






"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[Any]								=   (DDPMScheduler,)


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   **a				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Any  			={
                '''num_train_timesteps''': 1_0_0_0,
                '''beta_start''': 0.0_0_0_1,
                '''beta_end''': 0.0_2,
                '''beta_schedule''': '''linear''',
                '''variance_type''': '''fixed_small''',
                '''clip_sample''': True,
            }

            config.update(**a						)
            return config


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,   [0.0_0_2, 0.0_2, 0.2, 2]						):
                  self.check_over_configs(beta_start=a							,   beta_end=a						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            for variance in ["fixed_small", "fixed_large", "other"]:
                  self.check_over_configs(variance_type=a						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=a						)


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            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 _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            for t in [0, 5_0_0, 9_9_9]:
                  self.check_over_forward(time_step=a						)


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=self.scheduler_classes[0]
            __snake_case      :    Optional[Any]  			=self.get_scheduler_config()
            __snake_case      :    Optional[int]  			=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						) - 0.0_0_9_7_9						)						) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.0_2						)						) < 1e-5


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.scheduler_classes[0]
            __snake_case      :    str  			=self.get_scheduler_config()
            __snake_case      :    Optional[Any]  			=scheduler_class(**a						)

            __snake_case      :    Dict  			=len(a						)

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

            for t in reversed(range(a						)						):
                  # 1. predict noise residual
                  __snake_case      :    Any  			=model(a							,   a						)

                  # 2. predict previous mean of sample x_t-1
                  __snake_case      :    Optional[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
                  __snake_case      :    str  			=pred_prev_sample

            __snake_case      :    Dict  			=torch.sum(torch.abs(a						)						)
            __snake_case      :    List[Any]  			=torch.mean(torch.abs(a						)						)

            assert abs(result_sum.item() - 2_5_8.9_6_0_6						) < 1e-2
            assert abs(result_mean.item() - 0.3_3_7_2						) < 1e-3


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    List[str]  			=self.scheduler_classes[0]
            __snake_case      :    Dict  			=self.get_scheduler_config(prediction_type='''v_prediction'''						)
            __snake_case      :    Optional[Any]  			=scheduler_class(**a						)

            __snake_case      :    Any  			=len(a						)

            __snake_case      :    Tuple  			=self.dummy_model()
            __snake_case      :    List[str]  			=self.dummy_sample_deter
            __snake_case      :    int  			=torch.manual_seed(0						)

            for t in reversed(range(a						)						):
                  # 1. predict noise residual
                  __snake_case      :    List[str]  			=model(a							,   a						)

                  # 2. predict previous mean of sample x_t-1
                  __snake_case      :    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
                  __snake_case      :    Dict  			=pred_prev_sample

            __snake_case      :    Optional[Any]  			=torch.sum(torch.abs(a						)						)
            __snake_case      :    List[str]  			=torch.mean(torch.abs(a						)						)

            assert abs(result_sum.item() - 2_0_2.0_2_9_6						) < 1e-2
            assert abs(result_mean.item() - 0.2_6_3_1						) < 1e-3


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    int  			=self.scheduler_classes[0]
            __snake_case      :    int  			=self.get_scheduler_config()
            __snake_case      :    Optional[int]  			=scheduler_class(**a						)

            __snake_case      :    Tuple  			=[1_0_0, 8_7, 5_0, 1, 0]

            scheduler.set_timesteps(timesteps=a						)

            __snake_case      :    Any  			=scheduler.timesteps

            for i, timestep in enumerate(a						):
                  if i == len(a						) - 1:
                        __snake_case      :    List[str]  			=-1
                  else:
                        __snake_case      :    Optional[Any]  			=timesteps[i + 1]

                  __snake_case      :    Optional[Any]  			=scheduler.previous_timestep(a						)
                  __snake_case      :    Optional[Any]  			=prev_t.item()

                  self.assertEqual(a							,   a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=self.scheduler_classes[0]
            __snake_case      :    Optional[int]  			=self.get_scheduler_config()
            __snake_case      :    str  			=scheduler_class(**a						)

            __snake_case      :    List[str]  			=[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 _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Any  			=self.scheduler_classes[0]
            __snake_case      :    Tuple  			=self.get_scheduler_config()
            __snake_case      :    Any  			=scheduler_class(**a						)

            __snake_case      :    int  			=[1_0_0, 8_7, 5_0, 1, 0]
            __snake_case      :    Any  			=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 _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=self.scheduler_classes[0]
            __snake_case      :    Dict  			=self.get_scheduler_config()
            __snake_case      :    Any  			=scheduler_class(**a						)

            __snake_case      :    Tuple  			=[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"""





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCamelCase_	:				int										= subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
UpperCamelCase_	:				Optional[int]										= (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

UpperCamelCase_	:				List[Any]										= """|""".join(sys.argv[1:])
UpperCamelCase_	:				List[str]										= re.compile(rF'''^({joined_dirs}).*?\.py$''')

UpperCamelCase_	:				str										= [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


