"""simple docstring"""



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							A__       :     Any								=			TapasConfig.from_json_file(lowerCAmelCase							)
							# set absolute/relative position embeddings parameter
							A__       :     Union[str, Any]								=			reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														A__       :     Any								=			TapasForQuestionAnswering(config=lowerCAmelCase							)
							elif task == "WTQ":
														# run_task_main.py hparams
														A__       :     str								=			4
														A__       :     int								=			True
														# hparam_utils.py hparams
														A__       :     Tuple								=			0.66_4694
														A__       :     List[str]								=			0.20_7951
														A__       :     Any								=			0.12_1194
														A__       :     Dict								=			True
														A__       :     Optional[int]								=			True
														A__       :     List[str]								=			False
														A__       :     Union[str, Any]								=			0.035_2513

														A__       :     Optional[int]								=			TapasForQuestionAnswering(config=lowerCAmelCase							)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														A__       :     str								=			4
														A__       :     List[str]								=			False
														# hparam_utils.py hparams
														A__       :     Optional[int]								=			36.4519
														A__       :     Union[str, Any]								=			0.90_3421
														A__       :     Optional[int]								=			222.088
														A__       :     Any								=			True
														A__       :     List[Any]								=			True
														A__       :     str								=			True
														A__       :     Tuple								=			0.76_3141

														A__       :     Dict								=			TapasForQuestionAnswering(config=lowerCAmelCase							)
							elif task == "TABFACT":
														A__       :     List[str]								=			TapasForSequenceClassification(config=lowerCAmelCase							)
							elif task == "MLM":
														A__       :     Dict								=			TapasForMaskedLM(config=lowerCAmelCase							)
							elif task == "INTERMEDIATE_PRETRAINING":
														A__       :     List[Any]								=			TapasModel(config=lowerCAmelCase							)
							else:
														raise ValueError(F'''Task {task} not supported.'''							)

							print(F'''Building PyTorch model from configuration: {config}'''							)
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)

							# Save pytorch-model (weights and configuration)
							print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
							model.save_pretrained(lowerCAmelCase							)

							# Save tokenizer files
							print(F'''Save tokenizer files to {pytorch_dump_path}'''							)
							A__       :     Optional[int]								=			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""			,  model_max_length=512							)
							tokenizer.save_pretrained(lowerCAmelCase							)

							print("""Used relative position embeddings:"""			,  model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
				_UpperCamelCase								=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
				)
				parser.add_argument(
				    "--reset_position_index_per_cell",
				    default=False,
				    action="store_true",
				    help="Whether to use relative position embeddings or not. Defaults to True.",
				)
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--tapas_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained TAPAS 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."
				)
				_UpperCamelCase								=		parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.task,
				    args.reset_position_index_per_cell,
				    args.tf_checkpoint_path,
				    args.tapas_config_file,
				    args.pytorch_dump_path,
				)






"""simple docstring"""



from collections import defaultdict
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			total  # total no of tasks (N)

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

										A__       :     Optional[int]								=			defaultdict(snake_case_							)  # 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__       :     Optional[int]								=			(1 << len(snake_case_							)) - 1





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										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__       :     Optional[int]								=			self.count_ways_until(snake_case_   ,      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__       :     List[str]								=			total_ways_util

										return self.dp[mask][task_no]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for i in range(len(snake_case_							)							):
																	for j in task_performed[i]:
																								self.task[j].append(snake_case_							)

        # 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__":
				_UpperCamelCase								=		5  # total no of tasks (the value of N)

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





