
from ...configuration_utils import PretrainedConfig


lowerCamelCase__     :		List[Any]        = {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[str]	   =      'tapas'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=3_05_22       ,	SCREAMING_SNAKE_CASE_=7_68       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=30_72       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=10_24       ,	SCREAMING_SNAKE_CASE_=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=1E-12       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=1_0.0       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=1.0       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=1.0       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=1.0       ,	SCREAMING_SNAKE_CASE_=1.0       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_="ratio"       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=64       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
   lowercase__	: Tuple     		=   vocab_size
   lowercase__	: str     		=   hidden_size
   lowercase__	: List[Any]     		=   num_hidden_layers
   lowercase__	: Any     		=   num_attention_heads
   lowercase__	: List[Any]     		=   hidden_act
   lowercase__	: Optional[Any]     		=   intermediate_size
   lowercase__	: List[Any]     		=   hidden_dropout_prob
   lowercase__	: List[Any]     		=   attention_probs_dropout_prob
   lowercase__	: List[Any]     		=   max_position_embeddings
   lowercase__	: List[str]     		=   type_vocab_sizes
   lowercase__	: Tuple     		=   initializer_range
   lowercase__	: Optional[Any]     		=   layer_norm_eps

   # Fine-tuning task hyperparameters
   lowercase__	: Union[str, Any]     		=   positive_label_weight
   lowercase__	: Optional[Any]     		=   num_aggregation_labels
   lowercase__	: str     		=   aggregation_loss_weight
   lowercase__	: str     		=   use_answer_as_supervision
   lowercase__	: Union[str, Any]     		=   answer_loss_importance
   lowercase__	: Union[str, Any]     		=   use_normalized_answer_loss
   lowercase__	: str     		=   huber_loss_delta
   lowercase__	: Tuple     		=   temperature
   lowercase__	: Any     		=   aggregation_temperature
   lowercase__	: List[Any]     		=   use_gumbel_for_cells
   lowercase__	: List[str]     		=   use_gumbel_for_aggregation
   lowercase__	: Dict     		=   average_approximation_function
   lowercase__	: str     		=   cell_selection_preference
   lowercase__	: List[Any]     		=   answer_loss_cutoff
   lowercase__	: Optional[Any]     		=   max_num_rows
   lowercase__	: str     		=   max_num_columns
   lowercase__	: Optional[Any]     		=   average_logits_per_cell
   lowercase__	: Tuple     		=   select_one_column
   lowercase__	: str     		=   allow_empty_column_selection
   lowercase__	: Optional[Any]     		=   init_cell_selection_weights_to_zero
   lowercase__	: Dict     		=   reset_position_index_per_cell
   lowercase__	: str     		=   disable_per_token_loss

   # Aggregation hyperparameters
   lowercase__	: Union[str, Any]     		=   aggregation_labels
   lowercase__	: Optional[Any]     		=   no_aggregation_label_index

   if isinstance(self.aggregation_labels       ,	SCREAMING_SNAKE_CASE_):
     lowercase__	: Any     		=   {int(SCREAMING_SNAKE_CASE_): v for k, v in aggregation_labels.items()}








import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      (DDPMScheduler,)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       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=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_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) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: int     		=   len(SCREAMING_SNAKE_CASE_)

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

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: List[str]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_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
     lowercase__	: str     		=   pred_prev_sample

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

   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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.dummy_model()
   lowercase__	: Union[str, Any]     		=   self.dummy_sample_deter
   lowercase__	: int     		=   torch.manual_seed(0)

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

     # 2. predict previous mean of sample x_t-1
     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_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
     lowercase__	: Tuple     		=   pred_prev_sample

   lowercase__	: Union[str, Any]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: int     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   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   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: List[str]     		=   [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__	: Optional[int]     		=   -1
     else:
       lowercase__	: Tuple     		=   timesteps[i + 1]

     lowercase__	: Any     		=   scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: List[Any]     		=   [1_00, 87, 50, 51, 0]

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


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: int     		=   [1_00, 87, 50, 1, 0]
   lowercase__	: Union[str, Any]     		=   len(SCREAMING_SNAKE_CASE_)

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



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [scheduler.config.num_train_timesteps]

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






