


"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							(DDPMScheduler,)
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	{
					    '''num_train_timesteps''': 10_00,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					    '''variance_type''': '''fixed_small''',
					    '''clip_sample''': True,
					}

					config.update(**SCREAMING_SNAKE_CASE__   )
					return config
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					for timesteps in [1, 5, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,				[0.0_0_2, 0.0_2, 0.2, 2]   ):
								self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__					,				beta_end=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					for variance in ["fixed_small", "fixed_large", "other"]:
								self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__   )
					for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(
											    thresholding=SCREAMING_SNAKE_CASE__					,				prediction_type=SCREAMING_SNAKE_CASE__					,				sample_max_value=SCREAMING_SNAKE_CASE__					,				)
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					for t in [0, 5_00, 9_99]:
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.scheduler_classes[0]
					_UpperCamelCase					:Any					     =	self.get_scheduler_config()
					_UpperCamelCase					:Optional[Any]					     =	scheduler_class(**SCREAMING_SNAKE_CASE__   )

					assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1E-5
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.scheduler_classes[0]
					_UpperCamelCase					:List[Any]					     =	self.get_scheduler_config()
					_UpperCamelCase					:Optional[Any]					     =	scheduler_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	len(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	self.dummy_model()
					_UpperCamelCase					:str					     =	self.dummy_sample_deter
					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )

					for t in reversed(range(SCREAMING_SNAKE_CASE__   )   ):
								# 1. predict noise residual
								_UpperCamelCase					:Tuple					     =	model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

								# 2. predict previous mean of sample x_t-1
								_UpperCamelCase					:int					     =	scheduler.step(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

								# if t > 0:
								#     noise = self.dummy_sample_deter
								#     variance = scheduler.get_variance(t) ** (0.5) * noise
								#
								# sample = pred_prev_sample + variance
								_UpperCamelCase					:Any					     =	pred_prev_sample

					_UpperCamelCase					:Optional[int]					     =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:List[str]					     =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

					assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1E-2
					assert abs(result_mean.item() - 0.3_3_7_2   ) < 1E-3
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.scheduler_classes[0]
					_UpperCamelCase					:Union[str, Any]					     =	self.get_scheduler_config(prediction_type='''v_prediction'''   )
					_UpperCamelCase					:str					     =	scheduler_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	len(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:int					     =	self.dummy_model()
					_UpperCamelCase					:Any					     =	self.dummy_sample_deter
					_UpperCamelCase					:Tuple					     =	torch.manual_seed(0   )

					for t in reversed(range(SCREAMING_SNAKE_CASE__   )   ):
								# 1. predict noise residual
								_UpperCamelCase					:Optional[int]					     =	model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

								# 2. predict previous mean of sample x_t-1
								_UpperCamelCase					:Union[str, Any]					     =	scheduler.step(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

								# if t > 0:
								#     noise = self.dummy_sample_deter
								#     variance = scheduler.get_variance(t) ** (0.5) * noise
								#
								# sample = pred_prev_sample + variance
								_UpperCamelCase					:Union[str, Any]					     =	pred_prev_sample

					_UpperCamelCase					:Union[str, Any]					     =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:Optional[Any]					     =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

					assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1E-2
					assert abs(result_mean.item() - 0.2_6_3_1   ) < 1E-3
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.scheduler_classes[0]
					_UpperCamelCase					:str					     =	self.get_scheduler_config()
					_UpperCamelCase					:int					     =	scheduler_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	[1_00, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Tuple					     =	scheduler.timesteps

					for i, timestep in enumerate(SCREAMING_SNAKE_CASE__   ):
								if i == len(SCREAMING_SNAKE_CASE__   ) - 1:
											_UpperCamelCase					:Optional[Any]					     =	-1
								else:
											_UpperCamelCase					:Optional[int]					     =	timesteps[i + 1]

								_UpperCamelCase					:str					     =	scheduler.previous_timestep(SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:Optional[int]					     =	prev_t.item()

								self.assertEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.scheduler_classes[0]
					_UpperCamelCase					:Union[str, Any]					     =	self.get_scheduler_config()
					_UpperCamelCase					:Dict					     =	scheduler_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	[1_00, 87, 50, 51, 0]

					with self.assertRaises(SCREAMING_SNAKE_CASE__					,				msg='''`custom_timesteps` must be in descending order.'''   ):
								scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.scheduler_classes[0]
					_UpperCamelCase					:int					     =	self.get_scheduler_config()
					_UpperCamelCase					:Dict					     =	scheduler_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	[1_00, 87, 50, 1, 0]
					_UpperCamelCase					:Optional[int]					     =	len(SCREAMING_SNAKE_CASE__   )

					with self.assertRaises(SCREAMING_SNAKE_CASE__					,				msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
								scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE__					,				timesteps=SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	self.scheduler_classes[0]
					_UpperCamelCase					:List[str]					     =	self.get_scheduler_config()
					_UpperCamelCase					:Optional[Any]					     =	scheduler_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	[scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    SCREAMING_SNAKE_CASE__					,				msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''					,				):
								scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__   )





"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

