





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  _a  (					__UpperCamelCase				):






							"""simple docstring"""

							snake_case_							=    (HeunDiscreteScheduler,)
							snake_case_							=    10

							def   A_	(   self					:  int				,						**a					:  List[str]     )						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					Dict =       {
													    "num_train_timesteps": 11_00,
													    "beta_start": 0.0001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													}

													config.update(**__lowercase     )
													return config

							def   A_	(   self					:  Optional[int]     )						->Any:
													for timesteps in [10, 50, 1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=__lowercase     )

							def   A_	(   self					:  Tuple     )						->List[str]:
													for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]				,						[0.0002, 0.002, 0.02]     ):
																			self.check_over_configs(beta_start=__lowercase				,						beta_end=__lowercase     )

							def   A_	(   self					:  List[Any]     )						->Optional[int]:
													for schedule in ["linear", "scaled_linear", "exp"]:
																			self.check_over_configs(beta_schedule=__lowercase     )

							def   A_	(   self					:  Tuple     )						->int:
													for clip_sample_range in [1.0, 2.0, 3.0]:
																			self.check_over_configs(clip_sample_range=__lowercase				,						clip_sample=__lowercase     )

							def   A_	(   self					:  str     )						->List[Any]:
													for prediction_type in ["epsilon", "v_prediction", "sample"]:
																			self.check_over_configs(prediction_type=__lowercase     )

							def   A_	(   self					:  Tuple     )						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__  :					Tuple =       scheduler_class(**__lowercase     )

													scheduler.set_timesteps(self.num_inference_steps     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       self.dummy_model()
													SCREAMING_SNAKE_CASE__  :					int =       self.dummy_sample_deter * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE__  :					Any =       sample.to(__lowercase     )

													for i, t in enumerate(scheduler.timesteps     ):
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       scheduler.scale_model_input(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					str =       model(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					Tuple =       scheduler.step(__lowercase				,						__lowercase				,						__lowercase     )
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       output.prev_sample

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

													if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 0.1233     ) < 1E-2
																			assert abs(result_mean.item() - 0.0002     ) < 1E-3
													else:
																			# CUDA
																			assert abs(result_sum.item() - 0.1233     ) < 1E-2
																			assert abs(result_mean.item() - 0.0002     ) < 1E-3

							def   A_	(   self					:  int     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					List[str] =       self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.get_scheduler_config(prediction_type="v_prediction"     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       scheduler_class(**__lowercase     )

													scheduler.set_timesteps(self.num_inference_steps     )

													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.dummy_model()
													SCREAMING_SNAKE_CASE__  :					List[str] =       self.dummy_sample_deter * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       sample.to(__lowercase     )

													for i, t in enumerate(scheduler.timesteps     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       scheduler.scale_model_input(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       model(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       scheduler.step(__lowercase				,						__lowercase				,						__lowercase     )
																			SCREAMING_SNAKE_CASE__  :					str =       output.prev_sample

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       torch.sum(torch.abs(__lowercase     )     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       torch.mean(torch.abs(__lowercase     )     )

													if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 4.6934E-07     ) < 1E-2
																			assert abs(result_mean.item() - 6.1112E-10     ) < 1E-3
													else:
																			# CUDA
																			assert abs(result_sum.item() - 4.693_4286_5017_0972E-07     ) < 1E-2
																			assert abs(result_mean.item() - 0.0002     ) < 1E-3

							def   A_	(   self					:  Any     )						->int:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__  :					List[str] =       scheduler_class(**__lowercase     )

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

													SCREAMING_SNAKE_CASE__  :					Dict =       self.dummy_model()
													SCREAMING_SNAKE_CASE__  :					List[str] =       self.dummy_sample_deter.to(__lowercase     ) * scheduler.init_noise_sigma

													for t in scheduler.timesteps:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       scheduler.scale_model_input(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					Any =       model(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       scheduler.step(__lowercase				,						__lowercase				,						__lowercase     )
																			SCREAMING_SNAKE_CASE__  :					Any =       output.prev_sample

