

"""simple docstring"""






import functools
import operator

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


_lowerCAmelCase       						=		logging.get_logger(__name__)

_lowerCAmelCase       						=		{
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class 						__UpperCamelCase     (     a_    ):
  _UpperCAmelCase        =		"sew"

  def __init__(		self     ,_A=32     ,_A=768     ,_A=12     ,_A=12     ,_A=3072     ,_A=2     ,_A="gelu"     ,_A=0.1     ,_A=0.1     ,_A=0.1     ,_A=0.0     ,_A=0.1     ,_A=0.1     ,_A=0.0_2     ,_A=1E-5     ,_A="group"     ,_A="gelu"     ,_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,_A=False     ,_A=128     ,_A=16     ,_A=True     ,_A=0.0_5     ,_A=10     ,_A=2     ,_A=0.0     ,_A=10     ,_A=0     ,_A="mean"     ,_A=False     ,_A=False     ,_A=256     ,_A=0     ,_A=1     ,_A=2     ,**_A     ,):
      '''simple docstring'''

      super().__init__(**_a     ,pad_token_id=_a     ,bos_token_id=_a     ,eos_token_id=_a   )
      _lowerCAmelCase							: Union[str, Any]	=     hidden_size
      _lowerCAmelCase							: Dict	=     feat_extract_norm
      _lowerCAmelCase							: Any	=     feat_extract_activation
      _lowerCAmelCase							: List[Any]	=     list(_a   )
      _lowerCAmelCase							: Optional[int]	=     list(_a   )
      _lowerCAmelCase							: Optional[Any]	=     list(_a   )
      _lowerCAmelCase							: Dict	=     conv_bias
      _lowerCAmelCase							: List[str]	=     num_conv_pos_embeddings
      _lowerCAmelCase							: str	=     num_conv_pos_embedding_groups
      _lowerCAmelCase							: Any	=     len(self.conv_dim   )
      _lowerCAmelCase							: int	=     num_hidden_layers
      _lowerCAmelCase							: Any	=     intermediate_size
      _lowerCAmelCase							: Optional[Any]	=     squeeze_factor
      _lowerCAmelCase							: str	=     hidden_act
      _lowerCAmelCase							: Union[str, Any]	=     num_attention_heads
      _lowerCAmelCase							: Optional[Any]	=     hidden_dropout
      _lowerCAmelCase							: Union[str, Any]	=     attention_dropout
      _lowerCAmelCase							: Any	=     activation_dropout
      _lowerCAmelCase							: Union[str, Any]	=     feat_proj_dropout
      _lowerCAmelCase							: Union[str, Any]	=     final_dropout
      _lowerCAmelCase							: int	=     layerdrop
      _lowerCAmelCase							: Dict	=     layer_norm_eps
      _lowerCAmelCase							: Any	=     initializer_range
      _lowerCAmelCase							: Tuple	=     vocab_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)`,'
              F"""but is `len(config.conv_dim) = {len(self.conv_dim   )}`, `len(config.conv_stride)"""
              F"""= {len(self.conv_stride   )}`, `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

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

      # ctc loss
      _lowerCAmelCase							: str	=     ctc_loss_reduction
      _lowerCAmelCase							: Union[str, Any]	=     ctc_zero_infinity

      # sequence classification
      _lowerCAmelCase							: List[str]	=     use_weighted_layer_sum
      _lowerCAmelCase							: Optional[int]	=     classifier_proj_size







  @property
  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

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







"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(DDPMScheduler,)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Dict									=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a							)
   def      __magic_name__     (     self							):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a							)
   def      __magic_name__     (     self							):
          self.check_over_configs(thresholding=_a							)
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )
   def      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          lowercase       :							Dict									=				len(_a							)

          lowercase       :							str									=				self.dummy_model()
          lowercase       :							Optional[int]									=				self.dummy_sample_deter
          lowercase       :							List[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Optional[Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							str									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							List[str]									=				pred_prev_sample

          lowercase       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							List[Any]									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							int									=				scheduler_class(**_a							)

          lowercase       :							str									=				len(_a							)

          lowercase       :							Optional[int]									=				self.dummy_model()
          lowercase       :							List[str]									=				self.dummy_sample_deter
          lowercase       :							Optional[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							Optional[Any]									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							Dict									=				pred_prev_sample

          lowercase       :							str									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Tuple									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a							)

          lowercase       :							Union[str, Any]									=				scheduler.timesteps

          for i, timestep in enumerate(_a							):
                 if i == len(_a							) - 1:
                        lowercase       :							Any									=				-1
                 else:
                        lowercase       :							Union[str, Any]									=				timesteps[i + 1]

                 lowercase       :							Optional[int]									=				scheduler.previous_timestep(_a							)
                 lowercase       :							Union[str, Any]									=				prev_t.item()

                 self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							List[str]									=				self.get_scheduler_config()
          lowercase       :							List[Any]									=				scheduler_class(**_a							)

          lowercase       :							Optional[int]									=				[100, 87, 50, 51, 0]

          with self.assertRaises(_a      ,       msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							Any									=				scheduler_class(**_a							)

          lowercase       :							int									=				[100, 87, 50, 1, 0]
          lowercase       :							Any									=				len(_a							)

          with self.assertRaises(_a      ,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."							):
                 scheduler.set_timesteps(num_inference_steps=_a      ,       timesteps=_a							)

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Optional[int]									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _a      ,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"      ,       ):
                 scheduler.set_timesteps(timesteps=_a							)


