




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							str	):
					# Construct configuration depending on size
					__lowerCAmelCase							  =			{"small": (5_12, 8, 24), "medium": (10_24, 8, 36), "large": (15_36, 16, 48)}
					__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			MODELS[model_size]  # set model hyperparameters
					__lowerCAmelCase							  =			ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_ , n_layer=SCREAMING_SNAKE_CASE_ , n_head=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			ImageGPTForCausalLM(SCREAMING_SNAKE_CASE_	)

					# Load weights from numpy
					load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

					# Save pytorch-model
					__lowerCAmelCase							  =			pytorch_dump_folder_path + "/" + WEIGHTS_NAME
					__lowerCAmelCase							  =			pytorch_dump_folder_path + "/" + CONFIG_NAME
					print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
					torch.save(model.state_dict() , SCREAMING_SNAKE_CASE_	)
					print(F"""Save configuration file to {pytorch_config_dump_path}"""	)
					with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8"	) as f:
										f.write(config.to_json_string()	)


if __name__ == "__main__":
					UpperCamelCase__            =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--imagegpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the TensorFlow checkpoint path.""",
					)
					parser.add_argument(
					    """--model_size""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					UpperCamelCase__            =     parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)










from __future__ import annotations

from pathlib import Path







def       _a (		SCREAMING_SNAKE_CASE_				:							tuple[int, int] , SCREAMING_SNAKE_CASE_				:							tuple[int, int]	):
					return pointa[0] * pointa[1] - pointa[1] * pointa[0]







def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			(xa, ya)
					__lowerCAmelCase							  =			(xa - xa, ya - ya)
					__lowerCAmelCase							  =			(xa - xa, ya - ya)
					__lowerCAmelCase							  =			-vector_product(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	) / vector_product(
					    SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			+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_				:							str = "p102_triangles.txt"	):
					__lowerCAmelCase							  =			Path(SCREAMING_SNAKE_CASE_	).parent.joinpath(SCREAMING_SNAKE_CASE_	).read_text(encoding="utf-8"	)

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

					__lowerCAmelCase							  =			0
					__lowerCAmelCase							  =			42

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

					return ret


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




