code: 
  
  
  from __future__ import annotations
  
  import os
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  					__SCREAMING_SNAKE_CASE    				=						(
  					    '''High card''',
  					    '''One pair''',
  					    '''Two pairs''',
  					    '''Three of a kind''',
  					    '''Straight''',
  					    '''Flush''',
  					    '''Full house''',
  					    '''Four of a kind''',
  					    '''Straight flush''',
  					    '''Royal flush''',
  					)
  
  					__SCREAMING_SNAKE_CASE    				=						(
  					    '''''',  # placeholder as tuples are zero-indexed
  					    '''One''',
  					    '''Two''',
  					    '''Three''',
  					    '''Four''',
  					    '''Five''',
  					    '''Six''',
  					    '''Seven''',
  					    '''Eight''',
  					    '''Nine''',
  					    '''Ten''',
  					    '''Jack''',
  					    '''Queen''',
  					    '''King''',
  					    '''Ace''',
  					)
  
  
  					def __init__(     self       ,				A_						):
  							if not isinstance(A_       ,				A_						):
  									_UpperCAmelCase		:       int       =    F'''Hand should be of type \'str\': {hand!r}'''
  									raise TypeError(A_						)
  							# split removes duplicate whitespaces so no need of strip
  							if len(hand.split(""" """						)						) != 5:
  									_UpperCAmelCase		:       List[Any]       =    F'''Hand should contain only 5 cards: {hand!r}'''
  									raise ValueError(A_						)
  							_UpperCAmelCase		:       Optional[int]       =    hand
  							_UpperCAmelCase		:       Union[str, Any]       =    0
  							_UpperCAmelCase		:       Any       =    0
  							_UpperCAmelCase		:       Tuple       =    self._internal_state()
  							_UpperCAmelCase		:       str       =    self._get_hand_type()
  							_UpperCAmelCase		:       Dict       =    self._card_values[0]
  
  
  					@property
  					def 					__snake_case(     self						):
  							return self._hand
  
  
  					def 					__snake_case(     self       ,				A_						):
  							# Breaking the tie works on the following order of precedence:
  							# 1. First pair (default 0)
  							# 2. Second pair (default 0)
  							# 3. Compare all cards in reverse order because they are sorted.
  
  							# First pair and second pair will only be a non-zero value if the card
  							# type is either from the following:
  							# 21: Four of a kind
  							# 20: Full house
  							# 17: Three of a kind
  							# 16: Two pairs
  							# 15: One pair
  							if self._hand_type > other._hand_type:
  									return "Win"
  							elif self._hand_type < other._hand_type:
  									return "Loss"
  							elif self._first_pair == other._first_pair:
  									if self._second_pair == other._second_pair:
  											return self._compare_cards(A_						)
  									else:
  											return "Win" if self._second_pair > other._second_pair else "Loss"
  							return "Win" if self._first_pair > other._first_pair else "Loss"
  
  
  					def 					__snake_case(     self						):
  							_UpperCAmelCase		:       List[Any]       =    PokerHand._HAND_NAME[self._hand_type - 14]
  							_UpperCAmelCase		:       str       =    PokerHand._CARD_NAME[self._high_card]
  							_UpperCAmelCase		:       List[str]       =    PokerHand._CARD_NAME[self._first_pair]
  							_UpperCAmelCase		:       Dict       =    PokerHand._CARD_NAME[self._second_pair]
  							if self._hand_type in [22, 19, 18]:
  									return name + F''', {high}-high'''
  							elif self._hand_type in [21, 17, 15]:
  									return name + F''', {paira}s'''
  							elif self._hand_type in [20, 16]:
  									_UpperCAmelCase		:       int       =    """over""" if self._hand_type == 20 else """and"""
  									return name + F''', {paira}s {join} {paira}s'''
  							elif self._hand_type == 23:
  									return name
  							else:
  									return name + F''', {high}'''
  
  
  					def 					__snake_case(     self       ,				A_						):
  							# Enumerate gives us the index as well as the element of a list
  							for index, card_value in enumerate(self._card_values						):
  									if card_value != other._card_values[index]:
  											return "Win" if card_value > other._card_values[index] else "Loss"
  							return "Tie"
  
  
  					def 					__snake_case(     self						):
  							# Number representing the type of hand internally:
  							# 23: Royal flush
  							# 22: Straight flush
  							# 21: Four of a kind
  							# 20: Full house
  							# 19: Flush
  							# 18: Straight
  							# 17: Three of a kind
  							# 16: Two pairs
  							# 15: One pair
  							# 14: High card
  							if self._is_flush():
  									if self._is_five_high_straight() or self._is_straight():
  											return 23 if sum(self._card_values						) == 60 else 22
  									return 19
  							elif self._is_five_high_straight() or self._is_straight():
  									return 18
  							return 14 + self._is_same_kind()
  
  
  					def 					__snake_case(     self						):
  							return len(self._card_suit						) == 1
  
  
  					def 					__snake_case(     self						):
  							# If a card is a five high straight (low ace) change the location of
  							# ace from the start of the list to the end. Check whether the first
  							# element is ace or not. (Don't want to change again)
  							# Five high straight (low ace): AH 2H 3S 4C 5D
  							# Why use sorted here? One call to this function will mutate the list to
  							# [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
  							# need to compare the sorted version.
  							# Refer test_multiple_calls_five_high_straight in test_poker_hand.py
  							if sorted(self._card_values						) == [2, 3, 4, 5, 14]:
  									if self._card_values[0] == 14:
  											# Remember, our list is sorted in reverse order
  											_UpperCAmelCase		:       Dict       =    self._card_values.pop(0						)
  											self._card_values.append(A_						)
  									return True
  							return False
  
  
  					def 					__snake_case(     self						):
  							for i in range(4						):
  									if self._card_values[i] - self._card_values[i + 1] != 1:
  											return False
  							return True
  
  
  					def 					__snake_case(     self						):
  							# Kind Values for internal use:
  							# 7: Four of a kind
  							# 6: Full house
  							# 3: Three of a kind
  							# 2: Two pairs
  							# 1: One pair
  							# 0: False
  							_UpperCAmelCase		:       Any       =    0
  							for i in range(4						):
  									# Compare two cards at a time, if they are same increase 'kind',
  									# add the value of the card to val1, if it is repeating again we
  									# will add 2 to 'kind' as there are now 3 cards with same value.
  									# If we get card of different value than val1, we will do the same
  									# thing with val2
  									if self._card_values[i] == self._card_values[i + 1]:
  											if not vala:
  													_UpperCAmelCase		:       Optional[Any]       =    self._card_values[i]
  													kind += 1
  											elif vala == self._card_values[i]:
  													kind += 2
  											elif not vala:
  													_UpperCAmelCase		:       Any       =    self._card_values[i]
  													kind += 1
  											elif vala == self._card_values[i]:
  													kind += 2
          # For consistency in hand type (look at note in _get_hand_type function)
  							_UpperCAmelCase		:       List[Any]       =    kind + 2 if kind in [4, 5] else kind
  							# first meaning first pair to compare in 'compare_with'
  							_UpperCAmelCase		:       Optional[int]       =    max(A_       ,				A_						)
  							_UpperCAmelCase		:       List[str]       =    min(A_       ,				A_						)
  							# If it's full house (three count pair + two count pair), make sure
  							# first pair is three count and if not then switch them both.
  							if kind == 6 and self._card_values.count(A_						) != 3:
  									_UpperCAmelCase		:       List[str]       =    second, first
  							_UpperCAmelCase		:       Optional[int]       =    first
  							_UpperCAmelCase		:       str       =    second
  							return kind
  
  
  					def 					__snake_case(     self						):
  							# Internal representation of hand as a list of card values and
  							# a set of card suit
  							_UpperCAmelCase		:       dict       =    {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
  							_UpperCAmelCase		:       str       =    self._hand.translate(str.maketrans(A_						)						).split()
  							_UpperCAmelCase		:       Tuple       =    [int(card[:-1]						) for card in new_hand]
  							_UpperCAmelCase		:       str       =    {card[-1] for card in new_hand}
  							return sorted(A_       ,				reverse=A_						), card_suit
  
  
  					def __repr__(     self						):
  							return F'''{self.__class__}("{self._hand}")'''
  
  
  					def __str__(     self						):
  							return self._hand
  
  
  					def __eq__(     self       ,				A_						):
  							if isinstance(A_       ,				A_						):
  									return self.compare_with(A_						) == "Tie"
  							return NotImplemented
  
  
  					def __lt__(     self       ,				A_						):
  							if isinstance(A_       ,				A_						):
  									return self.compare_with(A_						) == "Loss"
  							return NotImplemented
  
  
  					def __le__(     self       ,				A_						):
  							if isinstance(A_       ,				A_						):
  									return self < other or self == other
  							return NotImplemented
  
  
  					def __gt__(     self       ,				A_						):
  							if isinstance(A_       ,				A_						):
  									return not self < other and self != other
  							return NotImplemented
  
  
  					def __ge__(     self       ,				A_						):
  							if isinstance(A_       ,				A_						):
  									return not self < other
  							return NotImplemented
  
  
  
  
  					def __hash__(     self						):
  							return object.__hash__(self						)
  
  
  
  
  def       a__						(		):
  		# Solution for problem number 54 from Project Euler
  		# Input from poker_hands.txt file
  		_UpperCAmelCase		:       Optional[int]       =    0
  		_UpperCAmelCase		:       Optional[int]       =    os.path.abspath(os.path.dirname(snake_case__    )    )
  		_UpperCAmelCase		:       List[Any]       =    os.path.join(snake_case__	,      """poker_hands.txt"""    )
  		with open(snake_case__    ) as file_hand:
  				for line in file_hand:
  						_UpperCAmelCase		:       List[Any]       =    line[:14].strip()
  						_UpperCAmelCase		:       Optional[Any]       =    line[15:].strip()
  						_UpperCAmelCase		:       Any       =    PokerHand(snake_case__    ), PokerHand(snake_case__    )
  						_UpperCAmelCase		:       Optional[Any]       =    player.compare_with(snake_case__    )
  						if output == "Win":
  								answer += 1
  		return answer
  
  
  if __name__ == "__main__":
  	solution()
  
  
  
  
  
  
style_context: 
  
  
  import logging
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.albert.modeling_albert import (
      ALBERT_INPUTS_DOCSTRING,
      ALBERT_START_DOCSTRING,
      AlbertModel,
      AlbertPreTrainedModel,
      AlbertTransformer,
  )
  
  
  SCREAMING_SNAKE_CASE__				:    Dict         =    logging.getLogger(__name__)
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_=None       ,				A_=None						):
         if current_layer == 0:
           _UpperCAmelCase		:       Any       =    self.embedding_hidden_mapping_in(A_						)
         else:
           _UpperCAmelCase		:       Optional[int]       =    hidden_states[0]
  
         _UpperCAmelCase		:       Tuple       =    int(self.config.num_hidden_layers / self.config.num_hidden_groups						)
  
         # Index of the hidden group
         _UpperCAmelCase		:       Union[str, Any]       =    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)
  
         _UpperCAmelCase		:       int       =    self.albert_layer_groups[group_idx](
             A_       ,				A_       ,				head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]       ,				)
         _UpperCAmelCase		:       Dict       =    layer_group_output[0]
  
         return (hidden_states,)
  
  
  @add_start_docstrings(
      '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''       ,  A       ,  )
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_						):
         super().__init__(A_						)
  
         _UpperCAmelCase		:       List[Any]       =    AlbertTransformerWithPabee(A_						)
  
         self.init_weights()
         _UpperCAmelCase		:       Tuple       =    0
         _UpperCAmelCase		:       Optional[Any]       =    0
         _UpperCAmelCase		:       int       =    0
  
         _UpperCAmelCase		:       Dict       =    0
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       str       =    threshold
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       Optional[Any]       =    patience
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    0
         _UpperCAmelCase		:       Tuple       =    0
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Dict       =    self.inference_layers_num / self.inference_instances_num
         _UpperCAmelCase		:       Optional[int]       =    (
             F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
             F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
         )
         print(A_						)
  
  
  
  
       @add_start_docstrings_to_model_forward(A_						)
       def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=False       ,				):
  
         if input_ids is not None and inputs_embeds is not None:
           raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""						)
         elif input_ids is not None:
           _UpperCAmelCase		:       List[str]       =    input_ids.size()
         elif inputs_embeds is not None:
           _UpperCAmelCase		:       Optional[Any]       =    inputs_embeds.size()[:-1]
         else:
           raise ValueError("""You have to specify either input_ids or inputs_embeds"""						)
  
         _UpperCAmelCase		:       List[str]       =    input_ids.device if input_ids is not None else inputs_embeds.device
  
         if attention_mask is None:
           _UpperCAmelCase		:       Any       =    torch.ones(A_       ,				device=A_						)
         if token_type_ids is None:
           _UpperCAmelCase		:       List[str]       =    torch.zeros(A_       ,				dtype=torch.long       ,				device=A_						)
  
         _UpperCAmelCase		:       str       =    attention_mask.unsqueeze(1						).unsqueeze(2						)
         _UpperCAmelCase		:       Tuple       =    extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
         _UpperCAmelCase		:       Tuple       =    (1.0 - extended_attention_mask) * -1_0_0_0_0.0
         _UpperCAmelCase		:       Union[str, Any]       =    self.get_head_mask(A_       ,				self.config.num_hidden_layers						)
  
         _UpperCAmelCase		:       Dict       =    self.embeddings(
             A_       ,				position_ids=A_       ,				token_type_ids=A_       ,				inputs_embeds=A_						)
         _UpperCAmelCase		:       str       =    embedding_output
  
         if self.training:
           _UpperCAmelCase		:       Any       =    []
           for i in range(self.config.num_hidden_layers						):
             _UpperCAmelCase		:       Tuple       =    self.encoder.adaptive_forward(
                 A_       ,				current_layer=A_       ,				attention_mask=A_       ,				head_mask=A_       ,				)
  
             _UpperCAmelCase		:       List[Any]       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
             _UpperCAmelCase		:       List[str]       =    output_layers[i](output_dropout(A_						)						)
             res.append(A_						)
         elif self.patience == 0:  # Use all layers for inference
           _UpperCAmelCase		:       Optional[int]       =    self.encoder(A_       ,				A_       ,				head_mask=A_						)
           _UpperCAmelCase		:       Optional[int]       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
           _UpperCAmelCase		:       List[Any]       =    [output_layers[self.config.num_hidden_layers - 1](A_						)]
         else:
           _UpperCAmelCase		:       str       =    0
           _UpperCAmelCase		:       str       =    None
           _UpperCAmelCase		:       str       =    0
           for i in range(self.config.num_hidden_layers						):
             calculated_layer_num += 1
             _UpperCAmelCase		:       int       =    self.encoder.adaptive_forward(
                 A_       ,				current_layer=A_       ,				attention_mask=A_       ,				head_mask=A_       ,				)
  
             _UpperCAmelCase		:       Tuple       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
             _UpperCAmelCase		:       List[str]       =    output_layers[i](A_						)
             if regression:
               _UpperCAmelCase		:       Optional[Any]       =    logits.detach()
               if patient_result is not None:
                 _UpperCAmelCase		:       str       =    patient_result.detach()
               if (patient_result is not None) and torch.abs(patient_result - labels						) < self.regression_threshold:
                 patient_counter += 1
               else:
                 _UpperCAmelCase		:       Union[str, Any]       =    0
             else:
               _UpperCAmelCase		:       Dict       =    logits.detach().argmax(dim=1						)
               if patient_result is not None:
                 _UpperCAmelCase		:       Optional[Any]       =    patient_result.detach().argmax(dim=1						)
               if (patient_result is not None) and torch.all(labels.eq(A_						)						):
                 patient_counter += 1
               else:
                 _UpperCAmelCase		:       Dict       =    0
  
             _UpperCAmelCase		:       int       =    logits
             if patient_counter == self.patience:
               break
           _UpperCAmelCase		:       Tuple       =    [patient_result]
           self.inference_layers_num += calculated_layer_num
           self.inference_instances_num += 1
  
         return res
  
  
  @add_start_docstrings(
      '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
      the pooled output) e.g. for GLUE tasks. '''       ,  A       ,  )
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_						):
         super().__init__(A_						)
         _UpperCAmelCase		:       List[str]       =    config.num_labels
  
         _UpperCAmelCase		:       List[Any]       =    AlbertModelWithPabee(A_						)
         _UpperCAmelCase		:       Optional[int]       =    nn.Dropout(config.classifier_dropout_prob						)
         _UpperCAmelCase		:       Tuple       =    nn.ModuleList(
             [nn.Linear(config.hidden_size       ,				self.config.num_labels						) for _ in range(config.num_hidden_layers						)]						)
  
         self.init_weights()
  
  
  
  
       @add_start_docstrings_to_model_forward(A_						)
       def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				A_=None       ,				):
         _UpperCAmelCase		:       Optional[Any]       =    self.albert(
             input_ids=A_       ,				attention_mask=A_       ,				token_type_ids=A_       ,				position_ids=A_       ,				head_mask=A_       ,				inputs_embeds=A_       ,				output_dropout=self.dropout       ,				output_layers=self.classifiers       ,				regression=self.num_labels == 1       ,				)
  
         _UpperCAmelCase		:       str       =    (logits[-1],)
  
         if labels is not None:
           _UpperCAmelCase		:       List[Any]       =    None
           _UpperCAmelCase		:       Optional[int]       =    0
           for ix, logits_item in enumerate(A_						):
             if self.num_labels == 1:
               #  We are doing regression
               _UpperCAmelCase		:       int       =    MSELoss()
               _UpperCAmelCase		:       Union[str, Any]       =    loss_fct(logits_item.view(-1						)       ,				labels.view(-1						)						)
             else:
               _UpperCAmelCase		:       List[Any]       =    CrossEntropyLoss()
               _UpperCAmelCase		:       List[Any]       =    loss_fct(logits_item.view(-1       ,				self.num_labels						)       ,				labels.view(-1						)						)
             if total_loss is None:
               _UpperCAmelCase		:       List[str]       =    loss
             else:
               total_loss += loss * (ix + 1)
             total_weights += ix + 1
           _UpperCAmelCase		:       List[str]       =    (total_loss / total_weights,) + outputs
  
         return outputs
  
  
  
  
  
  