
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class 						__lowercase:


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42

       # setable values
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							None



       @classmethod
       def SCREAMING_SNAKE_CASE_				(     cls					:			Dict  ,      _lowerCAmelCase					:			CommonSchedulerState  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			jnp.ndarray  ,      )					->   List[Any]:
         return cls(
             common=_lowerCAmelCase  ,      final_alpha_cumprod=_lowerCAmelCase  ,      init_noise_sigma=_lowerCAmelCase  ,      timesteps=_lowerCAmelCase  ,      )





@dataclass
class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							42





class 						__lowercase(     SCREAMING_SNAKE_CASE		,  SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							[e.name for e in FlaxKarrasDiffusionSchedulers]

       UpperCamelCase_				 =							42



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Dict:
         return True



       @register_to_config
       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			int = 1000  ,      _lowerCAmelCase					:			float = 0.0001  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			str = "linear"  ,      _lowerCAmelCase					:			Optional[jnp.ndarray] = None  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 0  ,      _lowerCAmelCase					:			str = "epsilon"  ,      _lowerCAmelCase					:			jnp.dtype = jnp.floataa  ,      )					->   Dict:
         _lowerCAmelCase						=		dtype



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			Optional[CommonSchedulerState] = None       )					->   DDIMSchedulerState:
         if common is None:
           _lowerCAmelCase						=		CommonSchedulerState.create(self       )

         # At every step in ddim, we are looking into the previous alphas_cumprod
         # For the final step, there is no previous alphas_cumprod because we are already at 0
         # `set_alpha_to_one` decides whether we set this parameter simply to one or
         # whether we use the final alpha of the "non-previous" one.
         _lowerCAmelCase						=		(
             jnp.array(1.0  ,      dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
         )

         # standard deviation of the initial noise distribution
         _lowerCAmelCase						=		jnp.array(1.0  ,      dtype=self.dtype       )

         _lowerCAmelCase						=		jnp.arange(0  ,      self.config.num_train_timesteps       ).round()[::-1]

         return DDIMSchedulerState.create(
             common=_lowerCAmelCase  ,      final_alpha_cumprod=_lowerCAmelCase  ,      init_noise_sigma=_lowerCAmelCase  ,      timesteps=_lowerCAmelCase  ,      )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			DDIMSchedulerState  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			Optional[int] = None       )					->   jnp.ndarray:
         return sample



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			DDIMSchedulerState  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Tuple = ()       )					->   DDIMSchedulerState:
         _lowerCAmelCase						=		self.config.num_train_timesteps // num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # rounding to avoid issues when num_inference_step is power of 3
         _lowerCAmelCase						=		(jnp.arange(0  ,      _lowerCAmelCase       ) * step_ratio).round()[::-1] + self.config.steps_offset

         return state.replace(
             num_inference_steps=_lowerCAmelCase  ,      timesteps=_lowerCAmelCase  ,      )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			DDIMSchedulerState  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			Any       )					->   Optional[int]:
         _lowerCAmelCase						=		state.common.alphas_cumprod[timestep]
         _lowerCAmelCase						=		jnp.where(
             prev_timestep >= 0  ,      state.common.alphas_cumprod[prev_timestep]  ,      state.final_alpha_cumprod       )
         _lowerCAmelCase						=		1 - alpha_prod_t
         _lowerCAmelCase						=		1 - alpha_prod_t_prev

         _lowerCAmelCase						=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

         return variance



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			DDIMSchedulerState  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			float = 0.0  ,      _lowerCAmelCase					:			bool = True  ,      )					->   Union[FlaxDDIMSchedulerOutput, Tuple]:
         if state.num_inference_steps is None:
           raise ValueError(
               'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'       )

         # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
         # Ideally, read DDIM paper in-detail understanding

         # Notation (<variable name> -> <name in paper>
         # - pred_noise_t -> e_theta(x_t, t)
         # - pred_original_sample -> f_theta(x_t, t) or x_0
         # - std_dev_t -> sigma_t
         # - eta -> η
         # - pred_sample_direction -> "direction pointing to x_t"
         # - pred_prev_sample -> "x_t-1"

         # 1. get previous step value (=t-1)
         _lowerCAmelCase						=		timestep - self.config.num_train_timesteps // state.num_inference_steps

         _lowerCAmelCase						=		state.common.alphas_cumprod
         _lowerCAmelCase						=		state.final_alpha_cumprod

         # 2. compute alphas, betas
         _lowerCAmelCase						=		alphas_cumprod[timestep]
         _lowerCAmelCase						=		jnp.where(prev_timestep >= 0  ,      alphas_cumprod[prev_timestep]  ,      _lowerCAmelCase       )

         _lowerCAmelCase						=		1 - alpha_prod_t

         # 3. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         if self.config.prediction_type == "epsilon":
           _lowerCAmelCase						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           _lowerCAmelCase						=		model_output
         elif self.config.prediction_type == "sample":
           _lowerCAmelCase						=		model_output
           _lowerCAmelCase						=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
           _lowerCAmelCase						=		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           _lowerCAmelCase						=		(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
           raise ValueError(
               F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
               ' `v_prediction`'       )

