

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 			a					(		UpperCAmelCase			):
						_lowercase        =     (UniPCMultistepScheduler,)
						_lowercase        =     (("num_inference_steps", 2_5),)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      {
										    "num_train_timesteps": 1000,
										    "beta_start": 0.00_01,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "solver_type": "bh2",
										}

										config.update(**A_		)
										return config


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						str							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      self.dummy_sample
										_UpperCAmelCase    :						Optional[int]							=      0.1 * sample
										_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						str							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)
														# copy over dummy past residuals
														_UpperCAmelCase    :						Union[str, Any]							=      dummy_past_residuals[: scheduler.config.solver_order]

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

														_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      sample, sample
														for t in range(A_  ,   time_step + scheduler.config.solver_order + 1		):
																		_UpperCAmelCase    :						Dict							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
																		_UpperCAmelCase    :						Optional[Any]							=      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     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						Tuple							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						int							=      self.dummy_sample
										_UpperCAmelCase    :						str							=      0.1 * sample
										_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config()
														_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(A_		)
																		_UpperCAmelCase    :						Any							=      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)
																		_UpperCAmelCase    :						Optional[int]							=      dummy_past_residuals[: new_scheduler.config.solver_order]

														_UpperCAmelCase    :						List[Any]							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										if scheduler is None:
														_UpperCAmelCase    :						List[Any]							=      self.scheduler_classes[0]
														_UpperCAmelCase    :						Optional[int]							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						List[Any]							=      scheduler_class(**A_		)

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

										_UpperCAmelCase    :						List[str]							=      10
										_UpperCAmelCase    :						Dict							=      self.dummy_model()
										_UpperCAmelCase    :						Optional[int]							=      self.dummy_sample_deter
										scheduler.set_timesteps(A_		)

										for i, t in enumerate(scheduler.timesteps		):
														_UpperCAmelCase    :						int							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						Dict							=      scheduler.step(A_  ,   A_  ,   A_		).prev_sample

										return sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      dict(self.forward_default_kwargs		)

										_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("num_inference_steps"  ,   A_		)

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						List[str]							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Tuple							=      scheduler_class(**A_		)

														_UpperCAmelCase    :						Optional[int]							=      self.dummy_sample
														_UpperCAmelCase    :						Optional[Any]							=      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"		):
																		_UpperCAmelCase    :						int							=      num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_UpperCAmelCase    :						List[Any]							=      [residual + 0.2, residual + 0.15, residual + 0.10]
														_UpperCAmelCase    :						Optional[int]							=      dummy_past_residuals[: scheduler.config.solver_order]

														_UpperCAmelCase    :						Dict							=      scheduler.timesteps[5]
														_UpperCAmelCase    :						Dict							=      scheduler.timesteps[6]

														_UpperCAmelCase    :						Tuple							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						str							=      scheduler.step(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      UniPCMultistepScheduler(**self.get_scheduler_config()		)
										_UpperCAmelCase    :						Dict							=      self.full_loop(scheduler=A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.24_64		) < 1e-3

										_UpperCAmelCase    :						Tuple							=      DPMSolverSinglestepScheduler.from_config(scheduler.config		)
										_UpperCAmelCase    :						List[str]							=      DEISMultistepScheduler.from_config(scheduler.config		)
										_UpperCAmelCase    :						Dict							=      DPMSolverMultistepScheduler.from_config(scheduler.config		)
										_UpperCAmelCase    :						List[str]							=      UniPCMultistepScheduler.from_config(scheduler.config		)

										_UpperCAmelCase    :						int							=      self.full_loop(scheduler=A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.24_64		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for timesteps in [25, 50, 100, 999, 1000]:
														self.check_over_configs(num_train_timesteps=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.check_over_configs(thresholding=A_		)
										for order in [1, 2, 3]:
														for solver_type in ["bh1", "bh2"]:
																		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_  ,   solver_order=A_  ,   solver_type=A_  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for solver_type in ["bh1", "bh2"]:
														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_  ,   )
																						_UpperCAmelCase    :						str							=      self.full_loop(
																						    solver_order=A_  ,   solver_type=A_  ,   prediction_type=A_  ,   )
																						assert not torch.isnan(A_		).any(), "Samples have nan numbers"


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.check_over_configs(lower_order_final=A_		)
										self.check_over_configs(lower_order_final=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
														self.check_over_forward(num_inference_steps=A_  ,   time_step=0		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.full_loop()
										_UpperCAmelCase    :						Any							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.24_64		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.full_loop(prediction_type="v_prediction"		)
										_UpperCAmelCase    :						List[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_mean.item() - 0.10_14		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						List[Any]							=      self.get_scheduler_config(thresholding=A_  ,   dynamic_thresholding_ratio=0		)
										_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						List[Any]							=      10
										_UpperCAmelCase    :						int							=      self.dummy_model()
										_UpperCAmelCase    :						int							=      self.dummy_sample_deter.half()
										scheduler.set_timesteps(A_		)

										for i, t in enumerate(scheduler.timesteps		):
														_UpperCAmelCase    :						Optional[Any]							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						str							=      scheduler.step(A_  ,   A_  ,   A_		).prev_sample

										assert sample.dtype == torch.floataa





						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Union[str, Any]							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						List[Any]							=      scheduler_class(**A_		)

														scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
														assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps





import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE_            = 'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE_            = direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE_            = transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
SCREAMING_SNAKE_CASE_            = re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


SCREAMING_SNAKE_CASE_            = {
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Dict	)				->    Union[str, Any]:
				_UpperCAmelCase    :						List[Any]							=      None

				# source code of `config_class`
				_UpperCAmelCase    :						int							=      inspect.getsource(lowerCAmelCase	)
				_UpperCAmelCase    :						Tuple							=      _re_checkpoint.findall(lowerCAmelCase	)

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith("/"	):
												_UpperCAmelCase    :						Dict							=      ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								_UpperCAmelCase    :						Any							=      F'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												_UpperCAmelCase    :						Union[str, Any]							=      ckpt_name
												break

				return checkpoint

def 							__SCREAMING_SNAKE_CASE							(			)				->    Dict:
				_UpperCAmelCase    :						Any							=      []

				for config_class in list(CONFIG_MAPPING.values()	):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								_UpperCAmelCase    :						int							=      get_checkpoint_from_config_class(lowerCAmelCase	)

								_UpperCAmelCase    :						Tuple							=      config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase	)

				if len(lowerCAmelCase	) > 0:
								_UpperCAmelCase    :						int							=      "\n".join(sorted(lowerCAmelCase	)	)
								raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'	)


if __name__ == "__main__":
					check_config_docstrings_have_checkpoints()


