
'''simple docstring'''
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,
)


a_           =    logging.getLogger(__name__)






class  UpperCAmelCase_					(						snake_case__       ):





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_=None			,							lowercase_=None):
     if current_layer == 0:
         snake_case_			:     List[str]        =    self.embedding_hidden_mapping_in(lowercase_)
     else:
         snake_case_			:     List[str]        =    hidden_states[0]

     snake_case_			:     str        =    int(self.config.num_hidden_layers / self.config.num_hidden_groups)

     # Index of the hidden group
     snake_case_			:     Optional[int]        =    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups))

     snake_case_			:     str        =    self.albert_layer_groups[group_idx](
         lowercase_			,							lowercase_			,							head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]			,							)
     snake_case_			:     int        =    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."""     ,    snake_case__     ,    )
class  UpperCAmelCase_					(						snake_case__       ):





 def __init__(     self			,							lowercase_):
     super().__init__(lowercase_)

     snake_case_			:     List[str]        =    AlbertTransformerWithPabee(lowercase_)

     self.init_weights()
     snake_case_			:     Optional[Any]        =    0
     snake_case_			:     int        =    0
     snake_case_			:     Union[str, Any]        =    0

     snake_case_			:     Optional[int]        =    0





 def 							snake_case__		(     self			,							lowercase_):
     snake_case_			:     str        =    threshold





 def 							snake_case__		(     self			,							lowercase_):
     snake_case_			:     Optional[Any]        =    patience





 def 							snake_case__		(     self):
     snake_case_			:     Any        =    0
     snake_case_			:     str        =    0





 def 							snake_case__		(     self):
     snake_case_			:     Optional[int]        =    self.inference_layers_num / self.inference_instances_num
     snake_case_			:     List[str]        =    (
         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(lowercase_)





 @add_start_docstrings_to_model_forward(lowercase_)
 def 							snake_case__		(     self			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=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:
         snake_case_			:     Optional[Any]        =    input_ids.size()
     elif inputs_embeds is not None:
         snake_case_			:     Any        =    inputs_embeds.size()[:-1]
     else:
         raise ValueError("You have to specify either input_ids or inputs_embeds")

     snake_case_			:     Union[str, Any]        =    input_ids.device if input_ids is not None else inputs_embeds.device

     if attention_mask is None:
         snake_case_			:     int        =    torch.ones(lowercase_			,							device=lowercase_)
     if token_type_ids is None:
         snake_case_			:     Union[str, Any]        =    torch.zeros(lowercase_			,							dtype=torch.long			,							device=lowercase_)

     snake_case_			:     Any        =    attention_mask.unsqueeze(1).unsqueeze(2)
     snake_case_			:     int        =    extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
     snake_case_			:     Tuple        =    (1.0 - extended_attention_mask) * -10_000.0
     snake_case_			:     Optional[int]        =    self.get_head_mask(lowercase_			,							self.config.num_hidden_layers)

     snake_case_			:     Tuple        =    self.embeddings(
         lowercase_			,							position_ids=lowercase_			,							token_type_ids=lowercase_			,							inputs_embeds=lowercase_)
     snake_case_			:     List[str]        =    embedding_output

     if self.training:
         snake_case_			:     Optional[int]        =    []
         for i in range(self.config.num_hidden_layers):
             snake_case_			:     Optional[int]        =    self.encoder.adaptive_forward(
                 lowercase_			,							current_layer=lowercase_			,							attention_mask=lowercase_			,							head_mask=lowercase_			,							)

             snake_case_			:     int        =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
             snake_case_			:     Tuple        =    output_layers[i](output_dropout(lowercase_))
             res.append(lowercase_)
     elif self.patience == 0:  # Use all layers for inference
         snake_case_			:     Tuple        =    self.encoder(lowercase_			,							lowercase_			,							head_mask=lowercase_)
         snake_case_			:     Any        =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
         snake_case_			:     Optional[int]        =    [output_layers[self.config.num_hidden_layers - 1](lowercase_)]
     else:
         snake_case_			:     List[str]        =    0
         snake_case_			:     Dict        =    None
         snake_case_			:     Union[str, Any]        =    0
         for i in range(self.config.num_hidden_layers):
             calculated_layer_num += 1
             snake_case_			:     int        =    self.encoder.adaptive_forward(
                 lowercase_			,							current_layer=lowercase_			,							attention_mask=lowercase_			,							head_mask=lowercase_			,							)

