
"""simple docstring"""


import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def      lowerCAmelCase_     (	snake_case_	:    Dict							,					snake_case_	:    Optional[Any]							,					snake_case_	:    Dict    )     ->Tuple:
			# Initialise PyTorch model
			lowerCamelCase__			:  str  =json.load(open(snake_case_							,					'r'    )    )
			lowerCamelCase__			:  List[str]  =GPTNeoConfig(
			    hidden_size=config_json['n_embd']							,					num_layers=config_json['n_layer']							,					num_heads=config_json['n_head']							,					attention_types=config_json['attention_types']							,					max_position_embeddings=config_json['n_positions']							,					resid_dropout=config_json['res_dropout']							,					embed_dropout=config_json['embed_dropout']							,					attention_dropout=config_json['attn_dropout']							,					)
			print(f"""Building PyTorch model from configuration: {config}"""    )
			lowerCamelCase__			:  Tuple  =GPTNeoForCausalLM(snake_case_    )

			# Load weights from tf checkpoint
			load_tf_weights_in_gpt_neo(snake_case_							,					snake_case_							,					snake_case_    )

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


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 mesh-tf model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCAmelCase			=		parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_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.
from queue import Queue






def      lowerCAmelCase_     (	snake_case_	:    List[Any]    )     ->Optional[int]:
			lowerCamelCase__			:  Optional[int]  =Queue()
			lowerCamelCase__			:  Any  =[False] * len(snake_case_    )
			lowerCamelCase__			:  Optional[Any]  =[-1] * len(snake_case_    )

			def bfs():
						while not queue.empty():
									lowerCamelCase__			:  List[Any]  =queue.get()
									lowerCamelCase__			:  int  =True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															lowerCamelCase__			:  Union[str, Any]  =1 - color[u]
															queue.put(snake_case_    )

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(snake_case_    )    ):
						if not visited[i]:
									queue.put(snake_case_    )
									lowerCamelCase__			:  Tuple  =0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
							# Adjacency List of graph
							print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))