


'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Optional[Any]   =					(IPNDMScheduler,)
					lowerCAmelCase      :int   =					(('''num_inference_steps''', 50),)




					def 				snake_case__						(      self							,							**_lowerCamelCase):
								UpperCAmelCase__  :					int        =     {"""num_train_timesteps""": 1000}
								config.update(**_lowerCamelCase)
								return config




					def 				snake_case__						(      self							,							_lowerCamelCase=0							,							**_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     dict(self.forward_default_kwargs)
								UpperCAmelCase__  :					Any        =     kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     self.dummy_sample
								UpperCAmelCase__  :					Any        =     0.1 * sample
								UpperCAmelCase__  :					Tuple        =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
											UpperCAmelCase__  :					Tuple        =     self.get_scheduler_config(**_lowerCamelCase)
											UpperCAmelCase__  :					List[str]        =     scheduler_class(**_lowerCamelCase)
											scheduler.set_timesteps(_lowerCamelCase)
											# copy over dummy past residuals
											UpperCAmelCase__  :					Optional[Any]        =     dummy_past_residuals[:]

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

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

											UpperCAmelCase__  :					List[str]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					Any        =     new_scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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

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

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




					def 				snake_case__						(      self):
								pass




					def 				snake_case__						(      self							,							_lowerCamelCase=0							,							**_lowerCamelCase):
								UpperCAmelCase__  :					str        =     dict(self.forward_default_kwargs)
								UpperCAmelCase__  :					Union[str, Any]        =     kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     self.dummy_sample
								UpperCAmelCase__  :					Tuple        =     0.1 * sample
								UpperCAmelCase__  :					List[Any]        =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
											UpperCAmelCase__  :					Union[str, Any]        =     self.get_scheduler_config()
											UpperCAmelCase__  :					str        =     scheduler_class(**_lowerCamelCase)
											scheduler.set_timesteps(_lowerCamelCase)

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

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

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

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

											UpperCAmelCase__  :					Optional[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					Any        =     new_scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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

											UpperCAmelCase__  :					List[str]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					str        =     new_scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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




					def 				snake_case__						(      self							,							**_lowerCamelCase):
								UpperCAmelCase__  :					str        =     self.scheduler_classes[0]
								UpperCAmelCase__  :					Dict        =     self.get_scheduler_config(**_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__  :					Tuple        =     10
								UpperCAmelCase__  :					List[str]        =     self.dummy_model()
								UpperCAmelCase__  :					Union[str, Any]        =     self.dummy_sample_deter
								scheduler.set_timesteps(_lowerCamelCase)

								for i, t in enumerate(scheduler.timesteps):
											UpperCAmelCase__  :					Union[str, Any]        =     model(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					Any        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample

								for i, t in enumerate(scheduler.timesteps):
											UpperCAmelCase__  :					Tuple        =     model(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					Optional[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample

								return sample




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     dict(self.forward_default_kwargs)

								UpperCAmelCase__  :					List[Any]        =     kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)

								for scheduler_class in self.scheduler_classes:
											UpperCAmelCase__  :					List[str]        =     self.get_scheduler_config()
											UpperCAmelCase__  :					Any        =     scheduler_class(**_lowerCamelCase)

											UpperCAmelCase__  :					str        =     self.dummy_sample
											UpperCAmelCase__  :					List[str]        =     0.1 * sample

											if num_inference_steps is not None and hasattr(_lowerCamelCase							,							"""set_timesteps"""):
														scheduler.set_timesteps(_lowerCamelCase)
											elif num_inference_steps is not None and not hasattr(_lowerCamelCase							,							"""set_timesteps"""):
														UpperCAmelCase__  :					List[str]        =     num_inference_steps

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

											UpperCAmelCase__  :					List[Any]        =     scheduler.timesteps[5]
											UpperCAmelCase__  :					str        =     scheduler.timesteps[6]

											UpperCAmelCase__  :					Optional[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					Union[str, Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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

											UpperCAmelCase__  :					List[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__  :					List[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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




					def 				snake_case__						(      self):
								for timesteps in [100, 1000]:
											self.check_over_configs(num_train_timesteps=_lowerCamelCase							,							time_step=_lowerCamelCase)




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




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     self.full_loop()
								UpperCAmelCase__  :					List[Any]        =     torch.mean(torch.abs(_lowerCamelCase))

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



'''simple docstring'''



import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__A	=object()

# For specifying empty leaf dict `{}`
__A	=object()







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     tuple((re.compile(x + """$"""					) for x in qs)					)
			for i in range(len(UpperCamelCase__					) - len(UpperCamelCase__					) + 1					):
						UpperCAmelCase__  :					Optional[Any]        =     [x.match(UpperCamelCase__					) for x, y in zip(UpperCamelCase__ ,      ks[i:]					)]
						if matches and all(UpperCamelCase__					):
									return True
			return False







def     _UpperCamelCase      (		UpperCamelCase__					):
			def replace(UpperCamelCase__ ,      UpperCamelCase__					):
						for rule, replacement in rules:
									if _match(UpperCamelCase__ ,      UpperCamelCase__					):
												return replacement
						return val

			return replace







def     _UpperCamelCase      (		):
			return [
			    # embeddings
			    (("transformer", "wpe", "embedding"), P("""mp""" ,      UpperCamelCase__					)),
			    (("transformer", "wte", "embedding"), P("""mp""" ,      UpperCamelCase__					)),
			    # atention
			    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__ ,      """mp"""					)),
			    (("attention", "out_proj", "kernel"), P("""mp""" ,      UpperCamelCase__					)),
			    (("attention", "out_proj", "bias"), None),
			    # mlp
			    (("mlp", "c_fc", "kernel"), P(UpperCamelCase__ ,      """mp"""					)),
			    (("mlp", "c_fc", "bias"), P("""mp"""					)),
			    (("mlp", "c_proj", "kernel"), P("""mp""" ,      UpperCamelCase__					)),
			    (("mlp", "c_proj", "bias"), None),
			    # layer norms
			    ((r"ln_\d+", "bias"), None),
			    ((r"\d+", r"ln_\d+", "scale"), None),
			    (("ln_f", "bias"), None),
			    (("ln_f", "scale"), None),
			]







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =     _get_partition_rules()
			UpperCAmelCase__  :					Optional[int]        =     _replacement_rules(UpperCamelCase__					)
			UpperCAmelCase__  :					List[Any]        =     {k: _unmatched for k in flatten_dict(UpperCamelCase__					)}
			UpperCAmelCase__  :					Tuple        =     {k: replace(UpperCamelCase__ ,      UpperCamelCase__					) for k, v in initd.items()}
			assert _unmatched not in result.values(), "Incomplete partition spec."
			return freeze(unflatten_dict(UpperCamelCase__					)					)