




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class        snake_case__				(lowercase__  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   (DPMSolverMultistepScheduler,)
				SCREAMING_SNAKE_CASE_			:				Tuple									=   (("""num_inference_steps""", 25),)





				def 				__UpperCAmelCase				( self							:	str   , **__lowerCamelCase							:	Optional[int]				)   ->	int:
							a       =     {
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """solver_order""": 2,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							    """sample_max_value""": 1.0,
							    """algorithm_type""": """dpmsolver++""",
							    """solver_type""": """midpoint""",
							    """lower_order_final""": False,
							    """lambda_min_clipped""": -float("inf"				),
							    """variance_type""": None,
							}

							config.update(**SCREAMING_SNAKE_CASE__				)
							return config





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	Union[str, Any]=0   , **__lowerCamelCase							:	str				)   ->	int:
							a       =     dict(self.forward_default_kwargs				)
							a       =     kwargs.pop("num_inference_steps"   , SCREAMING_SNAKE_CASE__				)
							a       =     self.dummy_sample
							a       =     0.1 * sample
							a       =     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							pass





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Dict=0   , **__lowerCamelCase							:	str				)   ->	Any:
							a       =     dict(self.forward_default_kwargs				)
							a       =     kwargs.pop("num_inference_steps"   , SCREAMING_SNAKE_CASE__				)
							a       =     self.dummy_sample
							a       =     0.1 * sample
							a       =     [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
										a       =     self.get_scheduler_config()
										a       =     scheduler_class(**SCREAMING_SNAKE_CASE__				)
										scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				)

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

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

													# copy over dummy past residual (must be after setting timesteps)
													a       =     dummy_past_residuals[: new_scheduler.config.solver_order]

										a       =     scheduler.step(SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , **SCREAMING_SNAKE_CASE__				).prev_sample
										a       =     new_scheduler.step(SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , **SCREAMING_SNAKE_CASE__				).prev_sample

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





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Dict=None   , **__lowerCamelCase							:	int				)   ->	str:

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

							a       =     10
							a       =     self.dummy_model()
							a       =     self.dummy_sample_deter
							scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				)

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

							return sample





				def 				__UpperCAmelCase				( self							:	int				)   ->	List[Any]:
							a       =     dict(self.forward_default_kwargs				)

							a       =     kwargs.pop("num_inference_steps"   , SCREAMING_SNAKE_CASE__				)

							for scheduler_class in self.scheduler_classes:
										a       =     self.get_scheduler_config()
										a       =     scheduler_class(**SCREAMING_SNAKE_CASE__				)

										a       =     self.dummy_sample
										a       =     0.1 * sample

										if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__   , "set_timesteps"				):
													scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				)
										elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__   , "set_timesteps"				):
													a       =     num_inference_steps

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

										a       =     scheduler.timesteps[5]
										a       =     scheduler.timesteps[6]

										a       =     scheduler.step(SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , **SCREAMING_SNAKE_CASE__				).prev_sample
										a       =     scheduler.step(SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__   , **SCREAMING_SNAKE_CASE__				).prev_sample

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





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[Any]:

							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
										self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	List[Any]:
							self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__				)
							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=SCREAMING_SNAKE_CASE__   , prediction_type=SCREAMING_SNAKE_CASE__   , sample_max_value=SCREAMING_SNAKE_CASE__   , algorithm_type="dpmsolver++"   , solver_order=SCREAMING_SNAKE_CASE__   , solver_type=SCREAMING_SNAKE_CASE__   , )





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Optional[Any]:

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





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	str:

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





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Any:
							self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__				)
							self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[str]:
							self.check_over_configs(lambda_min_clipped=-float("inf"				)				)
							self.check_over_configs(lambda_min_clipped=-5.1				)





				def 				__UpperCAmelCase				( self							:	int				)   ->	Optional[int]:
							self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__				)
							self.check_over_configs(variance_type="learned_range"				)





				def 				__UpperCAmelCase				( self							:	str				)   ->	str:

