"""simple docstring"""






import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


__lowerCAmelCase					: Any        =logging.getLogger(__name__)

__lowerCAmelCase					: str        =tf.data.AUTOTUNE


def     UpperCAmelCase__ (       )				->					List[Any]:
      '''simple docstring'''


      lowercase														=			argparse.ArgumentParser(description="""Train a masked language model on TPU."""	)
      parser.add_argument(
          """--pretrained_model_config"""      ,	type=snake_case_      ,	default="""roberta-base"""      ,	help="""The model config to use. Note that we don't copy the model's weights, only the config!"""      ,	)
      parser.add_argument(
          """--tokenizer"""      ,	type=snake_case_      ,	default="""unigram-tokenizer-wikitext"""      ,	help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."""      ,	)

      parser.add_argument(
          """--per_replica_batch_size"""      ,	type=snake_case_      ,	default=8      ,	help="""Batch size per TPU core."""      ,	)

      parser.add_argument(
          """--no_tpu"""      ,	action="""store_true"""      ,	help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."""      ,	)

      parser.add_argument(
          """--tpu_name"""      ,	type=snake_case_      ,	help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."""      ,	default="""local"""      ,	)

      parser.add_argument(
          """--tpu_zone"""      ,	type=snake_case_      ,	help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""      ,	)

      parser.add_argument(
          """--gcp_project"""      ,	type=snake_case_      ,	help="""Google cloud project name. Only used for non-Colab TPU nodes."""	)

      parser.add_argument(
          """--bfloat16"""      ,	action="""store_true"""      ,	help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."""      ,	)

      parser.add_argument(
          """--train_dataset"""      ,	type=snake_case_      ,	help="""Path to training dataset to load. If the path begins with `gs://`"""
          """ then the dataset will be loaded from a Google Cloud Storage bucket."""      ,	)

      parser.add_argument(
          """--shuffle_buffer_size"""      ,	type=snake_case_      ,	default=2**1_8      ,	help="""Size of the shuffle buffer (in samples)"""      ,	)

      parser.add_argument(
          """--eval_dataset"""      ,	type=snake_case_      ,	help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
          """ then the dataset will be loaded from a Google Cloud Storage bucket."""      ,	)

      parser.add_argument(
          """--num_epochs"""      ,	type=snake_case_      ,	default=1      ,	help="""Number of epochs to train for."""      ,	)

      parser.add_argument(
          """--learning_rate"""      ,	type=snake_case_      ,	default=1e-4      ,	help="""Learning rate to use for training."""      ,	)

      parser.add_argument(
          """--weight_decay_rate"""      ,	type=snake_case_      ,	default=1e-3      ,	help="""Weight decay rate to use for training."""      ,	)

      parser.add_argument(
          """--max_length"""      ,	type=snake_case_      ,	default=5_1_2      ,	help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""      ,	)

      parser.add_argument(
          """--mlm_probability"""      ,	type=snake_case_      ,	default=0.15      ,	help="""Fraction of tokens to mask during training."""      ,	)

      parser.add_argument("""--output_dir"""      ,	type=snake_case_      ,	required=snake_case_      ,	help="""Path to save model checkpoints to."""	)
      parser.add_argument("""--hub_model_id"""      ,	type=snake_case_      ,	help="""Model ID to upload to on the Hugging Face Hub."""	)

      lowercase														=			parser.parse_args()
      return args


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple	)				->					int:
      '''simple docstring'''


      try:
            if args.tpu_name:
                  lowercase														=			tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name      ,	zone=args.tpu_zone      ,	project=args.gcp_project	)
            else:
                  lowercase														=			tf.distribute.cluster_resolver.TPUClusterResolver()
      except ValueError:
            raise RuntimeError(
                """Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
                """--gcp_project. When running on a TPU VM, use --tpu_name local."""	)

      tf.config.experimental_connect_to_cluster(snake_case_	)
      tf.tpu.experimental.initialize_tpu_system(snake_case_	)

      return tpu


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			0
      for file in file_list:
            lowercase														=			file.split("""/"""	)[-1]
            lowercase														=			re.search(R"""-\d+-(\d+)\.tfrecord"""      ,	snake_case_	).group(1	)
            lowercase														=			int(snake_case_	)
            num_samples += sample_count

      return num_samples


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :str=None	)				->					List[str]:
      '''simple docstring'''


