





'''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  _A (					_A		):
		lowercase__:   Union[str, Any]				     =   (DPMSolverSDEScheduler,)
		lowercase__:   Any				     =   10

		def 	lowercase__     (   self					:   List[Any]  ,      **__magic_name__					:   List[Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     {
					    """num_train_timesteps""": 11_00,
					    """beta_start""": 0.0001,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					    """noise_sampler_seed""": 0,
					}

					config.update(**__lowerCAmelCase			)
					return config

		def 	lowercase__     (   self					:   Tuple			)					->    str:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict			)					->    Any:


					"""simple docstring"""

					for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,      [0.0002, 0.002, 0.02]			):
								self.check_over_configs(beta_start=__lowerCAmelCase  ,      beta_end=__lowerCAmelCase			)

		def 	lowercase__     (   self					:   Any			)					->    int:


					"""simple docstring"""

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

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


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      str							      =     self.scheduler_classes[0]
					__snake_case					:      Optional[Any]							      =     self.get_scheduler_config()
					__snake_case					:      Optional[int]							      =     scheduler_class(**__lowerCAmelCase			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__snake_case					:      Dict							      =     self.dummy_model()
					__snake_case					:      Optional[Any]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
					__snake_case					:      Any							      =     sample.to(__lowerCAmelCase			)

					for i, t in enumerate(scheduler.timesteps			):
								__snake_case					:      List[Any]							      =     scheduler.scale_model_input(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      Tuple							      =     model(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      Any							      =     scheduler.step(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase			)
								__snake_case					:      int							      =     output.prev_sample

					__snake_case					:      Tuple							      =     torch.sum(torch.abs(__lowerCAmelCase			)			)
					__snake_case					:      Optional[Any]							      =     torch.mean(torch.abs(__lowerCAmelCase			)			)

					if torch_device in ["mps"]:
								assert abs(result_sum.item() - 167.47821044921875			) < 1E-2
								assert abs(result_mean.item() - 0.2178705964565277			) < 1E-3
					elif torch_device in ["cuda"]:
								assert abs(result_sum.item() - 171.59352111816406			) < 1E-2
								assert abs(result_mean.item() - 0.22342906892299652			) < 1E-3
					else:
								assert abs(result_sum.item() - 162.52383422851562			) < 1E-2
								assert abs(result_mean.item() - 0.211619570851326			) < 1E-3

		def 	lowercase__     (   self					:   Dict			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.scheduler_classes[0]
					__snake_case					:      Optional[int]							      =     self.get_scheduler_config(prediction_type="""v_prediction"""			)
					__snake_case					:      Any							      =     scheduler_class(**__lowerCAmelCase			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__snake_case					:      Tuple							      =     self.dummy_model()
					__snake_case					:      Optional[int]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
					__snake_case					:      Dict							      =     sample.to(__lowerCAmelCase			)

					for i, t in enumerate(scheduler.timesteps			):
								__snake_case					:      Any							      =     scheduler.scale_model_input(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      str							      =     model(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      Optional[int]							      =     scheduler.step(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase			)
								__snake_case					:      str							      =     output.prev_sample

					__snake_case					:      Optional[int]							      =     torch.sum(torch.abs(__lowerCAmelCase			)			)
					__snake_case					:      List[Any]							      =     torch.mean(torch.abs(__lowerCAmelCase			)			)

					if torch_device in ["mps"]:
								assert abs(result_sum.item() - 124.77149200439453			) < 1E-2
								assert abs(result_mean.item() - 0.16226289014816284			) < 1E-3
					elif torch_device in ["cuda"]:
								assert abs(result_sum.item() - 128.1663360595703			) < 1E-2
								assert abs(result_mean.item() - 0.16688326001167297			) < 1E-3
					else:
								assert abs(result_sum.item() - 119.8487548828125			) < 1E-2
								assert abs(result_mean.item() - 0.1560530662536621			) < 1E-3

		def 	lowercase__     (   self					:   int			)					->    Any:


					"""simple docstring"""

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

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

					__snake_case					:      int							      =     self.dummy_model()
					__snake_case					:      int							      =     self.dummy_sample_deter.to(__lowerCAmelCase			) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
								__snake_case					:      Any							      =     scheduler.scale_model_input(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      Optional[Any]							      =     model(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      Optional[int]							      =     scheduler.step(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase			)
								__snake_case					:      Optional[Any]							      =     output.prev_sample

