




'''simple docstring'''





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      int						       =    (DPMSolverSinglestepScheduler,)
			lowerCAmelCase_     :      Optional[int]						       =    (("num_inference_steps", 25),)




			def    lowerCAmelCase__				(					self				,							**a__							)				->				int:

										'''simple docstring'''




										snake_case_						=      {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "prediction_type": "epsilon",
										    "thresholding": False,
										    "sample_max_value": 1.0,
										    "algorithm_type": "dpmsolver++",
										    "solver_type": "midpoint",
										    "lambda_min_clipped": -float("inf"							),
										    "variance_type": None,
										}

										config.update(**a__							)
										return config




			def    lowerCAmelCase__				(					self				,							a__=0				,							**a__							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      dict(self.forward_default_kwargs							)
										snake_case_						=      kwargs.pop("num_inference_steps"				,							a__							)
										snake_case_						=      self.dummy_sample
										snake_case_						=      0.1 * sample
										snake_case_						=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_						=      self.get_scheduler_config(**a__							)
																	snake_case_						=      scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)
																	# copy over dummy past residuals
																	snake_case_						=      dummy_past_residuals[: scheduler.config.solver_order]

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

																	snake_case_					,      snake_case_						=      sample, sample
																	for t in range(a__				,							time_step + scheduler.config.solver_order + 1							):
																								snake_case_						=      scheduler.step(a__				,							a__				,							a__				,							**a__							).prev_sample
																								snake_case_						=      new_scheduler.step(a__				,							a__				,							a__				,							**a__							).prev_sample

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




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										pass




			def    lowerCAmelCase__				(					self				,							a__=0				,							**a__							)				->				Any:

										'''simple docstring'''




										snake_case_						=      dict(self.forward_default_kwargs							)
										snake_case_						=      kwargs.pop("num_inference_steps"				,							a__							)
										snake_case_						=      self.dummy_sample
										snake_case_						=      0.1 * sample
										snake_case_						=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_						=      self.get_scheduler_config()
																	snake_case_						=      scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)

																	# copy over dummy past residuals (must be after setting timesteps)
																	snake_case_						=      dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(a__							)
																								snake_case_						=      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)
																								snake_case_						=      dummy_past_residuals[: new_scheduler.config.solver_order]

																	snake_case_						=      scheduler.step(a__				,							a__				,							a__				,							**a__							).prev_sample
																	snake_case_						=      new_scheduler.step(a__				,							a__				,							a__				,							**a__							).prev_sample

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




			def    lowerCAmelCase__				(					self				,							a__=None				,							**a__							)				->				List[str]:

										'''simple docstring'''




										if scheduler is None:
																	snake_case_						=      self.scheduler_classes[0]
																	snake_case_						=      self.get_scheduler_config(**a__							)
																	snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config(**a__							)
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      10
										snake_case_						=      self.dummy_model()
										snake_case_						=      self.dummy_sample_deter
										scheduler.set_timesteps(a__							)

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

										return sample




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
										snake_case_						=      50
										snake_case_						=      self.dummy_model()
										snake_case_						=      self.dummy_sample_deter
										scheduler.set_timesteps(a__							)

										# make sure that the first t is uneven
										for i, t in enumerate(scheduler.timesteps[3:]							):
																	snake_case_						=      model(a__				,							a__							)
																	snake_case_						=      scheduler.step(a__				,							a__				,							a__							).prev_sample

										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_5_7_4							) < 1e-3




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=a__							)




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
										snake_case_						=      self.full_loop(scheduler=a__							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_7_9_1							) < 1e-3

										snake_case_						=      DEISMultistepScheduler.from_config(scheduler.config							)
										snake_case_						=      DPMSolverMultistepScheduler.from_config(scheduler.config							)
										snake_case_						=      UniPCMultistepScheduler.from_config(scheduler.config							)
										snake_case_						=      DPMSolverSinglestepScheduler.from_config(scheduler.config							)

										snake_case_						=      self.full_loop(scheduler=a__							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_7_9_1							) < 1e-3