         # 4. compute variance: "sigma_t(η)" -> see formula (16)
         # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
         _lowerCAmelCase						=		self._get_variance(_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		eta * variance ** 0.5

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         _lowerCAmelCase						=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

         # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         _lowerCAmelCase						=		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

         if not return_dict:
           return (prev_sample, state)

         return FlaxDDIMSchedulerOutput(prev_sample=_lowerCAmelCase  ,      state=_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			DDIMSchedulerState  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			jnp.ndarray  ,      )					->   jnp.ndarray:
         return add_noise_common(state.common  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			DDIMSchedulerState  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			jnp.ndarray  ,      _lowerCAmelCase					:			jnp.ndarray  ,      )					->   jnp.ndarray:
         return get_velocity_common(state.common  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )






       def __len__(     self					:			Union[str, Any]       )					->   Dict:
         return self.config.num_train_timesteps


from __future__ import annotations

import os





class 						__lowercase:


       """simple docstring"""







       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					:			Dict  ,      _lowerCAmelCase					:			str       )					->   None:
         if not isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		F'''Hand should be of type \'str\': {hand!r}'''
           raise TypeError(_lowerCAmelCase       )
         # split removes duplicate whitespaces so no need of strip
         if len(hand.split(' '       )       ) != 5:
           _lowerCAmelCase						=		F'''Hand should contain only 5 cards: {hand!r}'''
           raise ValueError(_lowerCAmelCase       )
         _lowerCAmelCase						=		hand
         _lowerCAmelCase						=		0
         _lowerCAmelCase						=		0
         _lowerCAmelCase	,       _lowerCAmelCase						=		self._internal_state()
         _lowerCAmelCase						=		self._get_hand_type()
         _lowerCAmelCase						=		self._card_values[0]



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   List[Any]:
         return self._hand



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			PokerHand       )					->   str:
         # 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(_lowerCAmelCase       )
           else:
             return "Win" if self._second_pair > other._second_pair else "Loss"
         return "Win" if self._first_pair > other._first_pair else "Loss"



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   str:
         _lowerCAmelCase						=		PokerHand._HAND_NAME[self._hand_type - 14]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._high_card]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._first_pair]
         _lowerCAmelCase						=		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]:
           _lowerCAmelCase						=		'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 SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			PokerHand       )					->   str:
         # 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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         # 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 SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   bool:
         return len(self._card_suit       ) == 1



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   bool:
         # 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
             _lowerCAmelCase						=		self._card_values.pop(0       )
             self._card_values.append(_lowerCAmelCase       )
           return True
         return False



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   bool:
         for i in range(4       ):
           if self._card_values[i] - self._card_values[i + 1] != 1:
             return False
         return True



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   int:
         # 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
         _lowerCAmelCase						=		_lowerCAmelCase						=		_lowerCAmelCase						=		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:
               _lowerCAmelCase						=		self._card_values[i]
               kind += 1
             elif vala == self._card_values[i]:
               kind += 2
             elif not vala:
               _lowerCAmelCase						=		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)
         _lowerCAmelCase						=		kind + 2 if kind in [4, 5] else kind
         # first meaning first pair to compare in 'compare_with'
         _lowerCAmelCase						=		max(_lowerCAmelCase  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		min(_lowerCAmelCase  ,      _lowerCAmelCase       )
         # 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(_lowerCAmelCase       ) != 3:
           _lowerCAmelCase	,       _lowerCAmelCase						=		second, first
         _lowerCAmelCase						=		first
         _lowerCAmelCase						=		second
         return kind



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



       def __repr__(     self					:			Tuple       )					->   Optional[Any]:
         return F'''{self.__class__}("{self._hand}")'''



       def __str__(     self					:			Optional[int]       )					->   Union[str, Any]:
         return self._hand



       def __eq__(     self					:			Optional[int]  ,      _lowerCAmelCase					:			str       )					->   Tuple:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self.compare_with(_lowerCAmelCase       ) == "Tie"
         return NotImplemented



       def __lt__(     self					:			List[Any]  ,      _lowerCAmelCase					:			int       )					->   List[str]:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self.compare_with(_lowerCAmelCase       ) == "Loss"
         return NotImplemented



       def __le__(     self					:			str  ,      _lowerCAmelCase					:			Dict       )					->   str:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self < other or self == other
         return NotImplemented



       def __gt__(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			List[Any]       )					->   int:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return not self < other and self != other
         return NotImplemented



       def __ge__(     self					:			Dict  ,      _lowerCAmelCase					:			Dict       )					->   Optional[Any]:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return not self < other
         return NotImplemented






       def __hash__(     self					:			Optional[int]       )					->   List[Any]:
         return object.__hash__(self       )



def 	_a  (					):






  """simple docstring"""




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


if __name__ == "__main__":
       solution()
