

'''simple docstring'''







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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[Any]:
       # Initialise PyTorch model
       A_						=      FunnelConfig.from_json_file(UpperCAmelCase__				)
       print(F'''Building PyTorch model from configuration: {config}'''				)
       A_						=      FunnelBaseModel(UpperCAmelCase__				) if base_model else FunnelModel(UpperCAmelCase__				)

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

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


if __name__ == "__main__":
      __lowerCamelCase		       =    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.'''
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
      )



'''simple docstring'''







from __future__ import annotations

import math
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5,       int(math.sqrt(UpperCAmelCase__				) + 1				),       6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


__lowerCamelCase		       =    [num for num in range(3, 10_0001, 2) if not is_prime(num)]
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			list[int]:
       if not isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
              raise ValueError("""n must be an integer"""				)
       if n <= 0:
              raise ValueError("""n must be >= 0"""				)

       A_						=      []
       for num in range(len(UpperCAmelCase__				)				):
              A_						=      0
              while 2 * i * i <= odd_composites[num]:
                     A_						=      odd_composites[num] - 2 * i * i
                     if is_prime(UpperCAmelCase__				):
                            break
                     i += 1
              else:
                     list_nums.append(odd_composites[num]				)
                     if len(UpperCAmelCase__				) == n:
                            return list_nums

       return []
def       UpperCAmelCase__    (      )			->			int:
       return compute_nums(1				)[0]


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