


"""simple docstring"""







import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

__lowercase    =				(
    """4S 3H 2C 7S 5H""",
    """9D 8H 2C 6S 7H""",
    """2D 6D 9D TH 7D""",
    """TC 8C 2S JH 6C""",
    """JH 8S TH AH QH""",
    """TS KS 5S 9S AC""",
    """KD 6S 9D TH AD""",
    """KS 8D 4D 9S 4S""",  # pair
    """8C 4S KH JS 4D""",  # pair
    """QH 8H KD JH 8S""",  # pair
    """KC 4H KS 2H 8D""",  # pair
    """KD 4S KC 3H 8S""",  # pair
    """AH 8S AS KC JH""",  # pair
    """3H 4C 4H 3S 2H""",  # 2 pairs
    """5S 5D 2C KH KH""",  # 2 pairs
    """3C KH 5D 5S KH""",  # 2 pairs
    """AS 3C KH AD KH""",  # 2 pairs
    """7C 7S 3S 7H 5S""",  # 3 of a kind
    """7C 7S KH 2H 7H""",  # 3 of a kind
    """AC KH QH AH AS""",  # 3 of a kind
    """2H 4D 3C AS 5S""",  # straight (low ace)
    """3C 5C 4C 2C 6H""",  # straight
    """6S 8S 7S 5H 9H""",  # straight
    """JS QS 9H TS KH""",  # straight
    """QC KH TS JS AH""",  # straight (high ace)
    """8C 9C 5C 3C TC""",  # flush
    """3S 8S 9S 5S KS""",  # flush
    """4C 5C 9C 8C KC""",  # flush
    """JH 8H AH KH QH""",  # flush
    """3D 2H 3H 2C 2D""",  # full house
    """2H 2C 3S 3H 3D""",  # full house
    """KH KC 3S 3H 3D""",  # full house
    """JC 6H JS JD JH""",  # 4 of a kind
    """JC 7H JS JD JH""",  # 4 of a kind
    """JC KH JS JD JH""",  # 4 of a kind
    """2S AS 4S 5S 3S""",  # straight flush (low ace)
    """2D 6D 3D 4D 5D""",  # straight flush
    """5C 6C 3C 7C 4C""",  # straight flush
    """JH 9H TH KH QH""",  # straight flush
    """JH AH TH KH QH""",  # royal flush (high ace straight flush)
)

