




from collections import defaultdict
from math import ceil, sqrt

def lowercase    (  SCREAMING_SNAKE_CASE = 1_00_00_00       ,    SCREAMING_SNAKE_CASE = 10 )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						defaultdict(SCREAMING_SNAKE_CASE )

			for outer_width in range(3       ,    (t_limit // 4) + 2 ):
						if outer_width * outer_width > t_limit:
									SCREAMING_SNAKE_CASE_								=						max(
									    ceil(sqrt(outer_width * outer_width - t_limit ) )       ,    1 )
						else:
									SCREAMING_SNAKE_CASE_								=						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() = }""")






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Union[str, 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 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."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
