



'''simple docstring'''

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 		lowerCamelCase__   (    UpperCAmelCase__  ):




					'''simple docstring'''



					A_         =    (DPMSolverMultistepScheduler,)
					A_         =    (("""num_inference_steps""", 25),)




					def     __UpperCAmelCase  (	self					:						Any  ,	**UpperCamelCase_					:						Optional[Any]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							int  	=		{
						    'num_train_timesteps': 1000,
						    'beta_start': 0.00_01,
						    '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(**UpperCamelCase_       )
						return config




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Dict=0  ,	**UpperCamelCase_					:						Dict       )	->							Tuple:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		dict(self.forward_default_kwargs       )
						_lowercase       :							Any  	=		kwargs.pop('num_inference_steps'  ,	UpperCamelCase_       )
						_lowercase       :							int  	=		self.dummy_sample
						_lowercase       :							Optional[int]  	=		0.1 * sample
						_lowercase       :							Any  	=		[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							_lowercase       :							Optional[int]  	=		self.get_scheduler_config(**UpperCamelCase_       )
							_lowercase       :							List[Any]  	=		scheduler_class(**UpperCamelCase_       )
							scheduler.set_timesteps(UpperCamelCase_       )
							# copy over dummy past residuals
							_lowercase       :							Optional[Any]  	=		dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(UpperCamelCase_       )
								_lowercase       :							List[Any]  	=		scheduler_class.from_pretrained(UpperCamelCase_       )
								new_scheduler.set_timesteps(UpperCamelCase_       )
								# copy over dummy past residuals
								_lowercase       :							Union[str, Any]  	=		dummy_past_residuals[: new_scheduler.config.solver_order]

							_lowercase       :							int  	=		sample, sample
							for t in range(UpperCamelCase_  ,	time_step + scheduler.config.solver_order + 1       ):
								_lowercase       :							Dict  	=		scheduler.step(UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_  ,	**UpperCamelCase_       ).prev_sample
								_lowercase       :							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"




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							List[Any]:



						'''simple docstring'''





						pass




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						str=0  ,	**UpperCamelCase_					:						Union[str, Any]       )	->							Tuple:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		dict(self.forward_default_kwargs       )
						_lowercase       :							Dict  	=		kwargs.pop('num_inference_steps'  ,	UpperCamelCase_       )
						_lowercase       :							str  	=		self.dummy_sample
						_lowercase       :							Union[str, Any]  	=		0.1 * sample
						_lowercase       :							Union[str, Any]  	=		[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							_lowercase       :							List[Any]  	=		self.get_scheduler_config()
							_lowercase       :							List[str]  	=		scheduler_class(**UpperCamelCase_       )
							scheduler.set_timesteps(UpperCamelCase_       )

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

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

							_lowercase       :							Any  	=		scheduler.step(UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_  ,	**UpperCamelCase_       ).prev_sample
							_lowercase       :							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     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Any=None  ,	**UpperCamelCase_					:						Union[str, Any]       )	->							List[str]:



						'''simple docstring'''





						if scheduler is None:
							_lowercase       :							Optional[Any]  	=		self.scheduler_classes[0]
							_lowercase       :							Optional[Any]  	=		self.get_scheduler_config(**UpperCamelCase_       )
							_lowercase       :							Union[str, Any]  	=		scheduler_class(**UpperCamelCase_       )

						_lowercase       :							List[Any]  	=		10
						_lowercase       :							int  	=		self.dummy_model()
						_lowercase       :							int  	=		self.dummy_sample_deter
						scheduler.set_timesteps(UpperCamelCase_       )

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

						return sample




					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							str:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		dict(self.forward_default_kwargs       )

						_lowercase       :							Optional[Any]  	=		kwargs.pop('num_inference_steps'  ,	UpperCamelCase_       )

						for scheduler_class in self.scheduler_classes:
							_lowercase       :							Union[str, Any]  	=		self.get_scheduler_config()
							_lowercase       :							Optional[Any]  	=		scheduler_class(**UpperCamelCase_       )

							_lowercase       :							Dict  	=		self.dummy_sample
							_lowercase       :							Dict  	=		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'       ):
								_lowercase       :							List[Any]  	=		num_inference_steps

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

							_lowercase       :							str  	=		scheduler.timesteps[5]
							_lowercase       :							str  	=		scheduler.timesteps[6]

							_lowercase       :							List[str]  	=		scheduler.step(UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_  ,	**UpperCamelCase_       ).prev_sample
							_lowercase       :							List[Any]  	=		scheduler.step(UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_  ,	**UpperCamelCase_       ).prev_sample

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




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



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Any       )	->							str:



