



'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase	:       str      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Union[str, Any]      	=	{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """marian"""
    _SCREAMING_SNAKE_CASE	  =  ["""past_key_values"""]
    _SCREAMING_SNAKE_CASE	  =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					Optional[Any]=5_8_1_0_1  ,						UpperCamelCase__      :					Dict=None  ,						UpperCamelCase__      :					Any=1_0_2_4  ,						UpperCamelCase__      :					List[str]=1_2  ,						UpperCamelCase__      :					Dict=4_0_9_6  ,						UpperCamelCase__      :					Tuple=1_6  ,						UpperCamelCase__      :					Any=1_2  ,						UpperCamelCase__      :					List[str]=4_0_9_6  ,						UpperCamelCase__      :					int=1_6  ,						UpperCamelCase__      :					str=0.0  ,						UpperCamelCase__      :					Optional[Any]=0.0  ,						UpperCamelCase__      :					Optional[int]=True  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[int]="gelu"  ,						UpperCamelCase__      :					List[Any]=1_0_2_4  ,						UpperCamelCase__      :					str=0.1  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Tuple=0.0  ,						UpperCamelCase__      :					List[str]=0.0_2  ,						UpperCamelCase__      :					List[str]=5_8_1_0_0  ,						UpperCamelCase__      :					Dict=False  ,						UpperCamelCase__      :					Optional[int]=5_8_1_0_0  ,						UpperCamelCase__      :					Union[str, Any]=0  ,						UpperCamelCase__      :					List[str]=0  ,						UpperCamelCase__      :					int=True  ,						**UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  decoder_vocab_size or vocab_size
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  d_model
       UpperCamelCase					=  encoder_ffn_dim
       UpperCamelCase					=  encoder_layers
       UpperCamelCase					=  encoder_attention_heads
       UpperCamelCase					=  decoder_ffn_dim
       UpperCamelCase					=  decoder_layers
       UpperCamelCase					=  decoder_attention_heads
       UpperCamelCase					=  dropout
       UpperCamelCase					=  attention_dropout
       UpperCamelCase					=  activation_dropout
       UpperCamelCase					=  activation_function
       UpperCamelCase					=  init_std
       UpperCamelCase					=  encoder_layerdrop
       UpperCamelCase					=  decoder_layerdrop
       UpperCamelCase					=  use_cache
       UpperCamelCase					=  encoder_layers
       UpperCamelCase					=  scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase					=  share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=UpperCamelCase__  ,						eos_token_id=UpperCamelCase__  ,						is_encoder_decoder=UpperCamelCase__  ,						decoder_start_token_id=UpperCamelCase__  ,						forced_eos_token_id=UpperCamelCase__  ,						**UpperCamelCase__  ,						)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
              ] )

          if self.use_past:
             UpperCamelCase					=  {0: 'batch'}
             UpperCamelCase					=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
             UpperCamelCase					=  {0: 'batch', 1: 'decoder_sequence'}
             UpperCamelCase					=  {0: 'batch', 1: 'decoder_sequence'}

          if self.use_past:
             self.fill_with_past_key_values_(UpperCamelCase__  ,						direction='inputs' )
       elif self.task == "causal-lm":
          # TODO: figure this case out.
          UpperCamelCase					=  OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
              ] )
          if self.use_past:
             UpperCamelCase				,      UpperCamelCase					=  self.num_layers
             for i in range(UpperCamelCase__ ):
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
       else:
          UpperCamelCase					=  OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                  ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
              ] )

       return common_inputs






    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def  A       (  self      :					Dict ):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  super().outputs
       else:
          UpperCamelCase					=  super(UpperCamelCase__  ,						self ).outputs
          if self.use_past:
             UpperCamelCase				,      UpperCamelCase					=  self.num_layers
             for i in range(UpperCamelCase__ ):
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  self._generate_dummy_inputs_for_encoder_and_decoder(
           UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       # Generate decoder inputs
       UpperCamelCase					=  seq_length if not self.use_past else 1
       UpperCamelCase					=  self._generate_dummy_inputs_for_encoder_and_decoder(
           UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase					=  dict(**UpperCamelCase__  ,						**UpperCamelCase__ )

       if self.use_past:
          if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
          else:
             import torch
          UpperCamelCase				,      UpperCamelCase					=  common_inputs['input_ids'].shape
          UpperCamelCase					=  common_inputs['decoder_input_ids'].shape[1]
          UpperCamelCase				,      UpperCamelCase					=  self.num_attention_heads
          UpperCamelCase					=  (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          UpperCamelCase					=  decoder_seq_length + 3
          UpperCamelCase					=  (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )

          UpperCamelCase					=  torch.cat(
              [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase__  ,						UpperCamelCase__ )]  ,						dim=1 )

