



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	(LMSDiscreteScheduler,)
		SCREAMING_SNAKE_CASE_       :			str					       =	1_0







		def 					a_  (    self  :   Dict       ,		**UpperCAmelCase_  :   Optional[int]					)       ->		int:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	{
						    '''num_train_timesteps''': 1100,
						    '''beta_start''': 0.0_001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						}

						config.update(**UpperCAmelCase_					)
						return config







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


						'''simple docstring'''







						for timesteps in [10, 50, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







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


						'''simple docstring'''







						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=UpperCAmelCase_       ,		beta_end=UpperCAmelCase_					)







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


						'''simple docstring'''







						for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=UpperCAmelCase_					)







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


						'''simple docstring'''







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







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


						'''simple docstring'''







						for t in [0, 500, 800]:
										self.check_over_forward(time_step=UpperCAmelCase_					)







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


						'''simple docstring'''







						_UpperCAmelCase						:  List[Any]     					=	self.scheduler_classes[0]
						_UpperCAmelCase						:  List[str]     					=	self.get_scheduler_config()
						_UpperCAmelCase						:  Optional[int]     					=	scheduler_class(**UpperCAmelCase_					)

						scheduler.set_timesteps(self.num_inference_steps					)

						_UpperCAmelCase						:  Optional[Any]     					=	self.dummy_model()
						_UpperCAmelCase						:  Tuple     					=	self.dummy_sample_deter * scheduler.init_noise_sigma

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

										_UpperCAmelCase						:  Tuple     					=	model(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  int     					=	scheduler.step(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					)
										_UpperCAmelCase						:  List[Any]     					=	output.prev_sample

						_UpperCAmelCase						:  Dict     					=	torch.sum(torch.abs(UpperCAmelCase_					)					)
						_UpperCAmelCase						:  List[Any]     					=	torch.mean(torch.abs(UpperCAmelCase_					)					)

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







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


						'''simple docstring'''







						_UpperCAmelCase						:  List[Any]     					=	self.scheduler_classes[0]
						_UpperCAmelCase						:  Optional[int]     					=	self.get_scheduler_config(prediction_type='''v_prediction'''					)
						_UpperCAmelCase						:  str     					=	scheduler_class(**UpperCAmelCase_					)

						scheduler.set_timesteps(self.num_inference_steps					)

						_UpperCAmelCase						:  Dict     					=	self.dummy_model()
						_UpperCAmelCase						:  Optional[Any]     					=	self.dummy_sample_deter * scheduler.init_noise_sigma

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

										_UpperCAmelCase						:  List[str]     					=	model(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  List[str]     					=	scheduler.step(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					)
										_UpperCAmelCase						:  Dict     					=	output.prev_sample

						_UpperCAmelCase						:  Union[str, Any]     					=	torch.sum(torch.abs(UpperCAmelCase_					)					)
						_UpperCAmelCase						:  Tuple     					=	torch.mean(torch.abs(UpperCAmelCase_					)					)

						assert abs(result_sum.item() - 0.0_017					) < 1E-2
						assert abs(result_mean.item() - 2.26_76E-06					) < 1E-3







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


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	self.scheduler_classes[0]
						_UpperCAmelCase						:  int     					=	self.get_scheduler_config()
						_UpperCAmelCase						:  List[str]     					=	scheduler_class(**UpperCAmelCase_					)

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

						_UpperCAmelCase						:  List[str]     					=	self.dummy_model()
						_UpperCAmelCase						:  str     					=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						_UpperCAmelCase						:  Union[str, Any]     					=	sample.to(UpperCAmelCase_					)

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

										_UpperCAmelCase						:  Optional[int]     					=	model(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  Optional[int]     					=	scheduler.step(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					)
										_UpperCAmelCase						:  List[Any]     					=	output.prev_sample

						_UpperCAmelCase						:  List[str]     					=	torch.sum(torch.abs(UpperCAmelCase_					)					)
						_UpperCAmelCase						:  int     					=	torch.mean(torch.abs(UpperCAmelCase_					)					)

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







		def 					a_  (    self  :   Dict					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	self.scheduler_classes[0]
						_UpperCAmelCase						:  Any     					=	self.get_scheduler_config()
						_UpperCAmelCase						:  int     					=	scheduler_class(**UpperCAmelCase_       ,		use_karras_sigmas=UpperCAmelCase_					)

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

						_UpperCAmelCase						:  List[str]     					=	self.dummy_model()
						_UpperCAmelCase						:  Optional[Any]     					=	self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma
						_UpperCAmelCase						:  Dict     					=	sample.to(UpperCAmelCase_					)

						for t in scheduler.timesteps:
										_UpperCAmelCase						:  Optional[int]     					=	scheduler.scale_model_input(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  Any     					=	model(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  Tuple     					=	scheduler.step(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					)
										_UpperCAmelCase						:  Dict     					=	output.prev_sample

						_UpperCAmelCase						:  List[str]     					=	torch.sum(torch.abs(UpperCAmelCase_					)					)
						_UpperCAmelCase						:  List[Any]     					=	torch.mean(torch.abs(UpperCAmelCase_					)					)

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





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


UpperCAmelCase__			:   str      			=    importlib.metadata.version('numpy')
UpperCAmelCase__			:   Optional[int]      			=    '.'.join([str(x) for x in sys.version_info[:3]])





class 							lowerCAmelCase_		(		lowercase_     ):







		def 					a_  (    self  :   Tuple					)       ->		Tuple:


						'''simple docstring'''







						require_version_core('''numpy<1000.4.5'''					)
						require_version_core('''numpy<1000.4'''					)
						require_version_core('''numpy<1000'''					)

						# le
						require_version_core('''numpy<=1000.4.5'''					)
						require_version_core(F'''numpy<={numpy_ver}'''					)

						# eq
						require_version_core(F'''numpy=={numpy_ver}'''					)

						# ne
						require_version_core('''numpy!=1000.4.5'''					)

						# ge
						require_version_core('''numpy>=1.0'''					)
						require_version_core('''numpy>=1.0.0'''					)
						require_version_core(F'''numpy>={numpy_ver}'''					)

						# gt
						require_version_core('''numpy>1.0.0'''					)

						# mix
						require_version_core('''numpy>1.0.0,<1000'''					)

						# requirement w/o version
						require_version_core('''numpy'''					)

						# unmet requirements due to version conflict
						for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''but found'''       ,		str(UpperCAmelCase_					)					)

        # unmet requirements due to missing module
						for req in ["numpipypie>1", "numpipypie2"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except importlib.metadata.PackageNotFoundError as e:
														self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''Try: pip install transformers -U'''       ,		str(UpperCAmelCase_					)					)

        # bogus requirements formats:
        # 1. whole thing
						for req in ["numpy??1.0.0", "numpy1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''requirement needs to be in the pip package format'''       ,		str(UpperCAmelCase_					)					)
        # 2. only operators
						for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''need one of '''       ,		str(UpperCAmelCase_					)					)







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


						'''simple docstring'''







						require_version('''python>=3.6.0'''					)

						# not matching requirements
						for req in ["python>9.9.9", "python<3.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn(F'''but found python=={python_ver}'''       ,		str(UpperCAmelCase_					)					)
