



import warnings

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


a_    :  Dict						  =		logging.get_logger(__name__)





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a=None					,      **__a     ):






           '''simple docstring'''



           warnings.warn(
               "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
               "instead."					,      __a					,      )
           super().__init__(args=__a					,      **__a     )




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      (PNDMScheduler,)
     _A      =      (('num_inference_steps', 50),)







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		{
               "num_train_timesteps": 10_00,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**__a     )
           return config







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config(**__a     )
                 lowerCamelCase             =		scheduler_class(**__a     )
                 scheduler.set_timesteps(__a     )
                 # copy over dummy past residuals
                 lowerCamelCase             =		dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a     )
                       lowerCamelCase             =		scheduler_class.from_pretrained(__a     )
                       new_scheduler.set_timesteps(__a     )
                       # copy over dummy past residuals
                       lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )
                 scheduler.set_timesteps(__a     )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase             =		dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a     )
                       lowerCamelCase             =		scheduler_class.from_pretrained(__a     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__a     )

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(**__a     )
           lowerCamelCase             =		scheduler_class(**__a     )

           lowerCamelCase             =		10
           lowerCamelCase             =		self.dummy_model()
           lowerCamelCase             =		self.dummy_sample_deter
           scheduler.set_timesteps(__a     )

           for i, t in enumerate(scheduler.prk_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a     ).prev_sample

           return sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )

           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

                 if num_inference_steps is not None and hasattr(__a					,      "set_timesteps"     ):
                       scheduler.set_timesteps(__a     )
                 elif num_inference_steps is not None and not hasattr(__a					,      "set_timesteps"     ):
                       lowerCamelCase             =		num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                 lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_prk(__a					,      1					,      __a					,      **__a     ).prev_sample

                 self.assertEqual(output_a.shape					,      sample.shape     )
                 self.assertEqual(output_a.shape					,      output_a.shape     )

                 lowerCamelCase             =		scheduler.step_plms(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_plms(__a					,      1					,      __a					,      **__a     ).prev_sample

                 self.assertEqual(output_a.shape					,      sample.shape     )
                 self.assertEqual(output_a.shape					,      output_a.shape     )







     def     _a   (self     ):






           '''simple docstring'''



           for timesteps in [1_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__a     )

           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(steps_offset=1     )
           lowerCamelCase             =		scheduler_class(**__a     )
           scheduler.set_timesteps(10     )
           assert torch.equal(
               scheduler.timesteps					,      torch.LongTensor(
                   [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]     )					,      )







     def     _a   (self     ):






           '''simple docstring'''



           for beta_start, beta_end in zip([0.0001, 0.001]					,      [0.002, 0.02]     ):
                 self.check_over_configs(beta_start=__a					,      beta_end=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__a     )







     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



           for t in [1, 5, 10]:
                 self.check_over_forward(time_step=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for t, num_inference_steps in zip([1, 5, 10]					,      [10, 50, 1_00]     ):
                 self.check_over_forward(num_inference_steps=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		27

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.set_timesteps(__a     )

                 # before power of 3 fix, would error on first step, so we only need to do two
                 for i, t in enumerate(scheduler.prk_timesteps[:2]     ):
                       lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           with self.assertRaises(__a     ):
                 lowerCamelCase             =		self.scheduler_classes[0]
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.step_plms(self.dummy_sample					,      1					,      self.dummy_sample     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop()
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 198.1318     ) < 1E-2
           assert abs(result_mean.item() - 0.2580     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(prediction_type="v_prediction"     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 67.3986     ) < 1E-2
           assert abs(result_mean.item() - 0.0878     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 230.0399     ) < 1E-2
           assert abs(result_mean.item() - 0.2995     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 186.9482     ) < 1E-2
           assert abs(result_mean.item() - 0.2434     ) < 1E-3