


'''simple docstring'''

import torch
from torch import nn
class   __lowerCAmelCase						(nn.Module     ):





      '''simple docstring'''






      def __init__(self    :    Optional[Any]   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    int=1   ,						UpperCamelCase    :    List[str]=False ):

             '''simple docstring'''







             super().__init__()

             lowercase__      			= n_token
             lowercase__      			= d_embed
             lowercase__      			= d_proj

             lowercase__      			= cutoffs + [n_token]
             lowercase__      			= [0] + self.cutoffs
             lowercase__      			= div_val

             lowercase__      			= self.cutoffs[0]
             lowercase__      			= len(self.cutoffs ) - 1
             lowercase__      			= self.shortlist_size + self.n_clusters

             if self.n_clusters > 0:
                    lowercase__      			= nn.Parameter(torch.zeros(self.n_clusters   ,						self.d_embed ) )
                    lowercase__      			= nn.Parameter(torch.zeros(self.n_clusters ) )

             lowercase__      			= nn.ModuleList()
             lowercase__      			= nn.ParameterList()

             if div_val == 1:
                    for i in range(len(self.cutoffs ) ):
                           if d_proj != d_embed:
                                  self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase   ,						UpperCamelCase ) ) )
                           else:
                                  self.out_projs.append(UpperCamelCase )

                    self.out_layers.append(nn.Linear(UpperCamelCase   ,						UpperCamelCase ) )
             else:
                    for i in range(len(self.cutoffs ) ):
                           lowercase__			,lowercase__      			= self.cutoff_ends[i], self.cutoff_ends[i + 1]
                           lowercase__      			= d_embed // (div_val**i)

                           self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase   ,						UpperCamelCase ) ) )

                           self.out_layers.append(nn.Linear(UpperCamelCase   ,						r_idx - l_idx ) )

             lowercase__      			= keep_order



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             if proj is None:
                    lowercase__      			= nn.functional.linear(UpperCamelCase   ,						UpperCamelCase   ,						bias=UpperCamelCase )
             else:
                    # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
                    lowercase__      			= nn.functional.linear(UpperCamelCase   ,						proj.t().contiguous() )
                    lowercase__      			= nn.functional.linear(UpperCamelCase   ,						UpperCamelCase   ,						bias=UpperCamelCase )
                    # else:
                    #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
                    #     if bias is not None:
                    #         logit = logit + bias

             return logit



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    List[str]=None   ,						UpperCamelCase    :    Tuple=False ):

             '''simple docstring'''







             if labels is not None:
                    # Shift so that tokens < n predict n
                    lowercase__      			= hidden[..., :-1, :].contiguous()
                    lowercase__      			= labels[..., 1:].contiguous()
                    lowercase__      			= hidden.view(-1   ,						hidden.size(-1 ) )
                    lowercase__      			= labels.view(-1 )
                    if hidden.size(0 ) != labels.size(0 ):
                           raise RuntimeError('''Input and labels should have the same size in the batch dimension.''' )
             else:
                    lowercase__      			= hidden.view(-1   ,						hidden.size(-1 ) )

             if self.n_clusters == 0:
                    lowercase__      			= self._compute_logit(UpperCamelCase   ,						self.out_layers[0].weight   ,						self.out_layers[0].bias   ,						self.out_projs[0] )
                    if labels is not None:
                           lowercase__      			= labels != -100
                           lowercase__      			= torch.zeros_like(UpperCamelCase   ,						dtype=hidden.dtype   ,						device=hidden.device )
                           lowercase__      			= (
                               -nn.functional.log_softmax(UpperCamelCase   ,						dim=-1 )[mask].gather(1   ,						labels[mask].unsqueeze(1 ) ).squeeze(1 )
                           )
                    else:
                           lowercase__      			= nn.functional.log_softmax(UpperCamelCase   ,						dim=-1 )
             else:
                    # construct weights and biases
                    lowercase__			,lowercase__      			= [], []
                    for i in range(len(self.cutoffs ) ):
                           if self.div_val == 1:
                                  lowercase__			,lowercase__      			= self.cutoff_ends[i], self.cutoff_ends[i + 1]
                                  lowercase__      			= self.out_layers[0].weight[l_idx:r_idx]
                                  lowercase__      			= self.out_layers[0].bias[l_idx:r_idx]
                           else:
                                  lowercase__      			= self.out_layers[i].weight
                                  lowercase__      			= self.out_layers[i].bias

