from __future__ import annotations
def    __magic_name__(	__UpperCAmelCase		)    ->     set[int]:



      '''simple docstring'''

      _lowerCamelCase           =					{1}
      _lowerCamelCase           =					[1]
      for _ in range(2			,    depth + 1		):
            _lowerCamelCase           =					[*previous_coefficients, 0]
            _lowerCamelCase           =					[0, *previous_coefficients]
            _lowerCamelCase           =					[]
            for x, y in zip(__UpperCAmelCase			,    __UpperCAmelCase		):
                  coefficients.add(x + y		)
                  previous_coefficients.append(x + y		)
      return coefficients
def    __magic_name__(	__UpperCAmelCase		)    ->     set[int]:



      '''simple docstring'''

      _lowerCamelCase           =					set()
      for number in unique_coefficients:
            _lowerCamelCase           =					2
            _lowerCamelCase           =					number
            while divisor**2 <= copy_number:
                  _lowerCamelCase           =					0
                  while copy_number % divisor == 0:
                        copy_number //= divisor
                        multiplicity += 1
                  if multiplicity >= 2:
                        non_squarefrees.add(__UpperCAmelCase		)
                        break
                  divisor += 1

      return unique_coefficients.difference(__UpperCAmelCase		)
def    __magic_name__(	__UpperCAmelCase = 51		)    ->     int:



      '''simple docstring'''

      _lowerCamelCase           =					get_pascal_triangle_unique_coefficients(__UpperCAmelCase		)
      _lowerCamelCase           =					get_squarefrees(__UpperCAmelCase		)
      return sum(__UpperCAmelCase		)


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

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

      print(F'Building PyTorch model from configuration: {config}'		)

      # Load weights from tf checkpoint
      load_tf_weights_in_canine(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)

      # Save pytorch-model (weights and configuration)
      print(F'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(__UpperCAmelCase		)

      # Save tokenizer files
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)