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										self.check_over_configs(thresholding=a__							)
										for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																								for threshold in [0.5, 1.0, 2.0]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    thresholding=a__				,							prediction_type=a__				,							sample_max_value=a__				,							algorithm_type="dpmsolver++"				,							solver_order=a__				,							solver_type=a__				,							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																								for order in [1, 2, 3]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    solver_order=a__				,							solver_type=a__				,							prediction_type=a__				,							algorithm_type=a__				,							)
																																						snake_case_						=      self.full_loop(
																																						    solver_order=a__				,							solver_type=a__				,							prediction_type=a__				,							algorithm_type=a__				,							)
																																						assert not torch.isnan(a__							).any(), "Samples have nan numbers"




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										self.check_over_configs(lower_order_final=a__							)
										self.check_over_configs(lower_order_final=a__							)




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										self.check_over_configs(lambda_min_clipped=-float("inf"							)							)
										self.check_over_configs(lambda_min_clipped=-5.1							)




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										self.check_over_configs(variance_type=a__							)
										self.check_over_configs(variance_type="learned_range"							)




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=a__				,							time_step=0							)




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      self.full_loop()
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_7_9_1							) < 1e-3




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.full_loop(use_karras_sigmas=a__							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_2_4_8							) < 1e-3




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      self.full_loop(prediction_type="v_prediction"							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.1_4_5_3							) < 1e-3




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      self.full_loop(prediction_type="v_prediction"				,							use_karras_sigmas=a__							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.0_6_4_9							) < 1e-3




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config(thresholding=a__				,							dynamic_thresholding_ratio=0							)
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      10
										snake_case_						=      self.dummy_model()
										snake_case_						=      self.dummy_sample_deter.half()
										scheduler.set_timesteps(a__							)

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

										assert sample.dtype == torch.floataa










import argparse
from collections import defaultdict

import yaml


_A     =			'''docs/source/en/_toctree.yml'''





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  defaultdict(_A  )
						for doc in model_doc:
												counts[doc["local"]] += 1
						lowerCAmelCase_      			=  [key for key, value in counts.items() if value > 1]

						lowerCAmelCase_      			=  []
						for duplicate_key in duplicates:
												lowerCAmelCase_      			=  list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}  )
												if len(_A  ) > 1:
																		raise ValueError(
																		    f"{duplicate_key} is present several times in the documentation table of content at "
																		    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																		    '''others.'''  )
												# Only add this once
												new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}  )

						# Add none duplicate-keys
						new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]  )

						# Sort
						return sorted(_A ,  key=lambda _A  : s["title"].lower()  )





def __UpperCamelCase			(       _A=False  ):
						with open(_A ,  encoding='''utf-8'''  ) as f:
												lowerCAmelCase_      			=  yaml.safe_load(f.read()  )

						# Get to the API doc
						lowerCAmelCase_      			=  0
						while content[api_idx]["title"] != "API":
												api_idx += 1
						lowerCAmelCase_      			=  content[api_idx]['''sections''']

						# Then to the model doc
						lowerCAmelCase_      			=  0
						while api_doc[model_idx]["title"] != "Models":
												model_idx += 1

						lowerCAmelCase_      			=  api_doc[model_idx]['''sections''']

						lowerCAmelCase_      			=  [(idx, section) for idx, section in enumerate(_A  ) if '''sections''' in section]
						lowerCAmelCase_      			=  False
						for idx, modality_doc in modalities_docs:
												lowerCAmelCase_      			=  modality_doc['''sections''']
												lowerCAmelCase_      			=  clean_model_doc_toc(_A  )

												if old_modality_doc != new_modality_doc:
																		lowerCAmelCase_      			=  True
																		if overwrite:
																								lowerCAmelCase_      			=  new_modality_doc

						if diff:
												if overwrite:
																		lowerCAmelCase_      			=  model_doc
																		lowerCAmelCase_      			=  api_doc
																		with open(_A ,  '''w''' ,  encoding='''utf-8'''  ) as f:
																								f.write(yaml.dump(_A ,  allow_unicode=_A  )  )
												else:
																		raise ValueError(
																		    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''  )


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						_A     =			parser.parse_args()

						check_model_doc(args.fix_and_overwrite)




