


"""simple docstring"""


import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def   snake_case						(				_a:     Optional[int]		,   _a:     Optional[Any]		,   _a:     int							)->		Tuple:
      '''simple docstring'''


      if gpta_config_file == "":
            lowerCamelCase__           =		GPTaConfig()
      else:
            lowerCamelCase__           =		GPTaConfig.from_json_file(_a							)
      lowerCamelCase__           =		GPTaModel(_a							)

      # Load weights from numpy
      load_tf_weights_in_gpta(_a		,   _a		,   _a							)

      # Save pytorch-model
      lowerCamelCase__           =		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
      lowerCamelCase__           =		pytorch_dump_folder_path + '/' + CONFIG_NAME
      print(F'Save PyTorch model to {pytorch_weights_dump_path}'							)
      torch.save(model.state_dict()		,   _a							)
      print(F'Save configuration file to {pytorch_config_dump_path}'							)
      with open(_a		,   'w'		,   encoding='utf-8'							) as f:
            f.write(config.to_json_string()							)


if __name__ == "__main__":
   _snake_case          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--gpt2_config_file",
       default="",
       type=str,
       help=(
           "An optional config json file corresponding to the pre-trained OpenAI model. \n"
           "This specifies the model architecture."
       ),
   )
   _snake_case          =							parser.parse_args()
   convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)







"""simple docstring"""


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def   snake_case						(				_a:     Optional[Any]							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[-1] * len(_a							)

      def dfs(_a:     Any		,   _a:     Optional[int]							):
            lowerCamelCase__           =		True
            lowerCamelCase__           =		c
            for u in graph[v]:
                  if not visited[u]:
                        dfs(_a		,   1 - c							)

      for i in range(len(_a							)							):
            if not visited[i]:
                  dfs(_a		,   0							)

      for i in range(len(_a							)							):
            for j in graph[i]:
                  if color[i] == color[j]:
                        return False

      return True


# Adjacency list of graph
_snake_case          =							{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}






print(check_bipartite_dfs(graph))