             snake_case_			:     str        =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
             snake_case_			:     str        =    output_layers[i](lowercase_)
             if regression:
                 snake_case_			:     Tuple        =    logits.detach()
                 if patient_result is not None:
                     snake_case_			:     Union[str, Any]        =    patient_result.detach()
                 if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
                     patient_counter += 1
                 else:
                     snake_case_			:     List[str]        =    0
             else:
                 snake_case_			:     List[str]        =    logits.detach().argmax(dim=1)
                 if patient_result is not None:
                     snake_case_			:     List[str]        =    patient_result.detach().argmax(dim=1)
                 if (patient_result is not None) and torch.all(labels.eq(lowercase_)):
                     patient_counter += 1
                 else:
                     snake_case_			:     Dict        =    0

             snake_case_			:     Dict        =    logits
             if patient_counter == self.patience:
                 break
         snake_case_			:     Dict        =    [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. """     ,    snake_case__     ,    )
class  UpperCAmelCase_					(						snake_case__       ):





 def __init__(     self			,							lowercase_):
     super().__init__(lowercase_)
     snake_case_			:     Union[str, Any]        =    config.num_labels

     snake_case_			:     int        =    AlbertModelWithPabee(lowercase_)
     snake_case_			:     Tuple        =    nn.Dropout(config.classifier_dropout_prob)
     snake_case_			:     Optional[int]        =    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(lowercase_)
 def 							snake_case__		(     self			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							lowercase_=None			,							):
     snake_case_			:     Any        =    self.albert(
         input_ids=lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_			,							position_ids=lowercase_			,							head_mask=lowercase_			,							inputs_embeds=lowercase_			,							output_dropout=self.dropout			,							output_layers=self.classifiers			,							regression=self.num_labels == 1			,							)

     snake_case_			:     Union[str, Any]        =    (logits[-1],)

     if labels is not None:
         snake_case_			:     Dict        =    None
         snake_case_			:     Tuple        =    0
         for ix, logits_item in enumerate(lowercase_):
             if self.num_labels == 1:
                 #  We are doing regression
                 snake_case_			:     List[str]        =    MSELoss()
                 snake_case_			:     int        =    loss_fct(logits_item.view(-1)			,							labels.view(-1))
             else:
                 snake_case_			:     Tuple        =    CrossEntropyLoss()
                 snake_case_			:     Optional[Any]        =    loss_fct(logits_item.view(-1			,							self.num_labels)			,							labels.view(-1))
             if total_loss is None:
                 snake_case_			:     int        =    loss
             else:
                 total_loss += loss * (ix + 1)
             total_weights += ix + 1
         snake_case_			:     Optional[int]        =    (total_loss / total_weights,) + outputs

     return outputs






'''simple docstring'''
from __future__ import annotations

import os






class  UpperCAmelCase_					:
 UpperCAmelCase_		     =    (
     """High card""",
     """One pair""",
     """Two pairs""",
     """Three of a kind""",
     """Straight""",
     """Flush""",
     """Full house""",
     """Four of a kind""",
     """Straight flush""",
     """Royal flush""",
 )

