





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:List[Any]						    =   (DDIMParallelScheduler,)
      _UpperCAmelCase	:Any						    =   (('eta', 0.0), ('num_inference_steps', 5_0))





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   {
           "num_train_timesteps": 1000,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "clip_sample": True,
       }

       config.update(**A_   )
       return config





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.scheduler_classes[0]
       UpperCamelCase	:					List[Any]       	=   self.get_scheduler_config(**A_   )
       UpperCamelCase	:					Dict       	=   scheduler_class(**A_   )

       UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   10, 0.0

       UpperCamelCase	:					List[Any]       	=   self.dummy_model()
       UpperCamelCase	:					Union[str, Any]       	=   self.dummy_sample_deter

       scheduler.set_timesteps(A_   )

       for t in scheduler.timesteps:
        UpperCamelCase	:					Any       	=   model(A_       ,							A_   )
        UpperCamelCase	:					Any       	=   scheduler.step(A_       ,							A_       ,							A_       ,							A_   ).prev_sample

       return sample





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=A_   )

       UpperCamelCase	:					Optional[Any]       	=   self.scheduler_classes[0]
       UpperCamelCase	:					Dict       	=   self.get_scheduler_config(steps_offset=1   )
       UpperCamelCase	:					List[str]       	=   scheduler_class(**A_   )
       scheduler.set_timesteps(5   )
       assert torch.equal(scheduler.timesteps       ,							torch.LongTensor([801, 601, 401, 201, 1]   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,							[0.0_02, 0.02, 0.2, 2]   ):
        self.check_over_configs(beta_start=A_       ,							beta_end=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.check_over_configs(thresholding=A_   )
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=A_       ,							prediction_type=A_       ,							sample_max_value=A_       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for t in [1, 10, 49]:
        self.check_over_forward(time_step=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for t, num_inference_steps in zip([1, 10, 50]       ,							[10, 50, 500]   ):
        self.check_over_forward(time_step=A_       ,							num_inference_steps=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for t, eta in zip([1, 10, 49]       ,							[0.0, 0.5, 1.0]   ):
        self.check_over_forward(time_step=A_       ,							eta=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.scheduler_classes[0]
       UpperCamelCase	:					Union[str, Any]       	=   self.get_scheduler_config()
       UpperCamelCase	:					List[Any]       	=   scheduler_class(**A_   )

       assert torch.sum(torch.abs(scheduler._get_variance(0       ,							0   ) - 0.0   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420       ,							400   ) - 0.1_47_71   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980       ,							960   ) - 0.3_24_60   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0       ,							0   ) - 0.0   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487       ,							486   ) - 0.0_09_79   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999       ,							998   ) - 0.02   )   ) < 1e-5





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   10, 0.0
       scheduler.set_timesteps(A_   )

       UpperCamelCase	:					Tuple       	=   self.dummy_model()
       UpperCamelCase	:					List[str]       	=   self.dummy_sample_deter
       UpperCamelCase	:					Optional[int]       	=   self.dummy_sample_deter + 0.1
       UpperCamelCase	:					Optional[int]       	=   self.dummy_sample_deter - 0.1

       UpperCamelCase	:					Optional[Any]       	=   samplea.shape[0]
       UpperCamelCase	:					Dict       	=   torch.stack([samplea, samplea, samplea]       ,							dim=0   )
       UpperCamelCase	:					int       	=   torch.arange(A_   )[0:3, None].repeat(1       ,							A_   )

       UpperCamelCase	:					Tuple       	=   model(samples.flatten(0       ,							1   )       ,							timesteps.flatten(0       ,							1   )   )
       UpperCamelCase	:					Optional[int]       	=   scheduler.batch_step_no_noise(A_       ,							timesteps.flatten(0       ,							1   )       ,							samples.flatten(0       ,							1   )       ,							A_   )

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

       assert abs(result_sum.item() - 11_47.79_04   ) < 1e-2
       assert abs(result_mean.item() - 0.49_82   ) < 1e-3





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.full_loop()

       UpperCamelCase	:					Dict       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					Dict       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 1_72.00_67   ) < 1e-2
       assert abs(result_mean.item() - 0.22_39_67   ) < 1e-3





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.full_loop(prediction_type="v_prediction"   )