                           if i == 0:
                                  lowercase__      			= torch.cat([weight_i, self.cluster_weight]   ,						dim=0 )
                                  lowercase__      			= torch.cat([bias_i, self.cluster_bias]   ,						dim=0 )

                           weights.append(UpperCamelCase )
                           biases.append(UpperCamelCase )

                    lowercase__			,lowercase__			,lowercase__      			= weights[0], biases[0], self.out_projs[0]

                    lowercase__      			= self._compute_logit(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
                    lowercase__      			= nn.functional.log_softmax(UpperCamelCase   ,						dim=1 )

                    if labels is None:
                           lowercase__      			= hidden.new_empty((head_logit.size(0 ), self.n_token) )
                    else:
                           lowercase__      			= torch.zeros_like(UpperCamelCase   ,						dtype=hidden.dtype   ,						device=hidden.device )

                    lowercase__      			= 0
                    lowercase__      			= [0] + self.cutoffs
                    for i in range(len(UpperCamelCase ) - 1 ):
                           lowercase__			,lowercase__      			= cutoff_values[i], cutoff_values[i + 1]

                           if labels is not None:
                                  lowercase__      			= (labels >= l_idx) & (labels < r_idx)
                                  lowercase__      			= mask_i.nonzero().squeeze()

                                  if indices_i.numel() == 0:
                                         continue

                                  lowercase__      			= labels.index_select(0   ,						UpperCamelCase ) - l_idx
                                  lowercase__      			= head_logprob.index_select(0   ,						UpperCamelCase )
                                  lowercase__      			= hidden.index_select(0   ,						UpperCamelCase )
                           else:
                                  lowercase__      			= hidden

                           if i == 0:
                                  if labels is not None:
                                         lowercase__      			= head_logprob_i.gather(1   ,						target_i[:, None] ).squeeze(1 )
                                  else:
                                         lowercase__      			= head_logprob[:, : self.cutoffs[0]]
                           else:
                                  lowercase__			,lowercase__			,lowercase__      			= weights[i], biases[i], self.out_projs[i]

                                  lowercase__      			= self._compute_logit(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
                                  lowercase__      			= nn.functional.log_softmax(UpperCamelCase   ,						dim=1 )
                                  lowercase__      			= self.cutoffs[0] + i - 1  # No probability for the head cluster
                                  if labels is not None:
                                         lowercase__      			= head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                                             1   ,						target_i[:, None] ).squeeze(1 )
                                  else:
                                         lowercase__      			= head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                                         lowercase__      			= logprob_i

                           if labels is not None:
                                  if (hasattr(self   ,						'''keep_order''' ) and self.keep_order) or keep_order:
                                         out.index_copy_(0   ,						UpperCamelCase   ,						-logprob_i )
                                  else:
                                         out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
                                  offset += logprob_i.size(0 )

             return out






      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    Tuple ):

             '''simple docstring'''







             if self.n_clusters == 0:
                    lowercase__      			= self._compute_logit(UpperCamelCase   ,						self.out_layers[0].weight   ,						self.out_layers[0].bias   ,						self.out_projs[0] )
                    return nn.functional.log_softmax(UpperCamelCase   ,						dim=-1 )
             else:
                    # construct weights and biases
                    lowercase__			,lowercase__      			= [], []
                    for i in range(len(self.cutoffs ) ):
                           if self.div_val == 1:
                                  lowercase__			,lowercase__      			= self.cutoff_ends[i], self.cutoff_ends[i + 1]
                                  lowercase__      			= self.out_layers[0].weight[l_idx:r_idx]
                                  lowercase__      			= self.out_layers[0].bias[l_idx:r_idx]
                           else:
                                  lowercase__      			= self.out_layers[i].weight
                                  lowercase__      			= self.out_layers[i].bias

