



from ...configuration_utils import PretrainedConfig


lowercase    : str    	=  {
    """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  __A(						__A ):
     __A	  =       "tapas"

     def __init__(      self,       A=3_0522,       A=768,       A=12,       A=12,       A=3072,       A="gelu",       A=0.1,       A=0.1,       A=1024,       A=[3, 256, 256, 2, 256, 256, 10],       A=0.02,       A=1E-1_2,       A=0,       A=10.0,       A=0,       A=1.0,       A=None,       A=1.0,       A=False,       A=None,       A=1.0,       A=1.0,       A=False,       A=False,       A="ratio",       A=None,       A=None,       A=64,       A=32,       A=False,       A=True,       A=False,       A=False,       A=True,       A=False,       A=None,       A=None,       **A,       ):





            """simple docstring"""

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

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

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

            # Aggregation hyperparameters
            _UpperCamelCase              =						aggregation_labels
            _UpperCamelCase              =						no_aggregation_label_index

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













from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class  __A(						__UpperCAmelCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						SMALL_MODEL_IDENTIFIER
            _UpperCamelCase              =						'''pt'''
            _UpperCamelCase              =						'''tf'''

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						AutoModel.from_pretrained(self.test_model      )
            model_pt.save_pretrained(A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TFAutoModel.from_pretrained(self.test_model,       from_pt=A      )
            model_tf.save_pretrained(A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''mock_framework'''

            # Framework provided - return whatever the user provides
            _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model,       A      )
            self.assertEqual(A,       A      )

            # Local checkpoint and framework provided - return provided framework
            # PyTorch checkpoint
            with TemporaryDirectory() as local_pt_ckpt:
                   self._setup_pt_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A,       A      )
                   self.assertEqual(A,       A      )

            # TensorFlow checkpoint
            with TemporaryDirectory() as local_tf_ckpt:
                   self._setup_tf_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A,       A      )
                   self.assertEqual(A,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            with TemporaryDirectory() as local_pt_ckpt:
                   self._setup_pt_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A      )
                   self.assertEqual(A,       self.framework_pt      )

            # TensorFlow checkpoint
            with TemporaryDirectory() as local_tf_ckpt:
                   self._setup_tf_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A      )
                   self.assertEqual(A,       self.framework_tf      )

            # Invalid local checkpoint
            with TemporaryDirectory() as local_invalid_ckpt:
                   with self.assertRaises(A      ):
                          _UpperCamelCase              =						FeaturesManager.determine_framework(A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_pt      )

            # PyTorch not in environment -> use TensorFlow
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_torch_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_tf      )

            # Both in environment -> use PyTorch
            _UpperCamelCase              =						MagicMock(return_value=A      )
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ), patch(
                '''transformers.onnx.features.is_torch_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_pt      )

            # Both not in environment -> raise error
            _UpperCamelCase              =						MagicMock(return_value=A      )
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ), patch(
                '''transformers.onnx.features.is_torch_available''',       A      ):
                   with self.assertRaises(A      ):
                          _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )






