



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							a_  (		_snake_case				):
						UpperCamelCase__      :						int				       =(LMSDiscreteScheduler,)
						UpperCamelCase__      :						Optional[int]				       =10

						def 	__a       (    self	:List[str]   ,						**_lowercase	:Union[str, Any])		->					Union[str, Any]:
												UpperCAmelCase_     							=     {
												    '''num_train_timesteps''': 1100,
												    '''beta_start''': 0.0_001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												}

												config.update(**_lowercase)
												return config

						def 	__a       (    self	:Any)		->					Union[str, Any]:
												for timesteps in [10, 50, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=_lowercase)

						def 	__a       (    self	:List[Any])		->					Optional[int]:
												for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]   ,						[0.0_002, 0.002, 0.02]):
																		self.check_over_configs(beta_start=_lowercase   ,						beta_end=_lowercase)

						def 	__a       (    self	:Optional[Any])		->					str:
												for schedule in ["linear", "scaled_linear"]:
																		self.check_over_configs(beta_schedule=_lowercase)

						def 	__a       (    self	:List[str])		->					Dict:
												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=_lowercase)

						def 	__a       (    self	:List[Any])		->					str:
												for t in [0, 500, 800]:
																		self.check_over_forward(time_step=_lowercase)

						def 	__a       (    self	:Tuple)		->					List[Any]:
												UpperCAmelCase_     							=     self.scheduler_classes[0]
												UpperCAmelCase_     							=     self.get_scheduler_config()
												UpperCAmelCase_     							=     scheduler_class(**_lowercase)

												scheduler.set_timesteps(self.num_inference_steps)

												UpperCAmelCase_     							=     self.dummy_model()
												UpperCAmelCase_     							=     self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(scheduler.timesteps):
																		UpperCAmelCase_     							=     scheduler.scale_model_input(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     model(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     scheduler.step(_lowercase   ,						_lowercase   ,						_lowercase)
																		UpperCAmelCase_     							=     output.prev_sample

												UpperCAmelCase_     							=     torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_     							=     torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 1_006.388) < 1E-2
												assert abs(result_mean.item() - 1.31) < 1E-3

						def 	__a       (    self	:Optional[Any])		->					List[Any]:
												UpperCAmelCase_     							=     self.scheduler_classes[0]
												UpperCAmelCase_     							=     self.get_scheduler_config(prediction_type='''v_prediction''')
												UpperCAmelCase_     							=     scheduler_class(**_lowercase)

												scheduler.set_timesteps(self.num_inference_steps)

												UpperCAmelCase_     							=     self.dummy_model()
												UpperCAmelCase_     							=     self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(scheduler.timesteps):
																		UpperCAmelCase_     							=     scheduler.scale_model_input(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     model(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     scheduler.step(_lowercase   ,						_lowercase   ,						_lowercase)
																		UpperCAmelCase_     							=     output.prev_sample

												UpperCAmelCase_     							=     torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_     							=     torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 0.0_017) < 1E-2
												assert abs(result_mean.item() - 2.2_6_7_6E-0_6) < 1E-3

						def 	__a       (    self	:str)		->					List[str]:
												UpperCAmelCase_     							=     self.scheduler_classes[0]
												UpperCAmelCase_     							=     self.get_scheduler_config()
												UpperCAmelCase_     							=     scheduler_class(**_lowercase)

												scheduler.set_timesteps(self.num_inference_steps   ,						device=_lowercase)

												UpperCAmelCase_     							=     self.dummy_model()
												UpperCAmelCase_     							=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												UpperCAmelCase_     							=     sample.to(_lowercase)

												for i, t in enumerate(scheduler.timesteps):
																		UpperCAmelCase_     							=     scheduler.scale_model_input(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     model(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     scheduler.step(_lowercase   ,						_lowercase   ,						_lowercase)
																		UpperCAmelCase_     							=     output.prev_sample

												UpperCAmelCase_     							=     torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_     							=     torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 1_006.388) < 1E-2
												assert abs(result_mean.item() - 1.31) < 1E-3




						def 	__a       (    self	:Optional[int])		->					Dict:
												UpperCAmelCase_     							=     self.scheduler_classes[0]
												UpperCAmelCase_     							=     self.get_scheduler_config()
												UpperCAmelCase_     							=     scheduler_class(**_lowercase   ,						use_karras_sigmas=_lowercase)

												scheduler.set_timesteps(self.num_inference_steps   ,						device=_lowercase)

												UpperCAmelCase_     							=     self.dummy_model()
												UpperCAmelCase_     							=     self.dummy_sample_deter.to(_lowercase) * scheduler.init_noise_sigma
												UpperCAmelCase_     							=     sample.to(_lowercase)

												for t in scheduler.timesteps:
																		UpperCAmelCase_     							=     scheduler.scale_model_input(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     model(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     scheduler.step(_lowercase   ,						_lowercase   ,						_lowercase)
																		UpperCAmelCase_     							=     output.prev_sample

												UpperCAmelCase_     							=     torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_     							=     torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 3_812.9_927) < 2E-2
												assert abs(result_mean.item() - 4.9_648) < 1E-3










import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
							from PIL import Image







class 							a_  (		unittest.TestCase					, _snake_case				):

						def 	__a       (    self	:Union[str, Any])		->					Optional[Any]:
												UpperCAmelCase_     							=     load_tool('''image-question-answering''')
												self.tool.setup()
												UpperCAmelCase_     							=     load_tool('''image-question-answering'''   ,						remote=_lowercase)

						def 	__a       (    self	:int)		->					Union[str, Any]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:int)		->					Dict:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:Optional[int])		->					int:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')




						def 	__a       (    self	:Optional[Any])		->					List[str]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')