      lowercase														=			count_samples(snake_case_	)
      lowercase														=			tf.data.Dataset.from_tensor_slices(snake_case_	)
      if shuffle:
            lowercase														=			dataset.shuffle(len(snake_case_	)	)
      lowercase														=			tf.data.TFRecordDataset(snake_case_      ,	num_parallel_reads=snake_case_	)
      # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
      lowercase														=			dataset.apply(tf.data.experimental.assert_cardinality(snake_case_	)	)
      lowercase														=			dataset.map(snake_case_      ,	num_parallel_calls=snake_case_	)
      if shuffle:
            assert shuffle_buffer_size is not None
            lowercase														=			dataset.shuffle(args.shuffle_buffer_size	)
      lowercase														=			dataset.batch(snake_case_      ,	drop_remainder=snake_case_	)
      lowercase														=			dataset.map(snake_case_      ,	num_parallel_calls=snake_case_	)
      lowercase														=			dataset.prefetch(snake_case_	)
      return dataset


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]	)				->					Tuple:
      '''simple docstring'''


      if not args.no_tpu:
            lowercase														=			initialize_tpu(snake_case_	)
            lowercase														=			tf.distribute.TPUStrategy(snake_case_	)
      else:
            lowercase														=			tf.distribute.OneDeviceStrategy(device="""/gpu:0"""	)

      if args.bfloataa:
            tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""	)

      lowercase														=			AutoTokenizer.from_pretrained(args.tokenizer	)
      lowercase														=			AutoConfig.from_pretrained(args.pretrained_model_config	)
      lowercase														=			tokenizer.vocab_size

      lowercase														=			tf.io.gfile.glob(os.path.join(args.train_dataset      ,	"""*.tfrecord"""	)	)
      if not training_records:
            raise ValueError(f'No .tfrecord files found in {args.train_dataset}.'	)
      lowercase														=			tf.io.gfile.glob(os.path.join(args.eval_dataset      ,	"""*.tfrecord"""	)	)
      if not eval_records:
            raise ValueError(f'No .tfrecord files found in {args.eval_dataset}.'	)

      lowercase														=			count_samples(snake_case_	)

      lowercase														=			num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
      lowercase														=			steps_per_epoch * args.num_epochs

      with strategy.scope():
            lowercase														=			TFAutoModelForMaskedLM.from_config(snake_case_	)
            model(model.dummy_inputs	)  # Pass some dummy inputs through the model to ensure all the weights are built
            lowercase														=			create_optimizer(
                num_train_steps=snake_case_      ,	num_warmup_steps=total_train_steps // 2_0      ,	init_lr=args.learning_rate      ,	weight_decay_rate=args.weight_decay_rate      ,	)

            # Transformers models compute the right loss for their task by default when labels are passed, and will
            # use this for training unless you specify your own loss function in compile().
            model.compile(optimizer=snake_case_      ,	metrics=["""accuracy"""]	)

      def decode_fn(lowerCAmelCase__  :Optional[int]	):
            lowercase														=			{
                """input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa      ,	shape=(args.max_length,)	),
                """attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa      ,	shape=(args.max_length,)	),
            }
            return tf.io.parse_single_example(snake_case_      ,	snake_case_	)

      # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
      # use their methods in our data pipeline.
      lowercase														=			DataCollatorForLanguageModeling(
          tokenizer=snake_case_      ,	mlm_probability=args.mlm_probability      ,	mlm=snake_case_      ,	return_tensors="""tf"""	)

      def mask_with_collator(lowerCAmelCase__  :int	):
            # TF really needs an isin() function
            lowercase														=			(
                ~tf.cast(batch["""attention_mask"""]      ,	tf.bool	)
                | (batch["""input_ids"""] == tokenizer.cls_token_id)
                | (batch["""input_ids"""] == tokenizer.sep_token_id)
            )
            lowercase														=			data_collator.tf_mask_tokens(
                batch["""input_ids"""]      ,	vocab_size=len(snake_case_	)      ,	mask_token_id=tokenizer.mask_token_id      ,	special_tokens_mask=snake_case_      ,	)
            return batch

      lowercase														=			args.per_replica_batch_size * strategy.num_replicas_in_sync

      lowercase														=			prepare_dataset(
          snake_case_      ,	decode_fn=snake_case_      ,	mask_fn=snake_case_      ,	batch_size=snake_case_      ,	shuffle=snake_case_      ,	shuffle_buffer_size=args.shuffle_buffer_size      ,	)

      lowercase														=			prepare_dataset(
          snake_case_      ,	decode_fn=snake_case_      ,	mask_fn=snake_case_      ,	batch_size=snake_case_      ,	shuffle=snake_case_      ,	)

      lowercase														=			[]
      if args.hub_model_id:
            callbacks.append(
                PushToHubCallback(output_dir=args.output_dir      ,	hub_model_id=args.hub_model_id      ,	tokenizer=snake_case_	)	)

      model.fit(
          snake_case_      ,	validation_data=snake_case_      ,	epochs=args.num_epochs      ,	callbacks=snake_case_      ,	)

      model.save_pretrained(args.output_dir	)


if __name__ == "__main__":
 __lowerCAmelCase					: Any        =parse_args()
 main(args)




