import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	__snake_case      (   _a      ):
 __lowerCamelCase			:      Optional[int]           =					(EulerDiscreteScheduler,)
 __lowerCamelCase			:      str           =					10
 def 		UpperCAmelCase__				(     self						,      **snake_case__  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       Tuple			     ={
           '''num_train_timesteps''': 1100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**snake_case__  )
       return config
 def 		UpperCAmelCase__				(     self  )		->							List[Any]:
       '''simple docstring'''





       for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__  )
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       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=snake_case__						,      beta_end=snake_case__  )
 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=snake_case__  )
 def 		UpperCAmelCase__				(     self  )		->							Union[str, Any]:
       '''simple docstring'''





       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=snake_case__  )
 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





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

       scheduler.set_timesteps(self.num_inference_steps  )

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

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

       for i, t in enumerate(scheduler.timesteps  ):
             UpperCAmelCase			:       str			     =scheduler.scale_model_input(snake_case__						,      snake_case__  )

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

             UpperCAmelCase			:       Dict			     =scheduler.step(snake_case__						,      snake_case__						,      snake_case__						,      generator=snake_case__  )
             UpperCAmelCase			:       str			     =output.prev_sample

       UpperCAmelCase			:       Optional[Any]			     =torch.sum(torch.abs(snake_case__  )  )
       UpperCAmelCase			:       Dict			     =torch.mean(torch.abs(snake_case__  )  )

       assert abs(result_sum.item() - 10.0807  ) < 1e-2
       assert abs(result_mean.item() - 0.0131  ) < 1e-3
 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





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

       scheduler.set_timesteps(self.num_inference_steps  )

       UpperCAmelCase			:       List[Any]			     =torch.manual_seed(0  )

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

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

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

             UpperCAmelCase			:       Optional[int]			     =scheduler.step(snake_case__						,      snake_case__						,      snake_case__						,      generator=snake_case__  )
             UpperCAmelCase			:       str			     =output.prev_sample

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

       assert abs(result_sum.item() - 0.0002  ) < 1e-2
       assert abs(result_mean.item() - 2.26_76e-06  ) < 1e-3
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       List[str]			     =self.scheduler_classes[0]
       UpperCAmelCase			:       List[Any]			     =self.get_scheduler_config()
       UpperCAmelCase			:       List[str]			     =scheduler_class(**snake_case__  )

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

       UpperCAmelCase			:       Optional[int]			     =torch.manual_seed(0  )

       UpperCAmelCase			:       Tuple			     =self.dummy_model()
       UpperCAmelCase			:       Dict			     =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase			:       List[Any]			     =sample.to(snake_case__  )

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

             UpperCAmelCase			:       int			     =model(snake_case__						,      snake_case__  )

             UpperCAmelCase			:       Any			     =scheduler.step(snake_case__						,      snake_case__						,      snake_case__						,      generator=snake_case__  )
             UpperCAmelCase			:       Optional[int]			     =output.prev_sample

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

       assert abs(result_sum.item() - 10.0807  ) < 1e-2
       assert abs(result_mean.item() - 0.0131  ) < 1e-3






 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =self.scheduler_classes[0]
       UpperCAmelCase			:       List[Any]			     =self.get_scheduler_config()
       UpperCAmelCase			:       Optional[int]			     =scheduler_class(**snake_case__						,      use_karras_sigmas=snake_case__  )

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

       UpperCAmelCase			:       Optional[int]			     =torch.manual_seed(0  )

       UpperCAmelCase			:       List[Any]			     =self.dummy_model()
       UpperCAmelCase			:       Optional[int]			     =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase			:       Tuple			     =sample.to(snake_case__  )

       for t in scheduler.timesteps:
             UpperCAmelCase			:       int			     =scheduler.scale_model_input(snake_case__						,      snake_case__  )

             UpperCAmelCase			:       Any			     =model(snake_case__						,      snake_case__  )

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

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

       assert abs(result_sum.item() - 124.52_2994_9951_1719  ) < 1e-2
       assert abs(result_mean.item() - 0.1_6213_9326_3339_9963  ) < 1e-3







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


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