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       torch.sum(torch.abs(__lowercase     )     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       torch.mean(torch.abs(__lowercase     )     )

													if str(__lowercase     ).startswith("cpu"     ):
																			# The following sum varies between 148 and 156 on mps. Why?
																			assert abs(result_sum.item() - 0.1233     ) < 1E-2
																			assert abs(result_mean.item() - 0.0002     ) < 1E-3
													elif str(__lowercase     ).startswith("mps"     ):
																			# Larger tolerance on mps
																			assert abs(result_mean.item() - 0.0002     ) < 1E-2
													else:
																			# CUDA
																			assert abs(result_sum.item() - 0.1233     ) < 1E-2
																			assert abs(result_mean.item() - 0.0002     ) < 1E-3






							def   A_	(   self					:  int     )						->int:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       scheduler_class(**__lowercase				,						use_karras_sigmas=__lowercase     )

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

													SCREAMING_SNAKE_CASE__  :					Dict =       self.dummy_model()
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.dummy_sample_deter.to(__lowercase     ) * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE__  :					int =       sample.to(__lowercase     )

													for t in scheduler.timesteps:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       scheduler.scale_model_input(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					Any =       model(__lowercase				,						__lowercase     )

																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       scheduler.step(__lowercase				,						__lowercase				,						__lowercase     )
																			SCREAMING_SNAKE_CASE__  :					List[str] =       output.prev_sample

													SCREAMING_SNAKE_CASE__  :					Any =       torch.sum(torch.abs(__lowercase     )     )
													SCREAMING_SNAKE_CASE__  :					Any =       torch.mean(torch.abs(__lowercase     )     )

													assert abs(result_sum.item() - 0.0_0015     ) < 1E-2
													assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07     ) < 1E-2







"""simple docstring"""



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


a  :						Optional[Any]					       =							logging.get_logger(__name__)

a  :						int					       =							{
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						'opt'
    __lowercase    :   str											=						['past_key_values']


    def __init__(      self   ,			__lowercase=50272   ,			__lowercase=768   ,			__lowercase=12   ,			__lowercase=3072   ,			__lowercase=2048   ,			__lowercase=True   ,			__lowercase=False   ,			__lowercase=None   ,			__lowercase=0.1   ,			__lowercase=0.0   ,			__lowercase=12   ,			__lowercase="relu"   ,			__lowercase=0.0   ,			__lowercase=0.02   ,			__lowercase=True   ,			__lowercase=1   ,			__lowercase=2   ,			__lowercase=2   ,			__lowercase=True   ,			__lowercase=True   ,			**__lowercase   ,			):
     super().__init__(
         pad_token_id=__lowercase   ,			bos_token_id=__lowercase   ,			eos_token_id=__lowercase   ,			**__lowercase   ,			)
     UpperCAmelCase__ 						=				vocab_size
     UpperCAmelCase__ 						=				max_position_embeddings
     UpperCAmelCase__ 						=				num_attention_heads
     UpperCAmelCase__ 						=				word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
     UpperCAmelCase__ 						=				ffn_dim
     UpperCAmelCase__ 						=				hidden_size
     UpperCAmelCase__ 						=				num_hidden_layers
     UpperCAmelCase__ 						=				dropout
     UpperCAmelCase__ 						=				attention_dropout
     UpperCAmelCase__ 						=				activation_function
     UpperCAmelCase__ 						=				init_std
     UpperCAmelCase__ 						=				layerdrop
     UpperCAmelCase__ 						=				use_cache
     UpperCAmelCase__ 						=				do_layer_norm_before
     # We keep these variables at `True` for backward compatibility.
     UpperCAmelCase__ 						=				enable_bias
     UpperCAmelCase__ 						=				layer_norm_elementwise_affine

     # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
     # with checkpoints that have been fine-tuned before transformers v4.20.1
     # see https://github.com/facebookresearch/metaseq/pull/164
     UpperCAmelCase__ 						=				_remove_final_layer_norm