"""simple docstring"""






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

import pytest

from .sola import PokerHand

__lowerCAmelCase					: Optional[Any]        =(
    """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)
)

__lowerCAmelCase					: Union[str, Any]        =(
    ("""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"""),
)

__lowerCAmelCase					: Optional[int]        =(
    ("""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),
)

__lowerCAmelCase					: Union[str, Any]        =(
    ("""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),
)

__lowerCAmelCase					: Optional[int]        =(
    ("""2H 4D 3C AS 5S""", True, [5, 4, 3, 2, 1_4]),
    ("""2H 5D 3C AS 5S""", False, [1_4, 5, 5, 3, 2]),
    ("""JH QD KC AS TS""", False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ("""9D 3S 2C 7S 7C""", False, [9, 7, 7, 3, 2]),
)

__lowerCAmelCase					: int        =(
    ("""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),
)

__lowerCAmelCase					: List[str]        =(
    ("""JH AH TH KH QH""", 2_3),
    ("""JH 9H TH KH QH""", 2_2),
    ("""JC KH JS JD JH""", 2_1),
    ("""KH KC 3S 3H 3D""", 2_0),
    ("""8C 9C 5C 3C TC""", 1_9),
    ("""JS QS 9H TS KH""", 1_8),
    ("""7C 7S KH 2H 7H""", 1_7),
    ("""3C KH 5D 5S KH""", 1_6),
    ("""QH 8H KD JH 8S""", 1_5),
    ("""2D 6D 9D TH 7D""", 1_4),
)


def     UpperCAmelCase__ (       )				->					Optional[int]:
      '''simple docstring'''


      lowercase							,							lowercase														=			randrange(len(lowerCAmelCase__	)	), randrange(len(lowerCAmelCase__	)	)
      lowercase														=			["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
      lowercase							,							lowercase														=			SORTED_HANDS[play], SORTED_HANDS[oppo]
      return hand, other, expected


def     UpperCAmelCase__ (       lowerCAmelCase__  :int = 1_0_0	)				->					Optional[Any]:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, expected"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Union[str, Any]	)				->					Tuple:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, expected"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					int:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, expected, card_values"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[str]	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			PokerHand(lowerCAmelCase__	)
      assert player._is_five_high_straight() == expected
      assert player._card_values == card_values


@pytest.mark.parametrize("""hand, expected"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :List[Any]	)				->					str:
      '''simple docstring'''


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


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


      assert PokerHand(lowerCAmelCase__	)._hand_type == expected


@pytest.mark.parametrize("""hand, other, expected"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, other, expected"""      ,	generate_random_hands()	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Union[str, Any]	)				->					Optional[Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       )				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			[PokerHand(lowerCAmelCase__	) for hand in SORTED_HANDS]
      lowercase														=			poker_hands.copy()
      shuffle(lowerCAmelCase__	)
      lowercase														=			chain(sorted(lowerCAmelCase__	)	)
      for index, hand in enumerate(lowerCAmelCase__	):
            assert hand == poker_hands[index]


def     UpperCAmelCase__ (       )				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			[PokerHand("""2D AC 3H 4H 5S"""	), PokerHand("""2S 3H 4H 5S 6C"""	)]
      pokerhands.sort(reverse=lowerCAmelCase__	)
      assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"


def     UpperCAmelCase__ (       )				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			PokerHand("""2C 4S AS 3D 5C"""	)
      lowercase														=			True
      lowercase														=			[5, 4, 3, 2, 1_4]
      for _ in range(1_0	):
            assert pokerhand._is_five_high_straight() == expected
            assert pokerhand._card_values == expected_card_values






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


      lowercase														=			0
      lowercase														=			os.path.abspath(os.path.dirname(lowerCAmelCase__	)	)
      lowercase														=			os.path.join(lowerCAmelCase__      ,	"""poker_hands.txt"""	)
      with open(lowerCAmelCase__	) as file_hand:
            for line in file_hand:
                  lowercase														=			line[:1_4].strip()
                  lowercase														=			line[1_5:].strip()
                  lowercase							,							lowercase														=			PokerHand(lowerCAmelCase__	), PokerHand(lowerCAmelCase__	)
                  lowercase														=			player.compare_with(lowerCAmelCase__	)
                  if output == "Win":
                        answer += 1
      assert answer == 3_7_6



