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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				(DPMSolverSDEScheduler,)
  lowercase		:     Dict            =				10







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Tuple:
         A						:     Dict         ={
             '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(**SCREAMING_SNAKE_CASE__     )
         return config







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				str:
         for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Tuple:
         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=SCREAMING_SNAKE_CASE__      ,	beta_end=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Union[str, Any]:
         for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__     )







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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Tuple         =self.scheduler_classes[0]
         A						:     Tuple         =self.get_scheduler_config()
         A						:     Tuple         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

         scheduler.set_timesteps(self.num_inference_steps     )

         A						:     Dict         =self.dummy_model()
         A						:     Any         =self.dummy_sample_deter * scheduler.init_noise_sigma
         A						:     Union[str, Any]         =sample.to(SCREAMING_SNAKE_CASE__     )

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

                A						:     Tuple         =model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

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

         A						:     Optional[Any]         =torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
         A						:     List[str]         =torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5     ) < 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_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6     ) < 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_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2     ) < 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 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[Any]:
         A						:     Any         =self.scheduler_classes[0]
         A						:     Dict         =self.get_scheduler_config(prediction_type='v_prediction'     )
         A						:     List[Any]         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

         scheduler.set_timesteps(self.num_inference_steps     )

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

         for i, t in enumerate(scheduler.timesteps     ):
                A						:     Dict         =scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                A						:     Tuple         =model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                A						:     Any         =scheduler.step(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                A						:     Tuple         =output.prev_sample

         A						:     Union[str, Any]         =torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
         A						:     List[Any]         =torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3     ) < 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_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3     ) < 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_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5     ) < 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 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				List[str]:
         A						:     Dict         =self.scheduler_classes[0]
         A						:     str         =self.get_scheduler_config()
         A						:     Optional[int]         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

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

         A						:     Optional[Any]         =self.dummy_model()
         A						:     List[str]         =self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__     ) * scheduler.init_noise_sigma

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

                A						:     List[Any]         =model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

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

         A						:     Union[str, Any]         =torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
         A						:     Optional[int]         =torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8     ) < 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_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2     ) < 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_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2     ) < 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 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[str]:
         A						:     List[str]         =self.scheduler_classes[0]
         A						:     List[str]         =self.get_scheduler_config()
         A						:     Optional[Any]         =scheduler_class(**SCREAMING_SNAKE_CASE__      ,	use_karras_sigmas=SCREAMING_SNAKE_CASE__     )

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

         A						:     Optional[int]         =self.dummy_model()
         A						:     Tuple         =self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__     ) * scheduler.init_noise_sigma
         A						:     str         =sample.to(SCREAMING_SNAKE_CASE__     )

         for t in scheduler.timesteps:
                A						:     Any         =scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                A						:     Union[str, Any]         =model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                A						:     Tuple         =scheduler.step(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                A						:     Optional[Any]         =output.prev_sample

         A						:     Tuple         =torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
         A						:     str         =torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8     ) < 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_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5     ) < 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_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2     ) < 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







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


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






