



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			(EulerAncestralDiscreteScheduler,)
							_a      				=			10




							def        UpperCAmelCase__      (	self					:	Dict    ,    **A_					:	Dict):
									lowerCAmelCase_      :					int     =			{
									    '''num_train_timesteps''': 1_1_0_0,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**A_)
									return config




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=A_)




							def        UpperCAmelCase__      (	self					:	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=A_    ,    beta_end=A_)




							def        UpperCAmelCase__      (	self					:	int):
									for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=A_)




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=A_)




							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					str     =			self.scheduler_classes[0]
									lowerCAmelCase_      :					Optional[Any]     =			self.get_scheduler_config()
									lowerCAmelCase_      :					List[Any]     =			scheduler_class(**A_)

									scheduler.set_timesteps(self.num_inference_steps)

									lowerCAmelCase_      :					List[str]     =			torch.manual_seed(0)

									lowerCAmelCase_      :					Tuple     =			self.dummy_model()
									lowerCAmelCase_      :					List[str]     =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									lowerCAmelCase_      :					Any     =			sample.to(A_)

									for i, t in enumerate(scheduler.timesteps):
											lowerCAmelCase_      :					List[str]     =			scheduler.scale_model_input(A_    ,    A_)

											lowerCAmelCase_      :					int     =			model(A_    ,    A_)

											lowerCAmelCase_      :					Tuple     =			scheduler.step(A_    ,    A_    ,    A_    ,    generator=A_)
											lowerCAmelCase_      :					List[str]     =			output.prev_sample

									lowerCAmelCase_      :					str     =			torch.sum(torch.abs(A_))
									lowerCAmelCase_      :					Union[str, Any]     =			torch.mean(torch.abs(A_))

									assert abs(result_sum.item() - 152.3192) < 1e-2
									assert abs(result_mean.item() - 0.1983) < 1e-3




							def        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					Optional[Any]     =			self.scheduler_classes[0]
									lowerCAmelCase_      :					int     =			self.get_scheduler_config(prediction_type='''v_prediction''')
									lowerCAmelCase_      :					Dict     =			scheduler_class(**A_)

									scheduler.set_timesteps(self.num_inference_steps)

									lowerCAmelCase_      :					Tuple     =			torch.manual_seed(0)

									lowerCAmelCase_      :					Dict     =			self.dummy_model()
									lowerCAmelCase_      :					Tuple     =			self.dummy_sample_deter * scheduler.init_noise_sigma
									lowerCAmelCase_      :					List[Any]     =			sample.to(A_)

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

											lowerCAmelCase_      :					List[str]     =			model(A_    ,    A_)

											lowerCAmelCase_      :					str     =			scheduler.step(A_    ,    A_    ,    A_    ,    generator=A_)
											lowerCAmelCase_      :					str     =			output.prev_sample

									lowerCAmelCase_      :					Optional[int]     =			torch.sum(torch.abs(A_))
									lowerCAmelCase_      :					Any     =			torch.mean(torch.abs(A_))

									assert abs(result_sum.item() - 108.4439) < 1e-2
									assert abs(result_mean.item() - 0.1412) < 1e-3







							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					Tuple     =			self.scheduler_classes[0]
									lowerCAmelCase_      :					Dict     =			self.get_scheduler_config()
									lowerCAmelCase_      :					Union[str, Any]     =			scheduler_class(**A_)

									scheduler.set_timesteps(self.num_inference_steps    ,    device=A_)
									lowerCAmelCase_      :					List[Any]     =			torch.manual_seed(0)

									lowerCAmelCase_      :					Optional[int]     =			self.dummy_model()
									lowerCAmelCase_      :					Dict     =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									lowerCAmelCase_      :					List[str]     =			sample.to(A_)

									for t in scheduler.timesteps:
											lowerCAmelCase_      :					List[Any]     =			scheduler.scale_model_input(A_    ,    A_)

											lowerCAmelCase_      :					int     =			model(A_    ,    A_)

											lowerCAmelCase_      :					Any     =			scheduler.step(A_    ,    A_    ,    A_    ,    generator=A_)
											lowerCAmelCase_      :					List[str]     =			output.prev_sample

									lowerCAmelCase_      :					str     =			torch.sum(torch.abs(A_))
									lowerCAmelCase_      :					Union[str, Any]     =			torch.mean(torch.abs(A_))

									assert abs(result_sum.item() - 152.3192) < 1e-2
									assert abs(result_mean.item() - 0.1983) < 1e-3










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


A__  :  Tuple       = logging.get_logger(__name__)

A__  :  Tuple       = {
    '''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     __snake_case						(					UpperCamelCase_						):
							_a      				=			'''opt'''
							_a      				=			['''past_key_values''']




							def __init__(	self					:	Optional[int]    ,    A_					:	Any=5_0_2_7_2    ,    A_					:	Tuple=7_6_8    ,    A_					:	Optional[Any]=1_2    ,    A_					:	str=3_0_7_2    ,    A_					:	Dict=2_0_4_8    ,    A_					:	List[Any]=True    ,    A_					:	Optional[int]=False    ,    A_					:	Optional[int]=None    ,    A_					:	Dict=0.1    ,    A_					:	str=0.0    ,    A_					:	Union[str, Any]=1_2    ,    A_					:	List[str]="relu"    ,    A_					:	Dict=0.0    ,    A_					:	Union[str, Any]=0.02    ,    A_					:	Optional[Any]=True    ,    A_					:	Optional[Any]=1    ,    A_					:	List[Any]=2    ,    A_					:	Dict=2    ,    A_					:	Optional[int]=True    ,    A_					:	Optional[int]=True    ,    **A_					:	List[Any]    ,    ):
									super().__init__(
									    pad_token_id=A_    ,    bos_token_id=A_    ,    eos_token_id=A_    ,    **A_    ,    )
									lowerCAmelCase_      :					List[Any]     =			vocab_size
									lowerCAmelCase_      :					Union[str, Any]     =			max_position_embeddings
									lowerCAmelCase_      :					Optional[int]     =			num_attention_heads
									lowerCAmelCase_      :					int     =			word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
									lowerCAmelCase_      :					List[Any]     =			ffn_dim
									lowerCAmelCase_      :					Any     =			hidden_size
									lowerCAmelCase_      :					Union[str, Any]     =			num_hidden_layers
									lowerCAmelCase_      :					str     =			dropout
									lowerCAmelCase_      :					List[str]     =			attention_dropout
									lowerCAmelCase_      :					Any     =			activation_function
									lowerCAmelCase_      :					Union[str, Any]     =			init_std
									lowerCAmelCase_      :					int     =			layerdrop
									lowerCAmelCase_      :					Optional[int]     =			use_cache
									lowerCAmelCase_      :					int     =			do_layer_norm_before
									# We keep these variables at `True` for backward compatibility.
									lowerCAmelCase_      :					Optional[Any]     =			enable_bias
									lowerCAmelCase_      :					Optional[int]     =			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
									lowerCAmelCase_      :					Optional[Any]     =			_remove_final_layer_norm





