


'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Any     		=      BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
				print(f"""Building PyTorch model from configuration: {config}"""     )

				if is_trivia_qa:
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE__     )
				else:
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      BigBirdForPreTraining(SCREAMING_SNAKE_CASE__     )

				# Load weights from tf checkpoint
				load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      is_trivia_qa=SCREAMING_SNAKE_CASE__     )

				# Save pytorch-model
				print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
				model.save_pretrained(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
				UpperCAmelCase_		:   int							   =		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(
				    '--big_bird_config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained BERT 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.'
				)
				parser.add_argument(
				    '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
				)
				UpperCAmelCase_		:   str							   =		parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
				)








'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


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