					__snake_case					:      str							      =     torch.sum(torch.abs(__lowerCAmelCase			)			)
					__snake_case					:      List[str]							      =     torch.mean(torch.abs(__lowerCAmelCase			)			)

					if torch_device in ["mps"]:
								assert abs(result_sum.item() - 167.46957397460938			) < 1E-2
								assert abs(result_mean.item() - 0.21805934607982635			) < 1E-3
					elif torch_device in ["cuda"]:
								assert abs(result_sum.item() - 171.59353637695312			) < 1E-2
								assert abs(result_mean.item() - 0.22342908382415771			) < 1E-3
					else:
								assert abs(result_sum.item() - 162.52383422851562			) < 1E-2
								assert abs(result_mean.item() - 0.211619570851326			) < 1E-3

		def 	lowercase__     (   self					:   Tuple			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     self.scheduler_classes[0]
					__snake_case					:      Optional[int]							      =     self.get_scheduler_config()
					__snake_case					:      List[str]							      =     scheduler_class(**__lowerCAmelCase  ,      use_karras_sigmas=__lowerCAmelCase			)

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

					__snake_case					:      Optional[Any]							      =     self.dummy_model()
					__snake_case					:      Tuple							      =     self.dummy_sample_deter.to(__lowerCAmelCase			) * scheduler.init_noise_sigma
					__snake_case					:      Union[str, Any]							      =     sample.to(__lowerCAmelCase			)

					for t in scheduler.timesteps:
								__snake_case					:      Tuple							      =     scheduler.scale_model_input(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      Optional[int]							      =     model(__lowerCAmelCase  ,      __lowerCAmelCase			)

								__snake_case					:      Tuple							      =     scheduler.step(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase			)
								__snake_case					:      Optional[Any]							      =     output.prev_sample

					__snake_case					:      Optional[int]							      =     torch.sum(torch.abs(__lowerCAmelCase			)			)
					__snake_case					:      int							      =     torch.mean(torch.abs(__lowerCAmelCase			)			)

					if torch_device in ["mps"]:
								assert abs(result_sum.item() - 176.66974135742188			) < 1E-2
								assert abs(result_mean.item() - 0.23003872730981811			) < 1E-2
					elif torch_device in ["cuda"]:
								assert abs(result_sum.item() - 177.63653564453125			) < 1E-2
								assert abs(result_mean.item() - 0.23003872730981811			) < 1E-2
					else:
								assert abs(result_sum.item() - 170.3135223388672			) < 1E-2
								assert abs(result_mean.item() - 0.23003872730981811			) < 1E-2







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


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  "openai-gpt"
 a__					: Dict =  {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					int=4_04_78			,						__lowerCAmelCase					:					Tuple=5_12			,						__lowerCAmelCase					:					str=7_68			,						__lowerCAmelCase					:					List[Any]=12			,						__lowerCAmelCase					:					Any=12			,						__lowerCAmelCase					:					Dict="gelu"			,						__lowerCAmelCase					:					Union[str, Any]=0.1			,						__lowerCAmelCase					:					Optional[int]=0.1			,						__lowerCAmelCase					:					Any=0.1			,						__lowerCAmelCase					:					List[str]=1E-5			,						__lowerCAmelCase					:					Optional[int]=0.02			,						__lowerCAmelCase					:					Optional[Any]="cls_index"			,						__lowerCAmelCase					:					str=True			,						__lowerCAmelCase					:					int=None			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Optional[Any]=0.1			,						**__lowerCAmelCase					:					Tuple			,						)   ->	Optional[Any]:
    _A      =       vocab_size
    _A      =       n_positions
    _A      =       n_embd
    _A      =       n_layer
    _A      =       n_head
    _A      =       afn
    _A      =       resid_pdrop
    _A      =       embd_pdrop
    _A      =       attn_pdrop
    _A      =       layer_norm_epsilon
    _A      =       initializer_range
    _A      =       summary_type
    _A      =       summary_use_proj
    _A      =       summary_activation
    _A      =       summary_first_dropout
    _A      =       summary_proj_to_labels
    super().__init__(**__lowerCAmelCase      )