__lowercase    =				(
    ("""2H 3H 4H 5H 6H""", """KS AS TS QS JS""", """Loss"""),
    ("""2H 3H 4H 5H 6H""", """AS AD AC AH JD""", """Win"""),
    ("""AS AH 2H AD AC""", """JS JD JC JH 3D""", """Win"""),
    ("""2S AH 2H AS AC""", """JS JD JC JH AD""", """Loss"""),
    ("""2S AH 2H AS AC""", """2H 3H 5H 6H 7H""", """Win"""),
    ("""AS 3S 4S 8S 2S""", """2H 3H 5H 6H 7H""", """Win"""),
    ("""2H 3H 5H 6H 7H""", """2S 3H 4H 5S 6C""", """Win"""),
    ("""2S 3H 4H 5S 6C""", """3D 4C 5H 6H 2S""", """Tie"""),
    ("""2S 3H 4H 5S 6C""", """AH AC 5H 6H AS""", """Win"""),
    ("""2S 2H 4H 5S 4C""", """AH AC 5H 6H AS""", """Loss"""),
    ("""2S 2H 4H 5S 4C""", """AH AC 5H 6H 7S""", """Win"""),
    ("""6S AD 7H 4S AS""", """AH AC 5H 6H 7S""", """Loss"""),
    ("""2S AH 4H 5S KC""", """AH AC 5H 6H 7S""", """Loss"""),
    ("""2S 3H 6H 7S 9C""", """7H 3C TH 6H 9S""", """Loss"""),
    ("""4S 5H 6H TS AC""", """3S 5H 6H TS AC""", """Win"""),
    ("""2S AH 4H 5S 6C""", """AD 4C 5H 6H 2C""", """Tie"""),
    ("""AS AH 3H AD AC""", """AS AH 2H AD AC""", """Win"""),
    ("""AH AC 5H 5C QS""", """AH AC 5H 5C KS""", """Loss"""),
    ("""AH AC 5H 5C QS""", """KH KC 5H 5C QS""", """Win"""),
    ("""7C 7S KH 2H 7H""", """3C 3S AH 2H 3H""", """Win"""),
    ("""3C 3S AH 2H 3H""", """7C 7S KH 2H 7H""", """Loss"""),
    ("""6H 5H 4H 3H 2H""", """5H 4H 3H 2H AH""", """Win"""),
    ("""5H 4H 3H 2H AH""", """5H 4H 3H 2H AH""", """Tie"""),
    ("""5H 4H 3H 2H AH""", """6H 5H 4H 3H 2H""", """Loss"""),
    ("""AH AD KS KC AC""", """AH KD KH AC KC""", """Win"""),
    ("""2H 4D 3C AS 5S""", """2H 4D 3C 6S 5S""", """Loss"""),
    ("""2H 3S 3C 3H 2S""", """3S 3C 2S 2H 2D""", """Win"""),
    ("""4D 6D 5D 2D JH""", """3S 8S 3H TC KH""", """Loss"""),
    ("""4S 6C 8S 3S 7S""", """AD KS 2D 7D 7C""", """Loss"""),
    ("""6S 4C 7H 8C 3H""", """5H JC AH 9D 9C""", """Loss"""),
    ("""9D 9H JH TC QH""", """3C 2S JS 5C 7H""", """Win"""),
    ("""2H TC 8S AD 9S""", """4H TS 7H 2C 5C""", """Win"""),
    ("""9D 3S 2C 7S 7C""", """JC TD 3C TC 9H""", """Loss"""),
)

__lowercase    =				(
    ("""2H 3H 4H 5H 6H""", True),
    ("""AS AH 2H AD AC""", False),
    ("""2H 3H 5H 6H 7H""", True),
    ("""KS AS TS QS JS""", True),
    ("""8H 9H QS JS TH""", False),
    ("""AS 3S 4S 8S 2S""", True),
)

__lowercase    =				(
    ("""2H 3H 4H 5H 6H""", True),
    ("""AS AH 2H AD AC""", False),
    ("""2H 3H 5H 6H 7H""", False),
    ("""KS AS TS QS JS""", True),
    ("""8H 9H QS JS TH""", True),
)

__lowercase    =				(
    ("""2H 4D 3C AS 5S""", True, [5, 4, 3, 2, 14]),
    ("""2H 5D 3C AS 5S""", False, [14, 5, 5, 3, 2]),
    ("""JH QD KC AS TS""", False, [14, 13, 12, 11, 10]),
    ("""9D 3S 2C 7S 7C""", False, [9, 7, 7, 3, 2]),
)

__lowercase    =				(
    ("""JH AH TH KH QH""", 0),
    ("""JH 9H TH KH QH""", 0),
    ("""JC KH JS JD JH""", 7),
    ("""KH KC 3S 3H 3D""", 6),
    ("""8C 9C 5C 3C TC""", 0),
    ("""JS QS 9H TS KH""", 0),
    ("""7C 7S KH 2H 7H""", 3),
    ("""3C KH 5D 5S KH""", 2),
    ("""QH 8H KD JH 8S""", 1),
    ("""2D 6D 9D TH 7D""", 0),
)

__lowercase    =				(
    ("""JH AH TH KH QH""", 23),
    ("""JH 9H TH KH QH""", 22),
    ("""JC KH JS JD JH""", 21),
    ("""KH KC 3S 3H 3D""", 20),
    ("""8C 9C 5C 3C TC""", 19),
    ("""JS QS 9H TS KH""", 18),
    ("""7C 7S KH 2H 7H""", 17),
    ("""3C KH 5D 5S KH""", 16),
    ("""QH 8H KD JH 8S""", 15),
    ("""2D 6D 9D TH 7D""", 14),
)