						'''simple docstring'''





						self.check_over_configs(thresholding=UpperCamelCase_       )
						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=UpperCamelCase_  ,	prediction_type=UpperCamelCase_  ,	sample_max_value=UpperCamelCase_  ,	algorithm_type='dpmsolver++'  ,	solver_order=UpperCamelCase_  ,	solver_type=UpperCamelCase_  ,	)




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



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						int       )	->							List[str]:



						'''simple docstring'''





						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=UpperCamelCase_  ,	solver_type=UpperCamelCase_  ,	prediction_type=UpperCamelCase_  ,	algorithm_type=UpperCamelCase_  ,	)
										_lowercase       :							Optional[int]  	=		self.full_loop(
										    solver_order=UpperCamelCase_  ,	solver_type=UpperCamelCase_  ,	prediction_type=UpperCamelCase_  ,	algorithm_type=UpperCamelCase_  ,	)
										assert not torch.isnan(UpperCamelCase_       ).any(), "Samples have nan numbers"




					def     __UpperCAmelCase  (	self					:						List[Any]       )	->							List[Any]:



						'''simple docstring'''





						self.check_over_configs(lower_order_final=UpperCamelCase_       )
						self.check_over_configs(lower_order_final=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							Optional[Any]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							Union[str, Any]:



						'''simple docstring'''





						self.check_over_configs(variance_type=UpperCamelCase_       )
						self.check_over_configs(variance_type='learned_range'       )




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							int:



						'''simple docstring'''





						_lowercase       :							int  	=		self.full_loop()
						_lowercase       :							Optional[int]  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_mean.item() - 0.33_01       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						Dict       )	->							Any:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		self.full_loop(thresholding=UpperCamelCase_  ,	dynamic_thresholding_ratio=0.87  ,	sample_max_value=0.5       )
						_lowercase       :							Any  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_mean.item() - 1.13_64       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						Dict       )	->							Union[str, Any]:



						'''simple docstring'''





						_lowercase       :							Optional[Any]  	=		self.full_loop(prediction_type='v_prediction'       )
						_lowercase       :							int  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_mean.item() - 0.22_51       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						Any       )	->							str:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		self.full_loop(prediction_type='v_prediction'  ,	use_karras_sigmas=UpperCamelCase_       )
						_lowercase       :							Optional[int]  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_mean.item() - 0.20_96       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		DPMSolverMultistepScheduler(**self.get_scheduler_config()       )
						_lowercase       :							Optional[int]  	=		self.full_loop(scheduler=UpperCamelCase_       )
						_lowercase       :							List[Any]  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_mean.item() - 0.33_01       ) < 1E-3

						_lowercase       :							Dict  	=		DPMSolverSinglestepScheduler.from_config(scheduler.config       )
						_lowercase       :							List[str]  	=		UniPCMultistepScheduler.from_config(scheduler.config       )
						_lowercase       :							Union[str, Any]  	=		DEISMultistepScheduler.from_config(scheduler.config       )
						_lowercase       :							List[str]  	=		DPMSolverMultistepScheduler.from_config(scheduler.config       )

						_lowercase       :							Dict  	=		self.full_loop(scheduler=UpperCamelCase_       )
						_lowercase       :							Dict  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_mean.item() - 0.33_01       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						str       )	->							Dict:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		self.scheduler_classes[0]
						_lowercase       :							List[str]  	=		self.get_scheduler_config(thresholding=UpperCamelCase_  ,	dynamic_thresholding_ratio=0       )
						_lowercase       :							str  	=		scheduler_class(**UpperCamelCase_       )

						_lowercase       :							int  	=		10
						_lowercase       :							Optional[int]  	=		self.dummy_model()
						_lowercase       :							List[str]  	=		self.dummy_sample_deter.half()
						scheduler.set_timesteps(UpperCamelCase_       )

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

						assert sample.dtype == torch.floataa




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



						'''simple docstring'''





						for scheduler_class in self.scheduler_classes:
							_lowercase       :							Optional[int]  	=		self.get_scheduler_config(**UpperCamelCase_       )
							_lowercase       :							int  	=		scheduler_class(**UpperCamelCase_       )

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


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case					:							List[Any]           =							logging.getLogger()




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    argparse.ArgumentParser()
			parser.add_argument('-f'							)
			UpperCAmelCase__		:List[Any] 		=    parser.parse_args()
			return args.f




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    logging.StreamHandler(sys.stdout	)
									logger.addHandler(A	)

									UpperCAmelCase__		:List[str] 		=    self.get_auto_remove_tmp_dir()
									UpperCAmelCase__		:List[str] 		=    F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

									with patch.object(A  , 'argv'  , A	):
												UpperCAmelCase__		:Any 		=    run_glue_with_pabee.main()
												for value in result.values():
															self.assertGreaterEqual(A  , 0.75	)





