




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (DDIMParallelScheduler,)
    lowerCAmelCase         = (('''eta''', 0.0), ('''num_inference_steps''', 50))





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_     :				Dict  =     {
             '''num_train_timesteps''': 1_000,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''clip_sample''': True,
         }

         config.update(**_SCREAMING_SNAKE_CASE    )
         return config





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         UpperCAmelCase_     :				List[str]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				List[Any]  =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     10, 0.0

         UpperCAmelCase_     :				List[str]  =     self.dummy_model()
         UpperCAmelCase_     :				List[str]  =     self.dummy_sample_deter

         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

         for t in scheduler.timesteps:
              UpperCAmelCase_     :				Tuple  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				int  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample

         return sample





    def 				a__ ( self    )					->						Optional[int]:
         for timesteps in [100, 500, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE    )

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





    def 				a__ ( self    )					->						Optional[int]:
         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=_SCREAMING_SNAKE_CASE  ,beta_end=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						int:
         for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Dict:
         self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=_SCREAMING_SNAKE_CASE  ,prediction_type=_SCREAMING_SNAKE_CASE  ,sample_max_value=_SCREAMING_SNAKE_CASE  ,)





    def 				a__ ( self    )					->						str:
         for t in [1, 10, 49]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						int:
         for t, num_inference_steps in zip([1, 10, 50]  ,[10, 50, 500]    ):
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE  ,num_inference_steps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[Any]:
         for t, eta in zip([1, 10, 49]  ,[0.0, 0.5, 1.0]    ):
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE  ,eta=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Union[str, Any]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				str  =     self.get_scheduler_config()
         UpperCAmelCase_     :				int  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         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 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Optional[Any]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Optional[Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Union[str, Any]  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     10, 0.0
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     self.dummy_model()
         UpperCAmelCase_     :				Optional[Any]  =     self.dummy_sample_deter
         UpperCAmelCase_     :				Dict  =     self.dummy_sample_deter + 0.1
         UpperCAmelCase_     :				Union[str, Any]  =     self.dummy_sample_deter - 0.1

         UpperCAmelCase_     :				Optional[Any]  =     samplea.shape[0]
         UpperCAmelCase_     :				Any  =     torch.stack([samplea, samplea, samplea]  ,dim=0    )
         UpperCAmelCase_     :				Optional[Any]  =     torch.arange(_SCREAMING_SNAKE_CASE    )[0:3, None].repeat(1  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     model(samples.flatten(0  ,1    )  ,timesteps.flatten(0  ,1    )    )
         UpperCAmelCase_     :				Optional[Any]  =     scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE  ,timesteps.flatten(0  ,1    )  ,samples.flatten(0  ,1    )  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Dict  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				List[str]  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				str  =     self.full_loop()

         UpperCAmelCase_     :				Optional[Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Tuple  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				List[Any]  =     self.full_loop(prediction_type='''v_prediction'''    )

         UpperCAmelCase_     :				Union[str, Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Any  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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





    def 				a__ ( self    )					->						Union[str, Any]:
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_     :				List[Any]  =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE  ,beta_start=0.01    )
         UpperCAmelCase_     :				Optional[Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				int  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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





    def 				a__ ( self    )					->						str:
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_     :				List[Any]  =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE  ,beta_start=0.01    )
         UpperCAmelCase_     :				Union[str, Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Any  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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





"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

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

