

"""simple docstring"""






import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						(DPMSolverSDEScheduler,)
			SCREAMING_SNAKE_CASE__     				=						10







			def 			__lowerCAmelCase				(			self			: int			,  **lowercase			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				{
								    "num_train_timesteps": 11_00,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "noise_sampler_seed": 0,
								}

								config.update(**lowercase  )
								return config







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								for timesteps in [10, 50, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=lowercase  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,  [0.0_0_0_2, 0.0_0_2, 0.0_2]  ):
													self.check_over_configs(beta_start=lowercase			,  beta_end=lowercase  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Dict      =				self.get_scheduler_config()
								UpperCAmelCase			:   Union[str, Any]      =				scheduler_class(**lowercase  )

								scheduler.set_timesteps(self.num_inference_steps  )

								UpperCAmelCase			:   Optional[Any]      =				self.dummy_model()
								UpperCAmelCase			:   Any      =				self.dummy_sample_deter * scheduler.init_noise_sigma
								UpperCAmelCase			:   int      =				sample.to(lowercase  )

								for i, t in enumerate(scheduler.timesteps  ):
													UpperCAmelCase			:   Optional[int]      =				scheduler.scale_model_input(lowercase			,  lowercase  )

													UpperCAmelCase			:   List[Any]      =				model(lowercase			,  lowercase  )

													UpperCAmelCase			:   Union[str, Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase  )
													UpperCAmelCase			:   int      =				output.prev_sample

								UpperCAmelCase			:   List[str]      =				torch.sum(torch.abs(lowercase  )  )
								UpperCAmelCase			:   Tuple      =				torch.mean(torch.abs(lowercase  )  )

								if torch_device in ["mps"]:
													assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75  ) < 1E-2
													assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7  ) < 1E-3
								elif torch_device in ["cuda"]:
													assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06  ) < 1E-2
													assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2  ) < 1E-3
								else:
													assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62  ) < 1E-2
													assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   List[str]      =				self.get_scheduler_config(prediction_type="v_prediction"  )
								UpperCAmelCase			:   Optional[Any]      =				scheduler_class(**lowercase  )

								scheduler.set_timesteps(self.num_inference_steps  )

								UpperCAmelCase			:   List[str]      =				self.dummy_model()
								UpperCAmelCase			:   List[Any]      =				self.dummy_sample_deter * scheduler.init_noise_sigma
								UpperCAmelCase			:   List[Any]      =				sample.to(lowercase  )

								for i, t in enumerate(scheduler.timesteps  ):
													UpperCAmelCase			:   int      =				scheduler.scale_model_input(lowercase			,  lowercase  )

													UpperCAmelCase			:   Optional[int]      =				model(lowercase			,  lowercase  )

													UpperCAmelCase			:   Union[str, Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase  )
													UpperCAmelCase			:   Tuple      =				output.prev_sample

								UpperCAmelCase			:   Any      =				torch.sum(torch.abs(lowercase  )  )
								UpperCAmelCase			:   Tuple      =				torch.mean(torch.abs(lowercase  )  )

								if torch_device in ["mps"]:
													assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53  ) < 1E-2
													assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4  ) < 1E-3
								elif torch_device in ["cuda"]:
													assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03  ) < 1E-2
													assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7  ) < 1E-3
								else:
													assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25  ) < 1E-2
													assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Optional[int]      =				self.get_scheduler_config()
								UpperCAmelCase			:   List[str]      =				scheduler_class(**lowercase  )

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

								UpperCAmelCase			:   int      =				self.dummy_model()
								UpperCAmelCase			:   int      =				self.dummy_sample_deter.to(lowercase  ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
													UpperCAmelCase			:   List[str]      =				scheduler.scale_model_input(lowercase			,  lowercase  )

													UpperCAmelCase			:   str      =				model(lowercase			,  lowercase  )

													UpperCAmelCase			:   Tuple      =				scheduler.step(lowercase			,  lowercase			,  lowercase  )
													UpperCAmelCase			:   Union[str, Any]      =				output.prev_sample

								UpperCAmelCase			:   int      =				torch.sum(torch.abs(lowercase  )  )
								UpperCAmelCase			:   Optional[int]      =				torch.mean(torch.abs(lowercase  )  )

								if torch_device in ["mps"]:
													assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38  ) < 1E-2
													assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5  ) < 1E-3
								elif torch_device in ["cuda"]:
													assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12  ) < 1E-2
													assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1  ) < 1E-3
								else:
													assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62  ) < 1E-2
													assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Tuple      =				self.get_scheduler_config()
								UpperCAmelCase			:   Any      =				scheduler_class(**lowercase			,  use_karras_sigmas=lowercase  )

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

								UpperCAmelCase			:   List[str]      =				self.dummy_model()
								UpperCAmelCase			:   int      =				self.dummy_sample_deter.to(lowercase  ) * scheduler.init_noise_sigma
								UpperCAmelCase			:   int      =				sample.to(lowercase  )

								for t in scheduler.timesteps:
													UpperCAmelCase			:   Dict      =				scheduler.scale_model_input(lowercase			,  lowercase  )

													UpperCAmelCase			:   List[str]      =				model(lowercase			,  lowercase  )

													UpperCAmelCase			:   int      =				scheduler.step(lowercase			,  lowercase			,  lowercase  )
													UpperCAmelCase			:   List[str]      =				output.prev_sample

								UpperCAmelCase			:   List[Any]      =				torch.sum(torch.abs(lowercase  )  )
								UpperCAmelCase			:   Optional[Any]      =				torch.mean(torch.abs(lowercase  )  )

								if torch_device in ["mps"]:
													assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88  ) < 1E-2
													assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1  ) < 1E-2
								elif torch_device in ["cuda"]:
													assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25  ) < 1E-2
													assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1  ) < 1E-2
								else:
													assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72  ) < 1E-2
													assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1  ) < 1E-2





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_   :   List[Any]        =       logging.get_logger(__name__)

snake_case_   :   List[Any]        =       {
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''rwkv'''
			SCREAMING_SNAKE_CASE__     				=						{'''max_position_embeddings''': '''context_length'''}







			def __init__(			self			: int			,  lowercase			: Optional[Any]=5_02_77			,  lowercase			: Union[str, Any]=10_24			,  lowercase			: Union[str, Any]=40_96			,  lowercase			: List[str]=32			,  lowercase			: str=None			,  lowercase			: Tuple=None			,  lowercase			: Dict=1E-5			,  lowercase			: Any=0			,  lowercase			: List[Any]=0			,  lowercase			: int=6			,  lowercase			: Dict=False			,  lowercase			: Dict=True			,  **lowercase			: Optional[Any]			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				vocab_size
								UpperCAmelCase			:   List[str]      =				context_length
								UpperCAmelCase			:   Optional[Any]      =				hidden_size
								UpperCAmelCase			:   List[Any]      =				num_hidden_layers
								UpperCAmelCase			:   Union[str, Any]      =				attention_hidden_size if attention_hidden_size is not None else hidden_size
								UpperCAmelCase			:   List[Any]      =				intermediate_size if intermediate_size is not None else 4 * hidden_size
								UpperCAmelCase			:   Tuple      =				layer_norm_epsilon
								UpperCAmelCase			:   Optional[int]      =				rescale_every
								UpperCAmelCase			:   Tuple      =				use_cache

								UpperCAmelCase			:   Union[str, Any]      =				bos_token_id
								UpperCAmelCase			:   Optional[int]      =				eos_token_id

								super().__init__(
								    tie_word_embeddings=lowercase			,  bos_token_id=lowercase			,  eos_token_id=lowercase			,  **lowercase  )


