"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_     ):





		"""simple docstring"""




		return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_=0     ):





		"""simple docstring"""




		return sorted(a_			,  key=lambda lowerCAmelCase_     : x[column]     )







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=float("""inf"""     )     ):





		"""simple docstring"""




		for i in range(points_counts - 1     ):
				for j in range(i + 1			,  a_     ):
						lowerCAmelCase__        =   euclidean_distance_sqr(points[i]			,  points[j]     )
						if current_dis < min_dis:
								lowerCAmelCase__        =   current_dis
		return min_dis







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=float("""inf"""     )     ):





		"""simple docstring"""




		for i in range(min(6			,  points_counts - 1     )			,  a_     ):
				for j in range(max(0			,  i - 6     )			,  a_     ):
						lowerCAmelCase__        =   euclidean_distance_sqr(points[i]			,  points[j]     )
						if current_dis < min_dis:
								lowerCAmelCase__        =   current_dis
		return min_dis







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_     ):





		"""simple docstring"""




		if points_counts <= 3:
				return dis_between_closest_pair(a_			,  a_     )

		# recursion
		lowerCAmelCase__        =   points_counts // 2
		lowerCAmelCase__        =   closest_pair_of_points_sqr(
		    a_			,  points_sorted_on_y[:mid]			,  a_     )
		lowerCAmelCase__        =   closest_pair_of_points_sqr(
		    a_			,  points_sorted_on_y[mid:]			,  points_counts - mid     )
		lowerCAmelCase__        =   min(a_			,  a_     )
		lowerCAmelCase__        =   []
		for point in points_sorted_on_x:
				if abs(point[0] - points_sorted_on_x[mid][0]     ) < closest_pair_dis:
						cross_strip.append(a_     )

		lowerCAmelCase__        =   dis_between_closest_in_strip(
		    a_			,  len(a_     )			,  a_     )
		return min(a_			,  a_     )







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_     ):





		"""simple docstring"""




		lowerCAmelCase__        =   column_based_sort(a_			,  column=0     )
		lowerCAmelCase__        =   column_based_sort(a_			,  column=1     )
		return (
		    closest_pair_of_points_sqr(
		        a_			,  a_			,  a_     )
		) ** 0.5


if __name__ == "__main__":
			__lowerCAmelCase    :							Optional[int]													=						[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
			print("Distance:", closest_pair_of_points(points, len(points)))





"""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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__        =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   True
				# hparam_utils.py hparams
				lowerCAmelCase__        =   0.66_46_94
				lowerCAmelCase__        =   0.20_79_51
				lowerCAmelCase__        =   0.12_11_94
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   False
				lowerCAmelCase__        =   0.0_35_25_13

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   False
				# hparam_utils.py hparams
				lowerCAmelCase__        =   36.45_19
				lowerCAmelCase__        =   0.90_34_21
				lowerCAmelCase__        =   2_22.0_88
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   0.76_31_41

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   TapasModel(config=lowerCamelCase__     )
		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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

		# Save pytorch-model (weights and configuration)
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(lowerCamelCase__     )

		# Save tokenizer files
		print(f"""Save tokenizer files to {pytorch_dump_path}"""     )
		lowerCAmelCase__        =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""			,  model_max_length=512     )
		tokenizer.save_pretrained(lowerCamelCase__     )

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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						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,
			)




