

'''simple docstring'''
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        a__  (							snake_case__  ):
   _SCREAMING_SNAKE_CASE   :					int     	=							(DDPMScheduler,)


   def 						_lowerCamelCase       (			self      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       {
          "num_train_timesteps": 1000,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "variance_type": "fixed_small",
          "clip_sample": True,
      }

      config.update(**UpperCAmelCase_  )
      return config


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=UpperCAmelCase_  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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=UpperCAmelCase_      ,    beta_end=UpperCAmelCase_  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=UpperCAmelCase_  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for t in [0, 500, 999]:
         self.check_over_forward(time_step=UpperCAmelCase_  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.scheduler_classes[0]
      _lowercase      :				List[str]      					=       self.get_scheduler_config()
      _lowercase      :				Union[str, Any]      					=       scheduler_class(**UpperCAmelCase_  )

      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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       self.scheduler_classes[0]
      _lowercase      :				Dict      					=       self.get_scheduler_config()
      _lowercase      :				Optional[int]      					=       scheduler_class(**UpperCAmelCase_  )

      _lowercase      :				List[Any]      					=       len(UpperCAmelCase_  )

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

      for t in reversed(range(UpperCAmelCase_  )  ):
         # 1. predict noise residual
         _lowercase      :				Any      					=       model(UpperCAmelCase_      ,    UpperCAmelCase_  )

         # 2. predict previous mean of sample x_t-1
         _lowercase      :				Union[str, Any]      					=       scheduler.step(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    generator=UpperCAmelCase_  ).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[Any]      					=       pred_prev_sample

      _lowercase      :				Dict      					=       torch.sum(torch.abs(UpperCAmelCase_  )  )
      _lowercase      :				List[Any]      					=       torch.mean(torch.abs(UpperCAmelCase_  )  )

      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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       self.scheduler_classes[0]
      _lowercase      :				Optional[int]      					=       self.get_scheduler_config(prediction_type="v_prediction"  )
      _lowercase      :				Optional[int]      					=       scheduler_class(**UpperCAmelCase_  )

      _lowercase      :				List[Any]      					=       len(UpperCAmelCase_  )

      _lowercase      :				Dict      					=       self.dummy_model()
      _lowercase      :				Any      					=       self.dummy_sample_deter
      _lowercase      :				str      					=       torch.manual_seed(0  )

      for t in reversed(range(UpperCAmelCase_  )  ):
         # 1. predict noise residual
         _lowercase      :				Tuple      					=       model(UpperCAmelCase_      ,    UpperCAmelCase_  )

         # 2. predict previous mean of sample x_t-1
         _lowercase      :				Optional[int]      					=       scheduler.step(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    generator=UpperCAmelCase_  ).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         _lowercase      :				Optional[int]      					=       pred_prev_sample

      _lowercase      :				str      					=       torch.sum(torch.abs(UpperCAmelCase_  )  )
      _lowercase      :				Optional[int]      					=       torch.mean(torch.abs(UpperCAmelCase_  )  )

      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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       self.scheduler_classes[0]
      _lowercase      :				Any      					=       self.get_scheduler_config()
      _lowercase      :				Optional[int]      					=       scheduler_class(**UpperCAmelCase_  )

      _lowercase      :				Union[str, Any]      					=       [100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=UpperCAmelCase_  )

      _lowercase      :				List[Any]      					=       scheduler.timesteps

      for i, timestep in enumerate(UpperCAmelCase_  ):
         if i == len(UpperCAmelCase_  ) - 1:
            _lowercase      :				str      					=       -1
         else:
            _lowercase      :				List[Any]      					=       timesteps[i + 1]

         _lowercase      :				int      					=       scheduler.previous_timestep(UpperCAmelCase_  )
         _lowercase      :				Dict      					=       prev_t.item()

         self.assertEqual(UpperCAmelCase_      ,    UpperCAmelCase_  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       self.scheduler_classes[0]
      _lowercase      :				Optional[Any]      					=       self.get_scheduler_config()
      _lowercase      :				Tuple      					=       scheduler_class(**UpperCAmelCase_  )

      _lowercase      :				Dict      					=       [100, 87, 50, 51, 0]

      with self.assertRaises(UpperCAmelCase_      ,    msg="`custom_timesteps` must be in descending order."  ):
         scheduler.set_timesteps(timesteps=UpperCAmelCase_  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.scheduler_classes[0]
      _lowercase      :				int      					=       self.get_scheduler_config()
      _lowercase      :				Optional[Any]      					=       scheduler_class(**UpperCAmelCase_  )

      _lowercase      :				Optional[int]      					=       [100, 87, 50, 1, 0]
      _lowercase      :				Optional[Any]      					=       len(UpperCAmelCase_  )

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






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       self.scheduler_classes[0]
      _lowercase      :				Union[str, Any]      					=       self.get_scheduler_config()
      _lowercase      :				Dict      					=       scheduler_class(**UpperCAmelCase_  )

      _lowercase      :				Any      					=       [scheduler.config.num_train_timesteps]

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









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'roc_bert'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=768      ,    _UpperCamelCase=12      ,    _UpperCamelCase=12      ,    _UpperCamelCase=3072      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-1_2      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0      ,    _UpperCamelCase="absolute"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=768      ,    _UpperCamelCase=910      ,    _UpperCamelCase=512      ,    _UpperCamelCase=24858      ,    _UpperCamelCase=True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       vocab_size
      _lowercase      :				List[str]      					=       max_position_embeddings
      _lowercase      :				List[Any]      					=       hidden_size
      _lowercase      :				Dict      					=       num_hidden_layers
      _lowercase      :				str      					=       num_attention_heads
      _lowercase      :				int      					=       intermediate_size
      _lowercase      :				Optional[Any]      					=       hidden_act
      _lowercase      :				Union[str, Any]      					=       hidden_dropout_prob
      _lowercase      :				Dict      					=       attention_probs_dropout_prob
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				List[Any]      					=       type_vocab_size
      _lowercase      :				Tuple      					=       layer_norm_eps
      _lowercase      :				Optional[int]      					=       use_cache
      _lowercase      :				Tuple      					=       enable_pronunciation
      _lowercase      :				Optional[int]      					=       enable_shape
      _lowercase      :				int      					=       pronunciation_embed_dim
      _lowercase      :				List[str]      					=       pronunciation_vocab_size
      _lowercase      :				int      					=       shape_embed_dim
      _lowercase      :				str      					=       shape_vocab_size
      _lowercase      :				str      					=       concat_input
      _lowercase      :				Dict      					=       position_embedding_type
      _lowercase      :				Optional[Any]      					=       classifier_dropout
      super().__init__(pad_token_id=_UpperCamelCase      ,    **_UpperCamelCase  )






