


'''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 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       List[str]		)					->	Dict:







     """simple docstring"""
     # 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

def 					lowerCamelCase_ ( __UpperCamelCase					:       dict							,    __UpperCamelCase					:       str		)					->	set[str]:







     """simple docstring"""
     _A		,				_A				=							set(__UpperCamelCase		), [start]

     while stack:
          _A				=							stack.pop()
          explored.add(__UpperCamelCase		)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]		):
               if adj not in explored:
                    stack.append(__UpperCamelCase		)
     return explored


lowerCAmelCase						=					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, """A"""))





