




from __future__ import annotations

from pathlib import Path
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			return pointa[0] * pointa[1] - pointa[1] * pointa[0]
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       (xa, ya)
			SCREAMING_SNAKE_CASE     =       (xa - xa, ya - ya)
			SCREAMING_SNAKE_CASE     =       (xa - xa, ya - ya)
			SCREAMING_SNAKE_CASE     =       -vector_product(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ) / vector_product(
			    SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       +vector_product(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ) / vector_product(
			    SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

			return a > 0 and b > 0 and a + b < 1
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE = "p102_triangles.txt" ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       Path(SCREAMING_SNAKE_CASE ).parent.joinpath(SCREAMING_SNAKE_CASE ).read_text(encoding="""utf-8""" )

			SCREAMING_SNAKE_CASE     =       []
			for line in data.strip().split("""\n""" ):
						triangles.append([int(SCREAMING_SNAKE_CASE ) for number in line.split(""",""" )] )

			SCREAMING_SNAKE_CASE     =       0
			SCREAMING_SNAKE_CASE     =       42

			for triangle in triangles:
						ret += contains_origin(*SCREAMING_SNAKE_CASE )

			return ret


if __name__ == "__main__":
		print(f'''{solution() = }''')












import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				str    						=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--convbert_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained ConvBERT 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."""
		)
		__A    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






