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


__lowerCAmelCase												=	{
    '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       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"tapas"

							def __init__(self  ,  UpperCAmelCase=30522  ,  UpperCAmelCase=768  ,  UpperCAmelCase=12  ,  UpperCAmelCase=12  ,  UpperCAmelCase=3072  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=1024  ,  UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-1_2  ,  UpperCAmelCase=0  ,  UpperCAmelCase=10.0  ,  UpperCAmelCase=0  ,  UpperCAmelCase=1.0  ,  UpperCAmelCase=None  ,  UpperCAmelCase=1.0  ,  UpperCAmelCase=False  ,  UpperCAmelCase=None  ,  UpperCAmelCase=1.0  ,  UpperCAmelCase=1.0  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  UpperCAmelCase="ratio"  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=64  ,  UpperCAmelCase=32  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  UpperCAmelCase=False  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       Tuple:
														super().__init__(pad_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )

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

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

														# Aggregation hyperparameters
														_snake_case       =  aggregation_labels
														_snake_case       =  no_aggregation_label_index

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

'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .multicontrolnet import MultiControlNetModel
				from .pipeline_controlnet import StableDiffusionControlNetPipeline
				from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
				from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
				from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline