]:
                   self.check_over_forward(time_step=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.scheduler_classes[0]
            a			   =self.get_scheduler_config()
            a			   =scheduler_class(**_lowerCAmelCase	)

            scheduler.set_timesteps(self.num_inference_steps	)

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

            for i, t in enumerate(scheduler.timesteps	):
                   a			   =scheduler.scale_model_input(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =model(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =scheduler.step(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
                   a			   =output.prev_sample

            a			   =torch.sum(torch.abs(_lowerCAmelCase	)	)
            a			   =torch.mean(torch.abs(_lowerCAmelCase	)	)

            assert abs(result_sum.item() - 10_06.3_88	) < 1E-2
            assert abs(result_mean.item() - 1.31	) < 1E-3
     def        lowerCAmelCase__  (				self	):
            a			   =self.scheduler_classes[0]
            a			   =self.get_scheduler_config(prediction_type="""v_prediction"""	)
            a			   =scheduler_class(**_lowerCAmelCase	)

            scheduler.set_timesteps(self.num_inference_steps	)

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

            for i, t in enumerate(scheduler.timesteps	):
                   a			   =scheduler.scale_model_input(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =model(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =scheduler.step(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
                   a			   =output.prev_sample

            a			   =torch.sum(torch.abs(_lowerCAmelCase	)	)
            a			   =torch.mean(torch.abs(_lowerCAmelCase	)	)

            assert abs(result_sum.item() - 0.00_17	) < 1E-2
            assert abs(result_mean.item() - 2.2_676E-06	) < 1E-3
     def        lowerCAmelCase__  (				self	):
            a			   =self.scheduler_classes[0]
            a			   =self.get_scheduler_config()
            a			   =scheduler_class(**_lowerCAmelCase	)

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

            a			   =self.dummy_model()
            a			   =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            a			   =sample.to(_lowerCAmelCase	)

            for i, t in enumerate(scheduler.timesteps	):
                   a			   =scheduler.scale_model_input(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =model(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =scheduler.step(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
                   a			   =output.prev_sample

            a			   =torch.sum(torch.abs(_lowerCAmelCase	)	)
            a			   =torch.mean(torch.abs(_lowerCAmelCase	)	)

            assert abs(result_sum.item() - 10_06.3_88	) < 1E-2
            assert abs(result_mean.item() - 1.31	) < 1E-3






     def        lowerCAmelCase__  (				self	):
            a			   =self.scheduler_classes[0]
            a			   =self.get_scheduler_config()
            a			   =scheduler_class(**_lowerCAmelCase ,    use_karras_sigmas=_lowerCAmelCase	)

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

            a			   =self.dummy_model()
            a			   =self.dummy_sample_deter.to(_lowerCAmelCase	) * scheduler.init_noise_sigma
            a			   =sample.to(_lowerCAmelCase	)

            for t in scheduler.timesteps:
                   a			   =scheduler.scale_model_input(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =model(_lowerCAmelCase ,    _lowerCAmelCase	)

                   a			   =scheduler.step(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
                   a			   =output.prev_sample

            a			   =torch.sum(torch.abs(_lowerCAmelCase	)	)
            a			   =torch.mean(torch.abs(_lowerCAmelCase	)	)

            assert abs(result_sum.item() - 38_12.99_27	) < 2E-2
            assert abs(result_mean.item() - 4.96_48	) < 1E-3






from __future__ import annotations

from typing import Any


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):  # noqa: A002
            a			   =item
            a			   =next



class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self	):
            a			   =None
            a			   =0
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            a			   =Node(_lowerCAmelCase ,    self.head	)
            self.size += 1
     def        lowerCAmelCase__  (				self	):
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   a			   =self.head.item
                   a			   =self.head.next
                   self.size -= 1
                   return item
     def        lowerCAmelCase__  (				self	):
            return self.head is None
     def __str__(				self	):
            if self.is_empty():
                   return ""
            else:
                   a			   =self.head
                   a			   =""""""
                   a			   =[]
                   while iterate:
                          item_list.append(str(iterate.item	)	)
                          a			   =iterate.next

                   a			   =""" --> """.join(_lowerCAmelCase	)

                   return item_str






     def __len__(				self	):
            return self.size
