



'''simple docstring'''






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 							a__( lowerCamelCase__     ):
    lowercase__			=   (DDPMScheduler,)







    def       lowercase_	(				self			:  Optional[int]						,      **__snake_case			:  str     ):
      a						: Union[str, Any]					       =					{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**__snake_case     )
      return config







    def       lowercase_	(				self			:  Optional[Any]     ):
      for timesteps in [1, 5, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=__snake_case     )







    def       lowercase_	(				self			:  int     ):
      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=__snake_case						,      beta_end=__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      for variance in ["fixed_small", "fixed_large", "other"]:
        self.check_over_configs(variance_type=__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      self.check_over_configs(thresholding=__snake_case     )
      for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(
              thresholding=__snake_case						,      prediction_type=__snake_case						,      sample_max_value=__snake_case						,      )







    def       lowercase_	(				self			:  Dict     ):
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(prediction_type=__snake_case     )







    def       lowercase_	(				self			:  int     ):
      for t in [0, 5_00, 9_99]:
        self.check_over_forward(time_step=__snake_case     )







    def       lowercase_	(				self			:  Dict     ):
      a						: List[str]					       =					self.scheduler_classes[0]
      a						: str					       =					self.get_scheduler_config()
      a						: int					       =					scheduler_class(**__snake_case     )

      assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.00979     )     ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.02     )     ) < 1e-5







    def       lowercase_	(				self			:  Tuple     ):
      a						: List[str]					       =					self.scheduler_classes[0]
      a						: Tuple					       =					self.get_scheduler_config()
      a						: List[str]					       =					scheduler_class(**__snake_case     )

      a						: Optional[Any]					       =					len(__snake_case     )

      a						: Dict					       =					self.dummy_model()
      a						: List[Any]					       =					self.dummy_sample_deter
      a						: int					       =					torch.manual_seed(0     )

      for t in reversed(range(__snake_case     )     ):
        # 1. predict noise residual
        a						: Optional[int]					       =					model(__snake_case						,      __snake_case     )

        # 2. predict previous mean of sample x_t-1
        a						: Dict					       =					scheduler.step(__snake_case						,      __snake_case						,      __snake_case						,      generator=__snake_case     ).prev_sample

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

      a						: Dict					       =					torch.sum(torch.abs(__snake_case     )     )
      a						: Any					       =					torch.mean(torch.abs(__snake_case     )     )

      assert abs(result_sum.item() - 258.9606     ) < 1e-2
      assert abs(result_mean.item() - 0.3372     ) < 1e-3







    def       lowercase_	(				self			:  List[str]     ):
      a						: int					       =					self.scheduler_classes[0]
      a						: Tuple					       =					self.get_scheduler_config(prediction_type='v_prediction'     )
      a						: int					       =					scheduler_class(**__snake_case     )

      a						: Dict					       =					len(__snake_case     )

      a						: Union[str, Any]					       =					self.dummy_model()
      a						: Optional[Any]					       =					self.dummy_sample_deter
      a						: str					       =					torch.manual_seed(0     )

      for t in reversed(range(__snake_case     )     ):
        # 1. predict noise residual
        a						: List[Any]					       =					model(__snake_case						,      __snake_case     )

        # 2. predict previous mean of sample x_t-1
        a						: Tuple					       =					scheduler.step(__snake_case						,      __snake_case						,      __snake_case						,      generator=__snake_case     ).prev_sample

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

      a						: int					       =					torch.sum(torch.abs(__snake_case     )     )
      a						: Optional[int]					       =					torch.mean(torch.abs(__snake_case     )     )

      assert abs(result_sum.item() - 202.0296     ) < 1e-2
      assert abs(result_mean.item() - 0.2631     ) < 1e-3







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Any					       =					self.scheduler_classes[0]
      a						: Optional[Any]					       =					self.get_scheduler_config()
      a						: List[Any]					       =					scheduler_class(**__snake_case     )

      a						: Dict					       =					[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=__snake_case     )

      a						: Any					       =					scheduler.timesteps

      for i, timestep in enumerate(__snake_case     ):
        if i == len(__snake_case     ) - 1:
          a						: Optional[int]					       =					-1
        else:
          a						: Union[str, Any]					       =					timesteps[i + 1]

        a						: List[str]					       =					scheduler.previous_timestep(__snake_case     )
        a						: Any					       =					prev_t.item()

        self.assertEqual(__snake_case						,      __snake_case     )







    def       lowercase_	(				self			:  int     ):
      a						: Optional[int]					       =					self.scheduler_classes[0]
      a						: Tuple					       =					self.get_scheduler_config()
      a						: Optional[Any]					       =					scheduler_class(**__snake_case     )

      a						: Optional[int]					       =					[1_00, 87, 50, 51, 0]

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







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Dict					       =					self.scheduler_classes[0]
      a						: int					       =					self.get_scheduler_config()
      a						: List[Any]					       =					scheduler_class(**__snake_case     )

      a						: List[str]					       =					[1_00, 87, 50, 1, 0]
      a						: Dict					       =					len(__snake_case     )

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







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Tuple					       =					self.scheduler_classes[0]
      a						: Dict					       =					self.get_scheduler_config()
      a						: List[Any]					       =					scheduler_class(**__snake_case     )

      a						: Dict					       =					[scheduler.config.num_train_timesteps]

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




'''simple docstring'''






import torch

from transformers import AutoModel

class 							a__( torch.nn.Module     ):







    def __init__(				self			:  Any						,      __snake_case			:  str="sayef/fsner-bert-base-uncased"     ):
      super(__snake_case						,      self     ).__init__()

      a						: List[str]					       =					AutoModel.from_pretrained(__snake_case						,      return_dict=__snake_case     )
      a						: Optional[Any]					       =					torch.nn.CosineSimilarity(3						,      1e-0_8     )
      a						: Tuple					       =					torch.nn.Softmax(dim=1     )







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  int     ):
      return self.bert(**__snake_case     ).last_hidden_state







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]     ):
      return token_embeddings.sum(2						,      keepdim=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]=1     ):
      return self.softmax(T * self.cos(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: List[Any]					       =					W_supports['sizes'].tolist()
      a						: Any					       =					W_supports['start_token_id'].item()
      a						: int					       =					W_supports['end_token_id'].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      a						: Optional[Any]					       =					self.BERT(**__snake_case     )
      a						: Tuple					       =					self.BERT(**__snake_case     )

      a						: Dict					       =					None
      a						: Optional[Any]					       =					None

      a						: Union[str, Any]					       =					W_supports['input_ids'] == start_token_id
      a						: str					       =					W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__snake_case     ):
        if i == 0:
          a						: Optional[int]					       =					0
        else:
          a						: Tuple					       =					support_sizes[i - 1]

        a						: Tuple					       =					S[s : s + size][start_token_masks[s : s + size]]
        a						: int					       =					S[s : s + size][end_token_masks[s : s + size]]

        a						: Any					       =					torch.matmul(q[i]						,      s_start.T     ).sum(1     ).softmax(0     )
        a						: List[str]					       =					torch.matmul(q[i]						,      s_end.T     ).sum(1     ).softmax(0     )

        if p_starts is not None:
          a						: List[Any]					       =					torch.vstack((p_starts, p_start)     )
          a						: List[Any]					       =					torch.vstack((p_ends, p_end)     )
        else:
          a						: List[Any]					       =					p_start
          a						: Optional[int]					       =					p_end

      return p_starts, p_ends