

'''simple docstring'''






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							(DDIMScheduler,)
    _A         =							(("eta", 0.0), ("num_inference_steps", 50))




    def   __lowerCamelCase				( self    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	{
              "num_train_timesteps": 1000,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**lowercase__      )
          return config




    def   __lowerCamelCase				( self    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.get_scheduler_config(**lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	scheduler_class(**lowercase__      )

          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      List[str]					=	10, 0.0

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.dummy_model()
          SCREAMING_SNAKE_CASE_				:      Tuple					=	self.dummy_sample_deter

          scheduler.set_timesteps(lowercase__      )

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	model(lowercase__    ,					lowercase__      )
                SCREAMING_SNAKE_CASE_				:      Dict					=	scheduler.step(lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__      ).prev_sample

          return sample




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,					[0.002, 0.02, 0.2, 2]      ):
                self.check_over_configs(beta_start=lowercase__    ,					beta_end=lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0      ) - 0.0      )      ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420    ,					400      ) - 0.14771      )      ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980    ,					960      ) - 0.32460      )      ) < 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      ) - 0.00979      )      ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999    ,					998      ) - 0.02      )      ) < 1e-5




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	self.full_loop()

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	torch.sum(torch.abs(lowercase__      )      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	torch.mean(torch.abs(lowercase__      )      )

          assert abs(result_sum.item() - 172.0067      ) < 1e-2
          assert abs(result_mean.item() - 0.223967      ) < 1e-3




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	self.full_loop(prediction_type="v_prediction"      )

          SCREAMING_SNAKE_CASE_				:      Tuple					=	torch.sum(torch.abs(lowercase__      )      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	torch.mean(torch.abs(lowercase__      )      )

          assert abs(result_sum.item() - 52.5302      ) < 1e-2
          assert abs(result_mean.item() - 0.0684      ) < 1e-3




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	self.full_loop(set_alpha_to_one=lowercase__    ,					beta_start=0.01      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	torch.sum(torch.abs(lowercase__      )      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	torch.mean(torch.abs(lowercase__      )      )

          assert abs(result_sum.item() - 149.8295      ) < 1e-2
          assert abs(result_mean.item() - 0.1951      ) < 1e-3







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	self.full_loop(set_alpha_to_one=lowercase__    ,					beta_start=0.01      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	torch.sum(torch.abs(lowercase__      )      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	torch.mean(torch.abs(lowercase__      )      )

          assert abs(result_sum.item() - 149.0784      ) < 1e-2
          assert abs(result_mean.item() - 0.1941      ) < 1e-3




'''simple docstring'''






import functools
import operator

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


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							"hubert"




    def __init__( self    ,					lowercase__=32    ,					lowercase__=768    ,					lowercase__=12    ,					lowercase__=12    ,					lowercase__=3072    ,					lowercase__="gelu"    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=True    ,					lowercase__=0.0    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.02    ,					lowercase__=1e-5    ,					lowercase__="group"    ,					lowercase__="gelu"    ,					lowercase__=(512, 512, 512, 512, 512, 512, 512)    ,					lowercase__=(5, 2, 2, 2, 2, 2, 2)    ,					lowercase__=(10, 3, 3, 3, 3, 2, 2)    ,					lowercase__=False    ,					lowercase__=128    ,					lowercase__=16    ,					lowercase__=False    ,					lowercase__=True    ,					lowercase__=0.05    ,					lowercase__=10    ,					lowercase__=2    ,					lowercase__=0.0    ,					lowercase__=10    ,					lowercase__=0    ,					lowercase__="sum"    ,					lowercase__=False    ,					lowercase__=False    ,					lowercase__=256    ,					lowercase__=0    ,					lowercase__=1    ,					lowercase__=2    ,					**lowercase__    ,					):






          """simple docstring"""






          super().__init__(**lowercase__    ,					pad_token_id=lowercase__    ,					bos_token_id=lowercase__    ,					eos_token_id=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	hidden_size
          SCREAMING_SNAKE_CASE_				:      List[str]					=	feat_extract_norm
          SCREAMING_SNAKE_CASE_				:      Dict					=	feat_extract_activation
          SCREAMING_SNAKE_CASE_				:      str					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      int					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	conv_bias
          SCREAMING_SNAKE_CASE_				:      str					=	num_conv_pos_embeddings
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	num_conv_pos_embedding_groups
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	len(self.conv_dim      )
          SCREAMING_SNAKE_CASE_				:      Any					=	num_hidden_layers
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	intermediate_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	hidden_act
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	num_attention_heads
          SCREAMING_SNAKE_CASE_				:      Any					=	hidden_dropout
          SCREAMING_SNAKE_CASE_				:      List[str]					=	attention_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	activation_dropout
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	feat_proj_layer_norm
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	feat_proj_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	final_dropout
          SCREAMING_SNAKE_CASE_				:      Dict					=	layerdrop
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	layer_norm_eps
          SCREAMING_SNAKE_CASE_				:      Tuple					=	initializer_range
          SCREAMING_SNAKE_CASE_				:      List[str]					=	vocab_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	do_stable_layer_norm
          SCREAMING_SNAKE_CASE_				:      List[str]					=	use_weighted_layer_sum
          SCREAMING_SNAKE_CASE_				:      Any					=	classifier_proj_size

          if (
              (len(self.conv_stride      ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
              or (len(self.conv_dim      ) != self.num_feat_extract_layers)
          ):
                raise ValueError(
                    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                    F" {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,"
                    F" `len(config.conv_kernel) = {len(self.conv_kernel      )}`."      )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          SCREAMING_SNAKE_CASE_				:      Tuple					=	apply_spec_augment
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	mask_time_prob
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	mask_time_length
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	mask_time_min_masks
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	mask_feature_prob
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	mask_feature_length
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	mask_feature_min_masks

          # ctc loss
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_loss_reduction
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_zero_infinity







    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return functools.reduce(operator.mul    ,					self.conv_stride    ,					1      )