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







		a						:		Optional[int]											=      randrange(len(A_	)	), randrange(len(A_	)	)
		a						:		int											=      ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
		a						:		Tuple											=      SORTED_HANDS[play], SORTED_HANDS[oppo]
		return hand, other, expected


def 					lowercase      (    A_ = 100	)->				Union[str, Any]:
		'''simple docstring'''







		return (generate_random_hand() for _ in range(A_	))


@pytest.mark.parametrize("hand, expected"  ,       A_	)
def 					lowercase      (    A_  ,       A_	)->				List[Any]:
		'''simple docstring'''







		assert PokerHand(A_	)._is_flush() == expected


@pytest.mark.parametrize("hand, expected"  ,       A_	)
def 					lowercase      (    A_  ,       A_	)->				Tuple:
		'''simple docstring'''







		assert PokerHand(A_	)._is_straight() == expected


@pytest.mark.parametrize("hand, expected, card_values"  ,       A_	)
def 					lowercase      (    A_  ,       A_  ,       A_	)->				int:
		'''simple docstring'''







		a						:		Tuple											=      PokerHand(A_	)
		assert player._is_five_high_straight() == expected
		assert player._card_values == card_values


@pytest.mark.parametrize("hand, expected"  ,       A_	)
def 					lowercase      (    A_  ,       A_	)->				List[str]:
		'''simple docstring'''







		assert PokerHand(A_	)._is_same_kind() == expected


@pytest.mark.parametrize("hand, expected"  ,       A_	)
def 					lowercase      (    A_  ,       A_	)->				Any:
		'''simple docstring'''







		assert PokerHand(A_	)._hand_type == expected


@pytest.mark.parametrize("hand, other, expected"  ,       A_	)
def 					lowercase      (    A_  ,       A_  ,       A_	)->				Any:
		'''simple docstring'''







		assert PokerHand(A_	).compare_with(PokerHand(A_	)	) == expected


@pytest.mark.parametrize("hand, other, expected"  ,       generate_random_hands()	)
def 					lowercase      (    A_  ,       A_  ,       A_	)->				str:
		'''simple docstring'''







		assert PokerHand(A_	).compare_with(PokerHand(A_	)	) == expected


def 					lowercase      (    )->				Tuple:
		'''simple docstring'''







		a						:		Dict											=      [PokerHand(A_	) for hand in SORTED_HANDS]
		a						:		Any											=      poker_hands.copy()
		shuffle(A_	)
		a						:		Union[str, Any]											=      chain(sorted(A_	)	)
		for index, hand in enumerate(A_	):
				assert hand == poker_hands[index]


def 					lowercase      (    )->				str:
		'''simple docstring'''







		a						:		Union[str, Any]											=      [PokerHand("2D AC 3H 4H 5S"	), PokerHand("2S 3H 4H 5S 6C"	)]
		pokerhands.sort(reverse=A_	)
		assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"


def 					lowercase      (    )->				Dict:
		'''simple docstring'''







		a						:		Optional[int]											=      PokerHand("2C 4S AS 3D 5C"	)
		a						:		Union[str, Any]											=      True
		a						:		List[str]											=      [5, 4, 3, 2, 14]
		for _ in range(10	):
				assert pokerhand._is_five_high_straight() == expected
				assert pokerhand._card_values == expected_card_values







def 					lowercase      (    )->				Any:
		'''simple docstring'''







		a						:		int											=      0
		a						:		Dict											=      os.path.abspath(os.path.dirname(A_	)	)
		a						:		Tuple											=      os.path.join(A_  ,       "poker_hands.txt"	)
		with open(A_	) as file_hand:
				for line in file_hand:
						a						:		Optional[Any]											=      line[:14].strip()
						a						:		Union[str, Any]											=      line[15:].strip()
						a						:		Tuple											=      PokerHand(A_	), PokerHand(A_	)
						a						:		Dict											=      player.compare_with(A_	)
						if output == "Win":
								answer += 1
		assert answer == 376










"""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,
 )






