
'''simple docstring'''






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		(DDPMScheduler,)



    def   lowerCamelCase_  (	self      :       str,**__A      :       List[str]     ):
          _lowerCamelCase				:   List[str]								=   {
              "num_train_timesteps": 1_0_0_0,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**__A     )
          return config



    def   lowerCamelCase_  (	self      :       str     ):
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__A     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          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   lowerCamelCase_  (	self      :       Any     ):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__A     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          for variance in ["fixed_small", "fixed_large", "other"]:
                self.check_over_configs(variance_type=__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=__A     )



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



    def   lowerCamelCase_  (	self      :       List[str]     ):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(prediction_type=__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          for t in [0, 5_0_0, 9_9_9]:
                self.check_over_forward(time_step=__A     )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   Optional[Any]								=   self.scheduler_classes[0]
          _lowerCamelCase				:   List[str]								=   self.get_scheduler_config()
          _lowerCamelCase				:   Union[str, Any]								=   scheduler_class(**__A     )

          assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.00979     )     ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.02     )     ) < 1e-5



    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   List[str]								=   self.scheduler_classes[0]
          _lowerCamelCase				:   List[str]								=   self.get_scheduler_config()
          _lowerCamelCase				:   Optional[int]								=   scheduler_class(**__A     )

          _lowerCamelCase				:   List[Any]								=   len(__A     )

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

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

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

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

          _lowerCamelCase				:   Dict								=   torch.sum(torch.abs(__A     )     )
          _lowerCamelCase				:   Tuple								=   torch.mean(torch.abs(__A     )     )

          assert abs(result_sum.item() - 258.9606     ) < 1e-2
          assert abs(result_mean.item() - 0.3372     ) < 1e-3



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Optional[Any]								=   self.scheduler_classes[0]
          _lowerCamelCase				:   Optional[Any]								=   self.get_scheduler_config(prediction_type="v_prediction"     )
          _lowerCamelCase				:   Optional[int]								=   scheduler_class(**__A     )

          _lowerCamelCase				:   List[str]								=   len(__A     )

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

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

                # 2. predict previous mean of sample x_t-1
                _lowerCamelCase				:   List[str]								=   scheduler.step(__A,__A,__A,generator=__A     ).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				:   Tuple								=   torch.sum(torch.abs(__A     )     )
          _lowerCamelCase				:   Union[str, Any]								=   torch.mean(torch.abs(__A     )     )

          assert abs(result_sum.item() - 202.0296     ) < 1e-2
          assert abs(result_mean.item() - 0.2631     ) < 1e-3



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   Tuple								=   self.scheduler_classes[0]
          _lowerCamelCase				:   List[str]								=   self.get_scheduler_config()
          _lowerCamelCase				:   Any								=   scheduler_class(**__A     )

          _lowerCamelCase				:   Union[str, Any]								=   [1_0_0, 8_7, 5_0, 1, 0]

          scheduler.set_timesteps(timesteps=__A     )

          _lowerCamelCase				:   List[str]								=   scheduler.timesteps

          for i, timestep in enumerate(__A     ):
                if i == len(__A     ) - 1:
                      _lowerCamelCase				:   Union[str, Any]								=   -1
                else:
                      _lowerCamelCase				:   List[str]								=   timesteps[i + 1]

                _lowerCamelCase				:   Optional[int]								=   scheduler.previous_timestep(__A     )
                _lowerCamelCase				:   int								=   prev_t.item()

                self.assertEqual(__A,__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   int								=   self.scheduler_classes[0]
          _lowerCamelCase				:   List[Any]								=   self.get_scheduler_config()
          _lowerCamelCase				:   List[Any]								=   scheduler_class(**__A     )

          _lowerCamelCase				:   Union[str, Any]								=   [1_0_0, 8_7, 5_0, 5_1, 0]

          with self.assertRaises(__A,msg="`custom_timesteps` must be in descending order."     ):
                scheduler.set_timesteps(timesteps=__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   int								=   self.scheduler_classes[0]
          _lowerCamelCase				:   List[str]								=   self.get_scheduler_config()
          _lowerCamelCase				:   List[str]								=   scheduler_class(**__A     )

          _lowerCamelCase				:   Tuple								=   [1_0_0, 8_7, 5_0, 1, 0]
          _lowerCamelCase				:   Dict								=   len(__A     )

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



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   List[str]								=   self.scheduler_classes[0]
          _lowerCamelCase				:   Tuple								=   self.get_scheduler_config()
          _lowerCamelCase				:   Optional[int]								=   scheduler_class(**__A     )

          _lowerCamelCase				:   int								=   [scheduler.config.num_train_timesteps]

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

'''simple docstring'''






import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       int,__A      :       Any=None,**__A      :       Optional[Any]     ):
          warnings.warn(
              "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
              "instead.",__A,)
          super().__init__(args=__A,**__A     )