





from __future__ import annotations

from pathlib import Path


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  int:
		return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  bool:
		lowerCamelCase :			tuple[int, int]    		=(xa, ya)
		lowerCamelCase :			tuple[int, int]    		=(xa - xa, ya - ya)
		lowerCamelCase :			tuple[int, int]    		=(xa - xa, ya - ya)
		lowerCamelCase :			float    		=-vector_product(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) / vector_product(
		    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			float    		=+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        A__						(							SCREAMING_SNAKE_CASE_ = "p102_triangles.txt"		) ->  int:
		lowerCamelCase :			str    		=Path(SCREAMING_SNAKE_CASE_		).parent.joinpath(SCREAMING_SNAKE_CASE_		).read_text(encoding='''utf-8'''		)

		lowerCamelCase :			list[list[int]]    		=[]
		for line in data.strip().split('''\n'''		):
				triangles.append([int(SCREAMING_SNAKE_CASE_		) for number in line.split(''','''		)]		)

		lowerCamelCase :			int    		=0
		lowerCamelCase :			list[int]

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

		return ret


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








import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

		# Encoder Layers
		for layer in range(config.num_hidden_layers		):
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''scale''']		)
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''kernel''']		).T
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''bias''']		)

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

		# Masked LM Layers
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''bias''']		)
		lowerCamelCase :			Optional[Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''scale''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''bias''']		)
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_output''']['''output_bias''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']		)

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			Any    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']		)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

		# Save PreTrained
		print(F"Saving pretrained model to {save_path}"		)
		fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_				=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--fnet_config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained FNet model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

