"""simple docstring"""




from collections import defaultdict
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													A__		   =							total  # total no of tasks (N)

													# DP table will have a dimension of (2^M)*N
													# initially all values are set to -1
													A__		   =							[
													    [-1 for i in range(total + 1   )] for j in range(2 ** len(__UpperCAmelCase   )   )
													]

													A__		   =							defaultdict(__UpperCAmelCase   )  # stores the list of persons for each task

													# final_mask is used to check if all persons are included by setting all bits
													# to 1
													A__		   =							(1 << len(__UpperCAmelCase   )) - 1





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[Any]:
													# if mask == self.finalmask all persons are distributed tasks, return 1
													if mask == self.final_mask:
																				return 1

													# if not everyone gets the task and no more tasks are available, return 0
													if task_no > self.total_tasks:
																				return 0

													# if case already considered
													if self.dp[mask][task_no] != -1:
																				return self.dp[mask][task_no]

													# Number of ways when we don't this task in the arrangement
													A__		   =							self.count_ways_until(__UpperCAmelCase						,task_no + 1   )

													# now assign the tasks one by one to all possible persons and recursively
													# assign for the remaining tasks.
													if task_no in self.task:
																				for p in self.task[task_no]:
																											# if p is already given a task
																											if mask & (1 << p):
																																		continue

																											# assign this task to p and change the mask value. And recursively
																											# assign tasks with the new mask value.
																											total_ways_util += self.count_ways_until(mask | (1 << p)						,task_no + 1   )

        # save the value.
													A__		   =							total_ways_util

													return self.dp[mask][task_no]





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													# Store the list of persons for each task
													for i in range(len(__UpperCAmelCase   )   ):
																				for j in task_performed[i]:
																											self.task[j].append(__UpperCAmelCase   )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
													return self.count_ways_until(0						,1   )


if __name__ == "__main__":
				__lowerCamelCase							 =     5  # total no of tasks (the value of N)

				# the list of tasks that can be done by M persons.
				__lowerCamelCase							 =     [[1, 3, 4], [1, 2, 5], [3, 4]]
				print(
				    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
				        task_performed
				    )
				)






"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(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(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT 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.albert_config_file, args.pytorch_dump_path)





