




'''simple docstring'''





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        UpperCamelCase_					(				SCREAMING_SNAKE_CASE_  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''tapas'''


    def __init__(    self   :			List[str]      ,				UpperCAmelCase__   :			Optional[int]=30_522      ,				UpperCAmelCase__   :			Any=768      ,				UpperCAmelCase__   :			Union[str, Any]=12      ,				UpperCAmelCase__   :			str=12      ,				UpperCAmelCase__   :			Optional[int]=3_072      ,				UpperCAmelCase__   :			str="gelu"      ,				UpperCAmelCase__   :			str=0.1      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Any=1_024      ,				UpperCAmelCase__   :			Optional[int]=[3, 256, 256, 2, 256, 256, 10]      ,				UpperCAmelCase__   :			int=0.02      ,				UpperCAmelCase__   :			List[str]=1e-12      ,				UpperCAmelCase__   :			Optional[Any]=0      ,				UpperCAmelCase__   :			str=10.0      ,				UpperCAmelCase__   :			str=0      ,				UpperCAmelCase__   :			Any=1.0      ,				UpperCAmelCase__   :			Union[str, Any]=None      ,				UpperCAmelCase__   :			Dict=1.0      ,				UpperCAmelCase__   :			Tuple=False      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Union[str, Any]=1.0      ,				UpperCAmelCase__   :			int=1.0      ,				UpperCAmelCase__   :			Any=False      ,				UpperCAmelCase__   :			List[Any]=False      ,				UpperCAmelCase__   :			Optional[Any]="ratio"      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Optional[int]=None      ,				UpperCAmelCase__   :			Optional[int]=64      ,				UpperCAmelCase__   :			Dict=32      ,				UpperCAmelCase__   :			Optional[Any]=False      ,				UpperCAmelCase__   :			str=True      ,				UpperCAmelCase__   :			int=False      ,				UpperCAmelCase__   :			Union[str, Any]=False      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=False      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			List[str]=None      ,				**UpperCAmelCase__   :			int      ,				)		->Optional[int]:





           '''simple docstring'''

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

           # 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      ,				UpperCamelCase__):
                  A__   				=					{int(UpperCamelCase__): v for k, v in aggregation_labels.items()}








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