							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
										self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__   , time_step=0				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Optional[Any]:
							a       =     self.full_loop()
							a       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

							assert abs(result_mean.item() - 0.3_301				) < 1e-3





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Optional[int]:
							a       =     self.full_loop(thresholding=SCREAMING_SNAKE_CASE__   , dynamic_thresholding_ratio=0.87   , sample_max_value=0.5				)
							a       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

							assert abs(result_mean.item() - 1.1_364				) < 1e-3





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[Any]:
							a       =     self.full_loop(prediction_type="v_prediction"				)
							a       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

							assert abs(result_mean.item() - 0.2_251				) < 1e-3





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	List[str]:
							a       =     self.full_loop(prediction_type="v_prediction"   , use_karras_sigmas=SCREAMING_SNAKE_CASE__				)
							a       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

							assert abs(result_mean.item() - 0.2_096				) < 1e-3





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Union[str, Any]:
							a       =     DPMSolverMultistepScheduler(**self.get_scheduler_config()				)
							a       =     self.full_loop(scheduler=SCREAMING_SNAKE_CASE__				)
							a       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

							assert abs(result_mean.item() - 0.3_301				) < 1e-3

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

							a       =     self.full_loop(scheduler=SCREAMING_SNAKE_CASE__				)
							a       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

							assert abs(result_mean.item() - 0.3_301				) < 1e-3





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							a       =     self.scheduler_classes[0]
							a       =     self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__   , dynamic_thresholding_ratio=0				)
							a       =     scheduler_class(**SCREAMING_SNAKE_CASE__				)

							a       =     10
							a       =     self.dummy_model()
							a       =     self.dummy_sample_deter.half()
							scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				)

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

							assert sample.dtype == torch.floataa





				def 				__UpperCAmelCase				( self							:	Optional[int]   , **__lowerCamelCase							:	Dict				)   ->	str:

							for scheduler_class in self.scheduler_classes:
										a       =     self.get_scheduler_config(**SCREAMING_SNAKE_CASE__				)
										a       =     scheduler_class(**SCREAMING_SNAKE_CASE__				)

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











# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List
def   UpperCAmelCase							(		A__				,  A__     )							->					Any:
	if name != "training_function" and name != "main":
		raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''     )
	_snake_case     , _snake_case					:							Union[str, Any]					     =   [], False
	for line in lines:
		if not found_start and f'''def {name}''' in line:
			_snake_case					:							Tuple					     =   True
			good_lines.append(A__     )
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(A__     )
def   UpperCAmelCase							(		A__     )							->					Dict:
	return [line for line in lines if not line.lstrip().startswith("""#"""     ) and line != "\n"]


def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__ = None     )							->					str:
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Tuple					     =   f.readlines()
	with open(os.path.abspath(os.path.join("""examples"""				,  """nlp_example.py"""     )     )				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	if secondary_filename is not None:
		with open(A__				,  """r"""     ) as f:
			_snake_case					:							List[str]					     =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		_snake_case					:							Dict					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							int					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		if secondary_filename is not None:
			_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
	else:
		_snake_case					:							str					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							List[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							Union[str, Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		if secondary_filename is not None:
			_snake_case					:							List[Any]					     =   clean_lines(
			    get_function_contents_by_name(A__				,  """training_function"""     )     )

	_snake_case					:							str					     =   """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

	# Specific code in our script that differs from the full version, aka what is new
	_snake_case					:							Dict					     =   []
	_snake_case					:							str					     =   []  # We keep track of the idxs just in case it's a repeated statement
	_snake_case					:							Optional[Any]					     =   iter(A__     )
	for i in range(len(A__     ) - 1     ):
		if i not in passed_idxs:
			_snake_case					:							Any					     =   next(A__     )
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(A__     )
					passed_idxs.append(A__     )
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					_snake_case					:							Optional[int]					     =   next(A__     )

    # Extract out just the new parts from the full_file_training_func
	_snake_case					:							Any					     =   []
	_snake_case					:							Tuple					     =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(A__     ):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(A__     )
					passed_idxs.append(A__     )

    # Finally, get the overall diff
	_snake_case					:							Optional[int]					     =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		_snake_case					:							Optional[int]					     =   [line for line in full_file_contents if line not in secondary_file_func]
		_snake_case					:							int					     =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example





