
from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE				:Optional[int]									=							{
    '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 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"tapas"






	def __init__(			self   :					List[Any]				,A   :					Dict=3_05_22				,A   :					int=7_68				,A   :					List[Any]=12				,A   :					Any=12				,A   :					str=30_72				,A   :					Dict="gelu"				,A   :					str=0.1				,A   :					Any=0.1				,A   :					Union[str, Any]=10_24				,A   :					Dict=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]				,A   :					str=0.02				,A   :					List[str]=1E-12				,A   :					List[Any]=0				,A   :					str=10.0				,A   :					Any=0				,A   :					Optional[int]=1.0				,A   :					Optional[Any]=None				,A   :					Any=1.0				,A   :					int=False				,A   :					str=None				,A   :					str=1.0				,A   :					Optional[Any]=1.0				,A   :					List[Any]=False				,A   :					Any=False				,A   :					Tuple="ratio"				,A   :					Optional[int]=None				,A   :					Any=None				,A   :					Optional[int]=64				,A   :					List[Any]=32				,A   :					int=False				,A   :					List[Any]=True				,A   :					str=False				,A   :					Optional[int]=False				,A   :					Any=True				,A   :					Tuple=False				,A   :					Tuple=None				,A   :					List[str]=None				,**A   :					Union[str, Any]				,):
					super().__init__(pad_token_id=A				,**A					)

					# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
					__A       =					vocab_size
					__A       =					hidden_size
					__A       =					num_hidden_layers
					__A       =					num_attention_heads
					__A       =					hidden_act
					__A       =					intermediate_size
					__A       =					hidden_dropout_prob
					__A       =					attention_probs_dropout_prob
					__A       =					max_position_embeddings
					__A       =					type_vocab_sizes
					__A       =					initializer_range
					__A       =					layer_norm_eps

					# Fine-tuning task hyperparameters
					__A       =					positive_label_weight
					__A       =					num_aggregation_labels
					__A       =					aggregation_loss_weight
					__A       =					use_answer_as_supervision
					__A       =					answer_loss_importance
					__A       =					use_normalized_answer_loss
					__A       =					huber_loss_delta
					__A       =					temperature
					__A       =					aggregation_temperature
					__A       =					use_gumbel_for_cells
					__A       =					use_gumbel_for_aggregation
					__A       =					average_approximation_function
					__A       =					cell_selection_preference
					__A       =					answer_loss_cutoff
					__A       =					max_num_rows
					__A       =					max_num_columns
					__A       =					average_logits_per_cell
					__A       =					select_one_column
					__A       =					allow_empty_column_selection
					__A       =					init_cell_selection_weights_to_zero
					__A       =					reset_position_index_per_cell
					__A       =					disable_per_token_loss

					# Aggregation hyperparameters
					__A       =					aggregation_labels
					__A       =					no_aggregation_label_index

					if isinstance(self.aggregation_labels				,A					):
									__A       =					{int(A					): v for k, v in aggregation_labels.items()}





'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

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


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

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




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[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_			)