       UpperCamelCase	:					List[str]       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					Optional[int]       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 52.53_02   ) < 1e-2
       assert abs(result_mean.item() - 0.06_84   ) < 1e-3





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.full_loop(set_alpha_to_one=A_       ,							beta_start=0.01   )
       UpperCamelCase	:					Dict       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					Union[str, Any]       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 1_49.82_95   ) < 1e-2
       assert abs(result_mean.item() - 0.19_51   ) < 1e-3





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.full_loop(set_alpha_to_one=A_       ,							beta_start=0.01   )
       UpperCamelCase	:					Union[str, Any]       	=   torch.sum(torch.abs(A_   )   )
       UpperCamelCase	:					Dict       	=   torch.mean(torch.abs(A_   )   )

       assert abs(result_sum.item() - 1_49.07_84   ) < 1e-2
       assert abs(result_mean.item() - 0.19_41   ) < 1e-3




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


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      ='transfo-xl'
      UpperCamelCase__							:  int      =['mems']
      UpperCamelCase__							:  List[str]      ={
          'n_token': 'vocab_size',
          'hidden_size': 'd_model',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(					self						:     Tuple			,   lowercase_						:     List[Any]=26_7735			,   lowercase_						:     List[Any]=[2_0000, 4_0000, 20_0000]			,   lowercase_						:     Tuple=1024			,   lowercase_						:     Optional[int]=1024			,   lowercase_						:     str=16			,   lowercase_						:     int=64			,   lowercase_						:     Any=4096			,   lowercase_						:     List[Any]=4			,   lowercase_						:     List[Any]=False			,   lowercase_						:     List[str]=18			,   lowercase_						:     Tuple=1600			,   lowercase_						:     List[str]=1000			,   lowercase_						:     Any=True			,   lowercase_						:     str=True			,   lowercase_						:     Optional[int]=0			,   lowercase_						:     List[Any]=-1			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Any=0.1			,   lowercase_						:     Tuple=0.0			,   lowercase_						:     str=True			,   lowercase_						:     Union[str, Any]="normal"			,   lowercase_						:     Dict=0.01			,   lowercase_						:     Optional[Any]=0.01			,   lowercase_						:     List[Any]=0.02			,   lowercase_						:     List[Any]=1E-5			,   lowercase_						:     Tuple=0			,   **lowercase_						:     Tuple			,   )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=vocab_size
             _lowerCamelCase       :		Optional[Any]		=[]
             self.cutoffs.extend(lowercase_ )
             if proj_share_all_but_first:
                    _lowerCamelCase       :		List[str]		=[False] + [True] * len(self.cutoffs )
             else:
                    _lowerCamelCase       :		str		=[False] + [False] * len(self.cutoffs )
             _lowerCamelCase       :		str		=d_model
             _lowerCamelCase       :		str		=d_embed
             _lowerCamelCase       :		str		=d_head
             _lowerCamelCase       :		Dict		=d_inner
             _lowerCamelCase       :		Union[str, Any]		=div_val
             _lowerCamelCase       :		Dict		=pre_lnorm
             _lowerCamelCase       :		Tuple		=n_layer
             _lowerCamelCase       :		Optional[Any]		=n_head
             _lowerCamelCase       :		List[Any]		=mem_len
             _lowerCamelCase       :		List[Any]		=same_length
             _lowerCamelCase       :		Optional[int]		=attn_type
             _lowerCamelCase       :		List[str]		=clamp_len
             _lowerCamelCase       :		int		=sample_softmax
             _lowerCamelCase       :		List[Any]		=adaptive
             _lowerCamelCase       :		List[str]		=dropout
             _lowerCamelCase       :		Any		=dropatt
             _lowerCamelCase       :		Dict		=untie_r
             _lowerCamelCase       :		List[Any]		=init
             _lowerCamelCase       :		List[str]		=init_range
             _lowerCamelCase       :		int		=proj_init_std
             _lowerCamelCase       :		Optional[int]		=init_std
             _lowerCamelCase       :		Tuple		=layer_norm_epsilon
             super().__init__(eos_token_id=lowercase_			,   **lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
             return -1






      @max_position_embeddings.setter
      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     int )					->						List[str]:
             """simple docstring"""




             raise NotImplementedError(
                 F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


