






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# Initialise PyTorch model
			SCREAMING_SNAKE_CASE						= TaConfig.from_json_file(_UpperCAmelCase)
			print(F'''Building PyTorch model from configuration: {config}''')
			SCREAMING_SNAKE_CASE						= TaForConditionalGeneration(_UpperCAmelCase)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase)

			# Save pytorch-model
			print(F'''Save PyTorch model to {pytorch_dump_path}''')
			model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
				a_		:						Any             =    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(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				a_		:						str             =    parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

			SCREAMING_SNAKE_CASE						= ''
			with open(_UpperCAmelCase) as f:
						SCREAMING_SNAKE_CASE						= f.readline()

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    word
			    for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase)


if __name__ == "__main__":
				print(solution())




