






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

import pytest

from .sola import PokerHand

a						:			str     	=					(
    "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)
)

a						:			str     	=					(
    ("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"),
)

a						:			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),
)

a						:			str     	=					(
    ("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),
)

a						:			List[str]     	=					(
    ("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]),
)

a						:			List[str]     	=					(
    ("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),
)

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






def 							lowerCamelCase__ (       __lowerCamelCase     :	int = 100						):
  return (generate_random_hand() for _ in range(__lowerCamelCase						))






@pytest.mark.parametrize("""hand, expected"""    ,						__lowerCamelCase						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	int						):
  assert PokerHand(__lowerCamelCase						)._is_flush() == expected






@pytest.mark.parametrize("""hand, expected"""    ,						__lowerCamelCase						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Tuple						):
  assert PokerHand(__lowerCamelCase						)._is_straight() == expected






@pytest.mark.parametrize("""hand, expected, card_values"""    ,						__lowerCamelCase						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	str    ,						__lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	Optional[int]						):
  __UpperCAmelCase				:      int	 =    PokerHand(__lowerCamelCase						)
  assert player._is_five_high_straight() == expected
  assert player._card_values == card_values






@pytest.mark.parametrize("""hand, expected"""    ,						__lowerCamelCase						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Union[str, Any]    ,						__lowerCamelCase     :	List[str]						):
  assert PokerHand(__lowerCamelCase						)._is_same_kind() == expected






@pytest.mark.parametrize("""hand, expected"""    ,						__lowerCamelCase						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	Tuple						):
  assert PokerHand(__lowerCamelCase						)._hand_type == expected






@pytest.mark.parametrize("""hand, other, expected"""    ,						__lowerCamelCase						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	int    ,						__lowerCamelCase     :	List[str]    ,						__lowerCamelCase     :	List[str]						):
  assert PokerHand(__lowerCamelCase						).compare_with(PokerHand(__lowerCamelCase						)						) == expected






@pytest.mark.parametrize("""hand, other, expected"""    ,						generate_random_hands()						)
def 							lowerCamelCase__ (       __lowerCamelCase     :	str    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	Optional[int]						):
  assert PokerHand(__lowerCamelCase						).compare_with(PokerHand(__lowerCamelCase						)						) == expected






def 							lowerCamelCase__ (       ):
  __UpperCAmelCase				:      Optional[Any]	 =    [PokerHand(__lowerCamelCase						) for hand in SORTED_HANDS]
  __UpperCAmelCase				:      Any	 =    poker_hands.copy()
  shuffle(__lowerCamelCase						)
  __UpperCAmelCase				:      str	 =    chain(sorted(__lowerCamelCase						)						)
  for index, hand in enumerate(__lowerCamelCase						):
    assert hand == poker_hands[index]






def 							lowerCamelCase__ (       ):
  # Test that five high straights are compared correctly.
  __UpperCAmelCase				:      Any	 =    [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 							lowerCamelCase__ (       ):
  # Multiple calls to five_high_straight function should still return True
  # and shouldn't mutate the list in every call other than the first.
  __UpperCAmelCase				:      List[Any]	 =    PokerHand("""2C 4S AS 3D 5C"""						)
  __UpperCAmelCase				:      str	 =    True
  __UpperCAmelCase				:      Dict	 =    [5, 4, 3, 2, 14]
  for _ in range(10						):
    assert pokerhand._is_five_high_straight() == expected
    assert pokerhand._card_values == expected_card_values







def 							lowerCamelCase__ (       ):
  # Problem number 54 from Project Euler
  # Testing from poker_hands.txt file
  __UpperCAmelCase				:      Tuple	 =    0
  __UpperCAmelCase				:      Tuple	 =    os.path.abspath(os.path.dirname(__lowerCamelCase						)						)
  __UpperCAmelCase				:      Dict	 =    os.path.join(__lowerCamelCase    ,						"""poker_hands.txt"""						)
  with open(__lowerCamelCase						) as file_hand:
    for line in file_hand:
      __UpperCAmelCase				:      Optional[Any]	 =    line[:14].strip()
      __UpperCAmelCase				:      List[Any]	 =    line[15:].strip()
      __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    PokerHand(__lowerCamelCase						), PokerHand(__lowerCamelCase						)
      __UpperCAmelCase				:      Dict	 =    player.compare_with(__lowerCamelCase						)
      if output == "Win":
        answer += 1
  assert answer == 376








