



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Dict =       (IPNDMScheduler,)
							A				:int =       (("num_inference_steps", 50),)



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							str      						= {"num_train_timesteps": 1000}
											config.update(**__UpperCAmelCase				)
											return config



							def 		_A	(							self ,					__UpperCAmelCase=0 ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[Any]      						= dict(self.forward_default_kwargs				)
											a__	:							Any      						= kwargs.pop("num_inference_steps" ,					__UpperCAmelCase				)
											a__	:							str      						= self.dummy_sample
											a__	:							Optional[int]      						= 0.1 * sample
											a__	:							Tuple      						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

											for scheduler_class in self.scheduler_classes:
															a__	:							List[str]      						= self.get_scheduler_config(**__UpperCAmelCase				)
															a__	:							Union[str, Any]      						= scheduler_class(**__UpperCAmelCase				)
															scheduler.set_timesteps(__UpperCAmelCase				)
															# copy over dummy past residuals
															a__	:							Optional[Any]      						= dummy_past_residuals[:]

															if time_step is None:
																			a__	:							Any      						= scheduler.timesteps[len(scheduler.timesteps				) // 2]

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(__UpperCAmelCase				)
																			a__	:							Optional[int]      						= scheduler_class.from_pretrained(__UpperCAmelCase				)
																			new_scheduler.set_timesteps(__UpperCAmelCase				)
																			# copy over dummy past residuals
																			a__	:							Optional[Any]      						= dummy_past_residuals[:]

															a__	:							List[Any]      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample
															a__	:							Union[str, Any]      						= new_scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample

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

															a__	:							List[Any]      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample
															a__	:							Dict      						= new_scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).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 ,					__UpperCAmelCase=0 ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[Any]      						= dict(self.forward_default_kwargs				)
											a__	:							str      						= kwargs.pop("num_inference_steps" ,					__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= self.dummy_sample
											a__	:							str      						= 0.1 * sample
											a__	:							Any      						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

											for scheduler_class in self.scheduler_classes:
															a__	:							Optional[int]      						= self.get_scheduler_config()
															a__	:							Dict      						= scheduler_class(**__UpperCAmelCase				)
															scheduler.set_timesteps(__UpperCAmelCase				)

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

															if time_step is None:
																			a__	:							List[Any]      						= scheduler.timesteps[len(scheduler.timesteps				) // 2]

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

																			# copy over dummy past residual (must be after setting timesteps)
																			a__	:							Union[str, Any]      						= dummy_past_residuals[:]

															a__	:							int      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample
															a__	:							Optional[Any]      						= new_scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample

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

															a__	:							int      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample
															a__	:							Any      						= new_scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample

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



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.scheduler_classes[0]
											a__	:							Union[str, Any]      						= self.get_scheduler_config(**__UpperCAmelCase				)
											a__	:							str      						= scheduler_class(**__UpperCAmelCase				)

											a__	:							Dict      						= 10
											a__	:							str      						= self.dummy_model()
											a__	:							Dict      						= self.dummy_sample_deter
											scheduler.set_timesteps(__UpperCAmelCase				)

											for i, t in enumerate(scheduler.timesteps				):
															a__	:							Dict      						= model(__UpperCAmelCase ,					__UpperCAmelCase				)
															a__	:							Optional[int]      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				).prev_sample

											for i, t in enumerate(scheduler.timesteps				):
															a__	:							Dict      						= model(__UpperCAmelCase ,					__UpperCAmelCase				)
															a__	:							Tuple      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				).prev_sample

											return sample



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= dict(self.forward_default_kwargs				)

											a__	:							List[str]      						= kwargs.pop("num_inference_steps" ,					__UpperCAmelCase				)

											for scheduler_class in self.scheduler_classes:
															a__	:							Optional[Any]      						= self.get_scheduler_config()
															a__	:							Any      						= scheduler_class(**__UpperCAmelCase				)

															a__	:							Union[str, Any]      						= self.dummy_sample
															a__	:							List[Any]      						= 0.1 * sample

															if num_inference_steps is not None and hasattr(__UpperCAmelCase ,					"set_timesteps"				):
																			scheduler.set_timesteps(__UpperCAmelCase				)
															elif num_inference_steps is not None and not hasattr(__UpperCAmelCase ,					"set_timesteps"				):
																			a__	:							Optional[int]      						= num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															a__	:							Optional[int]      						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
															a__	:							Dict      						= dummy_past_residuals[:]

															a__	:							int      						= scheduler.timesteps[5]
															a__	:							Union[str, Any]      						= scheduler.timesteps[6]

															a__	:							List[Any]      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample
															a__	:							Dict      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample

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

															a__	:							Optional[Any]      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).prev_sample
															a__	:							Tuple      						= scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase				).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 [100, 1000]:
															self.check_over_configs(num_train_timesteps=__UpperCAmelCase ,					time_step=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											for t, num_inference_steps in zip([1, 5, 10] ,					[10, 50, 100]				):
															self.check_over_forward(num_inference_steps=__UpperCAmelCase ,					time_step=__UpperCAmelCase				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.full_loop()
											a__	:							Dict      						= torch.mean(torch.abs(__UpperCAmelCase				)				)

											assert abs(result_mean.item() - 254_0529				) < 10







# 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


lowerCamelCase		       =      subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
lowerCamelCase		       =      subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()

lowerCamelCase		       =      """|""".join(sys.argv[1:])
lowerCamelCase		       =      re.compile(RF'^({joined_dirs}).*?\.py$')

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


