

'''simple docstring'''


from __future__ import annotations

from queue import Queue



class        lowercase    :


							def __init__(      self)				->					None:
								UpperCAmelCase_		:   dict[int, list[int]]													=       {}


							def     _snake_case				(      self)				->					None:





								for i in self.vertices:
									print(_snake_case     ,      ' : '     ,      ' -> '.join([str(_snake_case) for j in self.vertices[i]]))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					None:





								if from_vertex in self.vertices:
									self.vertices[from_vertex].append(_snake_case)
								else:
									UpperCAmelCase_		:   Optional[int]													=       [to_vertex]





							def     _snake_case				(      self     ,      _snake_case)				->					set[int]:
								UpperCAmelCase_		:   Union[str, Any]													=       set()

								# create a first in first out queue to store all the vertices for BFS
								UpperCAmelCase_		:   Queue													=       Queue()

								# mark the source node as visited and enqueue it
								visited.add(_snake_case)
								queue.put(_snake_case)

								while not queue.empty():
									UpperCAmelCase_		:   Optional[int]													=       queue.get()

									# loop through all adjacent vertex and enqueue it if not yet visited
									for adjacent_vertex in self.vertices[vertex]:
										if adjacent_vertex not in visited:
											queue.put(_snake_case)
											visited.add(_snake_case)
								return visited


if __name__ == "__main__":
						from doctest import testmod

						testmod(verbose=True)

						lowerCAmelCase__      				=			Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						# 0  :  1 -> 2
						# 1  :  2
						# 2  :  0 -> 3
						# 3  :  3

						assert sorted(g.bfs(2)) == [0, 1, 2, 3]









"""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							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     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__":
 A									=						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."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
