


"""simple docstring"""







from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def 					lowercase      (    A_  ,       A_	)->				str | Literal[False]:
		'''simple docstring'''







		a						:		Any											=      list(A_	)
		a						:		Optional[Any]											=      list(A_	)
		a						:		Dict											=      0
		for i in range(len(A_	)	):
				if lista[i] != lista[i]:
						count += 1
						a						:		str											=      "_"
		if count > 1:
				return False
		else:
				return "".join(A_	)


def 					lowercase      (    A_	)->				list[str]:
		'''simple docstring'''







		a						:		str											=      []
		while True:
				a						:		str											=      ["$"] * len(A_	)
				a						:		Dict											=      []
				for i in range(len(A_	)	):
						for j in range(i + 1  ,       len(A_	)	):
								a						:		Any											=      compare_string(binary[i]  ,       binary[j]	)
								if k is False:
										a						:		Union[str, Any]											=      "*"
										a						:		Tuple											=      "*"
										temp.append("X"	)
				for i in range(len(A_	)	):
						if checka[i] == "$":
								pi.append(binary[i]	)
				if len(A_	) == 0:
						return pi
				a						:		Tuple											=      list(set(A_	)	)


def 					lowercase      (    A_  ,       A_	)->				list[str]:
		'''simple docstring'''







		a						:		Union[str, Any]											=      []
		for minterm in minterms:
				a						:		int											=      ""
				for _ in range(A_	):
						a						:		int											=      str(minterm % 2	) + string
						minterm //= 2
				temp.append(A_	)
		return temp


def 					lowercase      (    A_  ,       A_  ,       A_	)->				bool:
		'''simple docstring'''







		a						:		Dict											=      list(A_	)
		a						:		Union[str, Any]											=      list(A_	)
		a						:		str											=      0
		for i in range(len(A_	)	):
				if lista[i] != lista[i]:
						count_n += 1
		return count_n == count


def 					lowercase      (    A_  ,       A_	)->				list[str]:
		'''simple docstring'''







		a						:		Union[str, Any]											=      []
		a						:		List[str]											=      [0] * len(A_	)
		for i in range(len(chart[0]	)	):
				a						:		int											=      0
				a						:		Any											=      -1
				for j in range(len(A_	)	):
						if chart[j][i] == 1:
								count += 1
								a						:		Union[str, Any]											=      j
				if count == 1:
						a						:		int											=      1
		for i in range(len(A_	)	):
				if select[i] == 1:
						for j in range(len(chart[0]	)	):
								if chart[i][j] == 1:
										for k in range(len(A_	)	):
												a						:		List[str]											=      0
						temp.append(prime_implicants[i]	)
		while True:
				a						:		Optional[Any]											=      0
				a						:		Union[str, Any]											=      -1
				a						:		Union[str, Any]											=      0
				for i in range(len(A_	)	):
						a						:		Tuple											=      chart[i].count(1	)
						if count_n > max_n:
								a						:		int											=      count_n
								a						:		List[str]											=      i

				if max_n == 0:
						return temp

				temp.append(prime_implicants[rem]	)

				for i in range(len(chart[0]	)	):
						if chart[rem][i] == 1:
								for j in range(len(A_	)	):
										a						:		Dict											=      0


def 					lowercase      (    A_  ,       A_	)->				list[list[int]]:
		'''simple docstring'''







		a						:		List[str]											=      [[0 for x in range(len(A_	)	)] for x in range(len(A_	)	)]
		for i in range(len(A_	)	):
				a						:		int											=      prime_implicants[i].count("_"	)
				for j in range(len(A_	)	):
						if is_for_table(prime_implicants[i]  ,       binary[j]  ,       A_	):
								a						:		Tuple											=      1

		return chart


def 					lowercase      (    )->				None:
		'''simple docstring'''







		a						:		Optional[int]											=      int(input("Enter the no. of variables\n"	)	)
		a						:		str											=      [
		    float(A_	)
		    for x in input(
		        "Enter the decimal representation of Minterms 'Spaces Separated'\n"	).split()
		]
		a						:		List[str]											=      decimal_to_binary(A_  ,       A_	)

		a						:		int											=      check(A_	)
		print("Prime Implicants are:"	)
		print(A_	)
		a						:		List[Any]											=      prime_implicant_chart(A_  ,       A_	)

		a						:		Dict											=      selection(A_  ,       A_	)
		print("Essential Prime Implicants are:"	)
		print(A_	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()










"""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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      TapasModel(config=A_	)
  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(A_  ,       A_  ,       A_	)

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

  # 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(A_	)

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


if __name__ == "__main__":
 __lowercase    =				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."""
 )
 __lowercase    =				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,
 )






