'''simple docstring'''







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  int		)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Tuple						    = TapasConfig.from_json_file(UpperCAmelCase__		)
							# set absolute/relative position embeddings parameter
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														SCREAMING_SNAKE_CASE__     :Any						    = TapasForQuestionAnswering(config=UpperCAmelCase__		)
							elif task == "WTQ":
														# run_task_main.py hparams
														SCREAMING_SNAKE_CASE__     :Any						    = 4
														SCREAMING_SNAKE_CASE__     :int						    = True
														# hparam_utils.py hparams
														SCREAMING_SNAKE_CASE__     :int						    = 0.66_4694
														SCREAMING_SNAKE_CASE__     :str						    = 0.20_7951
														SCREAMING_SNAKE_CASE__     :List[Any]						    = 0.12_1194
														SCREAMING_SNAKE_CASE__     :List[Any]						    = True
														SCREAMING_SNAKE_CASE__     :int						    = True
														SCREAMING_SNAKE_CASE__     :Tuple						    = False
														SCREAMING_SNAKE_CASE__     :Tuple						    = 0.035_2513

														SCREAMING_SNAKE_CASE__     :str						    = TapasForQuestionAnswering(config=UpperCAmelCase__		)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 4
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False
														# hparam_utils.py hparams
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 36.4519
														SCREAMING_SNAKE_CASE__     :Dict						    = 0.90_3421
														SCREAMING_SNAKE_CASE__     :Any						    = 222.088
														SCREAMING_SNAKE_CASE__     :str						    = True
														SCREAMING_SNAKE_CASE__     :int						    = True
														SCREAMING_SNAKE_CASE__     :int						    = True
														SCREAMING_SNAKE_CASE__     :List[str]						    = 0.76_3141

														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = TapasForQuestionAnswering(config=UpperCAmelCase__		)
							elif task == "TABFACT":
														SCREAMING_SNAKE_CASE__     :str						    = TapasForSequenceClassification(config=UpperCAmelCase__		)
							elif task == "MLM":
														SCREAMING_SNAKE_CASE__     :Any						    = TapasForMaskedLM(config=UpperCAmelCase__		)
							elif task == "INTERMEDIATE_PRETRAINING":
														SCREAMING_SNAKE_CASE__     :str						    = TapasModel(config=UpperCAmelCase__		)
							else:
														raise ValueError(F'''Task {task} not supported.'''		)

							print(F'''Building PyTorch model from configuration: {config}'''		)
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__		)

							# Save pytorch-model (weights and configuration)
							print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
							model.save_pretrained(UpperCAmelCase__		)

							# Save tokenizer files
							print(F'''Save tokenizer files to {pytorch_dump_path}'''		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + 'vocab.txt'				,						model_max_length=5_1_2		)
							tokenizer.save_pretrained(UpperCAmelCase__		)

							print('Used relative position embeddings:'				,						model.config.reset_position_index_per_cell		)


if __name__ == "__main__":
		UpperCamelCase_      	=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
		)
		parser.add_argument(
		    '''--reset_position_index_per_cell''',
		    default=False,
		    action='''store_true''',
		    help='''Whether to use relative position embeddings or not. Defaults to True.''',
		)
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--tapas_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		UpperCamelCase_      	=	parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)














from sklearn.metrics import matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		"""
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

SCREAMING_SNAKE_CASE__   :      int				=		"""\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION			)
class     lowerCamelCase_					(   datasets.Metric			):







				def       A						(    self  ):
								"""simple docstring"""
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,       citation=_CITATION							,       inputs_description=_KWARGS_DESCRIPTION							,       features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''int32'''  ),
								            '''references''': datasets.Value('''int32'''  ),
								        }  )							,       reference_urls=[
								        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
								    ]							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								return {
								    "matthews_correlation": float(matthews_corrcoef(__lowerCAmelCase							,       __lowerCAmelCase							,       sample_weight=__lowerCAmelCase  )  ),
								}






