


'''simple docstring'''

import argparse

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


logging.set_verbosity_info()






def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Dict      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		TaConfig.from_json_file(__lowerCamelCase				)
     print(F"Building PyTorch model from configuration: {config}"				)
     _a						=		TaForConditionalGeneration(__lowerCamelCase				)

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

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


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





'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a			,			_a						=		9, 14  # noqa: F841
     _a						=		[
         [0, 1, 4],
         [0, 7, 8],
         [1, 2, 8],
         [7, 8, 7],
         [7, 6, 1],
         [2, 8, 2],
         [8, 6, 6],
         [2, 3, 7],
         [2, 5, 4],
         [6, 5, 2],
         [3, 5, 14],
         [3, 4, 9],
         [5, 4, 10],
         [1, 7, 11],
     ]

     _a						=		defaultdict(__lowerCamelCase				)
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]				)
          adjancency[nodea].append([nodea, cost]				)

     _a						=		mst(__lowerCamelCase				)

     _a						=		[
         [7, 6, 1],
         [2, 8, 2],
         [6, 5, 2],
         [0, 1, 4],
         [2, 5, 4],
         [2, 3, 7],
         [0, 7, 8],
         [3, 4, 9],
     ]

     for answer in expected:
          _a						=		tuple(answer[:2]				)
          _a						=		tuple(edge[::-1]				)
          assert edge in result or reverse in result