"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
a							      =     logging.get_logger(__name__)

a							      =     [
    ('''bert.bert''', '''visual_bert'''),
    ('''bert.cls''', '''cls'''),
    ('''bert.classifier''', '''cls'''),
    ('''token_type_embeddings_visual''', '''visual_token_type_embeddings'''),
    ('''position_embeddings_visual''', '''visual_position_embeddings'''),
    ('''projection''', '''visual_projection'''),
]

a							      =     [
    '''nlvr2_coco_pre_trained.th''',
    '''nlvr2_fine_tuned.th''',
    '''nlvr2_pre_trained.th''',
    '''vcr_coco_pre_train.th''',
    '''vcr_fine_tune.th''',
    '''vcr_pre_train.th''',
    '''vqa_coco_pre_trained.th''',
    '''vqa_fine_tuned.th''',
    '''vqa_pre_trained.th''',
]






def 		_snake_case	(   _snake_case				:   Optional[Any]				)       ->   str:






      '''simple docstring'''


      _A     =  torch.load(_snake_case							,    map_location='cpu'				)
      return sd






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   str							,    _snake_case				:   Tuple=rename_keys_prefix				)       ->   List[str]:






      '''simple docstring'''


      _A     =  OrderedDict()
      _A     =  torch.arange(config.max_position_embeddings				).expand((1, -1)				)
      # detector_d = OrderedDict()
      for key in d:
            if "detector" in key:
                  # detector_d[key.replace('detector.','')] = d[key]
                  continue
            _A     =  key
            for name_pair in rename_keys_prefix:
                  _A     =  new_key.replace(name_pair[0]							,    name_pair[1]				)
            _A     =  d[key]
            if key == "bert.cls.predictions.decoder.weight":
                  # Old bert code didn't have `decoder.bias`, but was added separately
                  _A     =  new_d['cls.predictions.bias']
      return new_d






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   Dict				)       ->   Dict:






      '''simple docstring'''


      assert (
          checkpoint_path.split('/'				)[-1] in ACCEPTABLE_CHECKPOINTS
      ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

      # Get Config
      if "pre" in checkpoint_path:
            _A     =  'pretraining'
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "nlvr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 10_24}
            else:
                  raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''				)
      else:
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
                  _A     =  'multichoice'
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
                  _A     =  'vqa_advanced'
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48, 'num_labels': 31_29}
                  _A     =  'vqa'
            elif "nlvr" in checkpoint_path:
                  _A     =  {
                      'visual_embedding_dim': 10_24,
                      'num_labels': 2,
                  }
                  _A     =  'nlvr'

      _A     =  VisualBertConfig(**_snake_case				)

      # Load State Dict
      _A     =  load_state_dict(_snake_case				)

      _A     =  get_new_dict(_snake_case							,    _snake_case				)

      if model_type == "pretraining":
            _A     =  VisualBertForPreTraining(_snake_case				)
      elif model_type == "vqa":
            _A     =  VisualBertForQuestionAnswering(_snake_case				)
      elif model_type == "nlvr":
            _A     =  VisualBertForVisualReasoning(_snake_case				)
      elif model_type == "multichoice":
            _A     =  VisualBertForMultipleChoice(_snake_case				)

      model.load_state_dict(_snake_case				)
      # Save Checkpoints
      Path(_snake_case				).mkdir(exist_ok=_snake_case				)
      model.save_pretrained(_snake_case				)


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''orig_checkpoint_path''', type=str, help='''A path to .th on local filesystem.''')
       parser.add_argument('''pytorch_dump_folder_path''', type=str, help='''Path to the output PyTorch model.''')
       a							      =     parser.parse_args()
       convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)