 UpperCAmelCase_		     =    (
     """""",  # placeholder as tuples are zero-indexed
     """One""",
     """Two""",
     """Three""",
     """Four""",
     """Five""",
     """Six""",
     """Seven""",
     """Eight""",
     """Nine""",
     """Ten""",
     """Jack""",
     """Queen""",
     """King""",
     """Ace""",
 )





 def __init__(     self			,							lowercase_):
     if not isinstance(lowercase_			,							lowercase_):
         snake_case_			:     Union[str, Any]        =    F'Hand should be of type \'str\': {hand!r}'
         raise TypeError(lowercase_)
     # split removes duplicate whitespaces so no need of strip
     if len(hand.split(" ")) != 5:
         snake_case_			:     Optional[int]        =    F'Hand should contain only 5 cards: {hand!r}'
         raise ValueError(lowercase_)
     snake_case_			:     str        =    hand
     snake_case_			:     str        =    0
     snake_case_			:     List[str]        =    0
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self._internal_state()
     snake_case_			:     List[Any]        =    self._get_hand_type()
     snake_case_			:     Optional[int]        =    self._card_values[0]





 @property
 def 							snake_case__		(     self):
     return self._hand





 def 							snake_case__		(     self			,							lowercase_):
     # 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(lowercase_)
         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):
     snake_case_			:     Tuple        =    PokerHand._HAND_NAME[self._hand_type - 14]
     snake_case_			:     List[Any]        =    PokerHand._CARD_NAME[self._high_card]
     snake_case_			:     Any        =    PokerHand._CARD_NAME[self._first_pair]
     snake_case_			:     str        =    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]:
         snake_case_			:     List[str]        =    "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			,							lowercase_):
     # 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
             snake_case_			:     Optional[int]        =    self._card_values.pop(0)
             self._card_values.append(lowercase_)
         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
     snake_case_			:     Dict        =    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:
                 snake_case_			:     Tuple        =    self._card_values[i]
                 kind += 1
             elif vala == self._card_values[i]:
                 kind += 2
             elif not vala:
                 snake_case_			:     Optional[int]        =    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)
     snake_case_			:     Dict        =    kind + 2 if kind in [4, 5] else kind
     # first meaning first pair to compare in 'compare_with'
     snake_case_			:     Union[str, Any]        =    max(lowercase_			,							lowercase_)
     snake_case_			:     Optional[Any]        =    min(lowercase_			,							lowercase_)
     # 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(lowercase_) != 3:
         snake_case_      ,			snake_case_			:     Dict        =    second, first
     snake_case_			:     Optional[int]        =    first
     snake_case_			:     Dict        =    second
     return kind





 def 							snake_case__		(     self):
     # Internal representation of hand as a list of card values and
     # a set of card suit
     snake_case_			:     dict        =    {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
     snake_case_			:     Tuple        =    self._hand.translate(str.maketrans(lowercase_)).split()
     snake_case_			:     Dict        =    [int(card[:-1]) for card in new_hand]
     snake_case_			:     Tuple        =    {card[-1] for card in new_hand}
     return sorted(lowercase_			,							reverse=lowercase_), card_suit





 def __repr__(     self):
     return F'{self.__class__}("{self._hand}")'





 def __str__(     self):
     return self._hand





 def __eq__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self.compare_with(lowercase_) == "Tie"
     return NotImplemented





 def __lt__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self.compare_with(lowercase_) == "Loss"
     return NotImplemented





 def __le__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self < other or self == other
     return NotImplemented





 def __gt__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return not self < other and self != other
     return NotImplemented





 def __ge__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return not self < other
     return NotImplemented





 def __hash__(     self):
     return object.__hash__(self)


def     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     Dict        =    0
    snake_case_			:     Tuple        =    os.path.abspath(os.path.dirname(__SCREAMING_SNAKE_CASE							)							)
    snake_case_			:     List[Any]        =    os.path.join(__SCREAMING_SNAKE_CASE,		"poker_hands.txt"							)
    with open(__SCREAMING_SNAKE_CASE							) as file_hand:
        for line in file_hand:
            snake_case_			:     int        =    line[:1_4].strip()
            snake_case_			:     Optional[Any]        =    line[1_5:].strip()
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    PokerHand(__SCREAMING_SNAKE_CASE							), PokerHand(__SCREAMING_SNAKE_CASE							)
            snake_case_			:     Optional[int]        =    player.compare_with(__SCREAMING_SNAKE_CASE							)
            if output == "Win":
                answer += 1
    return answer


if __name__ == "__main__":
  solution()