                           if i == 0:
                                  lowercase__      			= torch.cat([weight_i, self.cluster_weight]   ,						dim=0 )
                                  lowercase__      			= torch.cat([bias_i, self.cluster_bias]   ,						dim=0 )

                           weights.append(UpperCamelCase )
                           biases.append(UpperCamelCase )

                    lowercase__			,lowercase__			,lowercase__      			= weights[0], biases[0], self.out_projs[0]
                    lowercase__      			= self._compute_logit(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )

                    lowercase__      			= hidden.new_empty((head_logit.size(0 ), self.n_token) )
                    lowercase__      			= nn.functional.log_softmax(UpperCamelCase   ,						dim=1 )

                    lowercase__      			= [0] + self.cutoffs
                    for i in range(len(UpperCamelCase ) - 1 ):
                           lowercase__			,lowercase__      			= cutoff_values[i], cutoff_values[i + 1]

                           if i == 0:
                                  lowercase__      			= head_logprob[:, : self.cutoffs[0]]
                           else:
                                  lowercase__			,lowercase__			,lowercase__      			= weights[i], biases[i], self.out_projs[i]

                                  lowercase__      			= self._compute_logit(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
                                  lowercase__      			= nn.functional.log_softmax(UpperCamelCase   ,						dim=1 )

                                  lowercase__      			= head_logprob[:, -i] + tail_logprob_i
                                  lowercase__      			= logprob_i

                    return out








'''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							:   Tuple  						=	(
    ('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							:   Dict  						=	(
    ('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							:   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							:   Tuple  						=	(
    ('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]),
)

lowerCamelCase							:   Optional[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							:   Dict  						=	(
    ('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 _SCREAMING_SNAKE_CASE      ()       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__			,lowercase__      			= randrange(len(A       )       ), randrange(len(A       )       )
       lowercase__      			= ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
       lowercase__			,lowercase__      			= SORTED_HANDS[play], SORTED_HANDS[oppo]
       return hand, other, expected

def _SCREAMING_SNAKE_CASE      (A = 100       )       ->       str:






       """simple docstring"""







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

@pytest.mark.parametrize('''hand, expected'''  , A       )
def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       List[str]:






       """simple docstring"""







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

@pytest.mark.parametrize('''hand, expected'''  , A       )
def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Union[str, Any]:






       """simple docstring"""







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

@pytest.mark.parametrize('''hand, expected, card_values'''  , A       )
def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Any:






       """simple docstring"""







       lowercase__      			= PokerHand(A       )
       assert player._is_five_high_straight() == expected
       assert player._card_values == card_values

@pytest.mark.parametrize('''hand, expected'''  , A       )
def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Tuple:






       """simple docstring"""







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

@pytest.mark.parametrize('''hand, expected'''  , A       )
def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Optional[Any]:






       """simple docstring"""







       assert PokerHand(A       )._hand_type == expected

@pytest.mark.parametrize('''hand, other, expected'''  , A       )
def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Union[str, Any]:






       """simple docstring"""







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

@pytest.mark.parametrize('''hand, other, expected'''  , generate_random_hands()       )
def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Optional[Any]:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      ()       ->       Tuple:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      ()       ->       List[Any]:






       """simple docstring"""







       lowercase__      			= [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 _SCREAMING_SNAKE_CASE      ()       ->       int:






       """simple docstring"""







       lowercase__      			= PokerHand('''2C 4S AS 3D 5C'''       )
       lowercase__      			= True
       lowercase__      			= [5, 4, 3, 2, 14]
       for _ in range(10       ):
              assert pokerhand._is_five_high_straight() == expected
              assert pokerhand._card_values == expected_card_values

def _SCREAMING_SNAKE_CASE      ()       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= 0
       lowercase__      			= os.path.abspath(os.path.dirname(A       )       )
       lowercase__      			= os.path.join(A  , '''poker_hands.txt'''       )
       with open(A       ) as file_hand:
              for line in file_hand:
                     lowercase__      			= line[:14].strip()
                     lowercase__      			= line[15:].strip()
                     lowercase__			,lowercase__      			= PokerHand(A       ), PokerHand(A       )
                     lowercase__      			= player.compare_with(A       )
                     if output == "Win":
                            answer += 1
       assert answer == 376