          UpperCamelCase					=  []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          UpperCamelCase				,      UpperCamelCase					=  self.num_layers
          UpperCamelCase					=  min(UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  max(UpperCamelCase__  ,						UpperCamelCase__ ) - min_num_layers
          UpperCamelCase					=  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

          for _ in range(UpperCamelCase__ ):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(UpperCamelCase__ ),
                     torch.zeros(UpperCamelCase__ ),
                     torch.zeros(UpperCamelCase__ ),
                     torch.zeros(UpperCamelCase__ ),
                 ) )
          # TODO: test this.
          UpperCamelCase					=  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
          for _ in range(UpperCamelCase__  ,						UpperCamelCase__ ):
             common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__ ), torch.zeros(UpperCamelCase__ )) )
       return common_inputs






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  self._generate_dummy_inputs_for_encoder_and_decoder(
           UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       if self.use_past:
          if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
          else:
             import torch
          UpperCamelCase				,      UpperCamelCase					=  common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          UpperCamelCase					=  seqlen + 2
          UpperCamelCase				,      UpperCamelCase					=  self.num_layers
          UpperCamelCase				,      UpperCamelCase					=  self.num_attention_heads
          UpperCamelCase					=  (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

          UpperCamelCase					=  common_inputs['attention_mask'].dtype
          UpperCamelCase					=  torch.cat(
              [common_inputs['attention_mask'], torch.ones(UpperCamelCase__  ,						UpperCamelCase__  ,						dtype=UpperCamelCase__ )]  ,						dim=1 )
          UpperCamelCase					=  [
              (torch.zeros(UpperCamelCase__ ), torch.zeros(UpperCamelCase__ )) for _ in range(UpperCamelCase__ )
          ]
       return common_inputs






    def  A       (  self      :					int  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  compute_effective_axis_dimension(
           UpperCamelCase__  ,						fixed_dimension=OnnxConfig.default_fixed_batch  ,						num_token_to_add=0 )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase					=  tokenizer.num_special_tokens_to_add(UpperCamelCase__ )
       UpperCamelCase					=  compute_effective_axis_dimension(
           UpperCamelCase__  ,						fixed_dimension=OnnxConfig.default_fixed_sequence  ,						num_token_to_add=UpperCamelCase__ )

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase					=  [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
       UpperCamelCase					=  dict(tokenizer(UpperCamelCase__  ,						return_tensors=UpperCamelCase__ ) )
       return common_inputs






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              UpperCamelCase__  ,						batch_size=UpperCamelCase__  ,						seq_length=UpperCamelCase__  ,						is_pair=UpperCamelCase__  ,						framework=UpperCamelCase__ )

       else:
          UpperCamelCase					=  self._generate_dummy_inputs_for_causal_lm(
              UpperCamelCase__  ,						batch_size=UpperCamelCase__  ,						seq_length=UpperCamelCase__  ,						is_pair=UpperCamelCase__  ,						framework=UpperCamelCase__ )

       return common_inputs






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  super()._flatten_past_key_values_(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       else:
          UpperCamelCase					=  super(UpperCamelCase__  ,						self )._flatten_past_key_values_(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    @property
    def  A       (  self      :					Dict ):


       """simple docstring"""







       return 1E-4






'''simple docstring'''


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

import pytest

from .sola import PokerHand

_lowerCamelCase	:       Optional[int]      	=	(
    "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	:       Union[str, Any]      	=	(
    ("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	:       Dict      	=	(
    ("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	:       Optional[Any]      	=	(
    ("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	:       List[Any]      	=	(
    ("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	:       List[str]      	=	(
    ("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				(   )       ->	Optional[Any]:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  randrange(len(A__    )    ), randrange(len(A__    )    )
   UpperCamelCase					=  ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
   UpperCamelCase				,      UpperCamelCase					=  SORTED_HANDS[play], SORTED_HANDS[oppo]
   return hand, other, expected







def 				__lowerCamelCase				(   A__ = 100    )       ->	Optional[Any]:






   """simple docstring"""
   return (generate_random_hand() for _ in range(A__    ))







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   assert PokerHand(A__    )._is_flush() == expected







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   assert PokerHand(A__    )._is_straight() == expected







@pytest.mark.parametrize('hand, expected, card_values'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  PokerHand(A__    )
   assert player._is_five_high_straight() == expected
   assert player._card_values == card_values







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   assert PokerHand(A__    )._is_same_kind() == expected







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	str:






   """simple docstring"""
   assert PokerHand(A__    )._hand_type == expected







@pytest.mark.parametrize('hand, other, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Tuple:






   """simple docstring"""
   assert PokerHand(A__    ).compare_with(PokerHand(A__    )    ) == expected







@pytest.mark.parametrize('hand, other, expected'    ,						generate_random_hands()    )
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[str]:






   """simple docstring"""
   assert PokerHand(A__    ).compare_with(PokerHand(A__    )    ) == expected







def 				__lowerCamelCase				(   )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  [PokerHand(A__    ) for hand in SORTED_HANDS]
   UpperCamelCase					=  poker_hands.copy()
   shuffle(A__    )
   UpperCamelCase					=  chain(sorted(A__    )    )
   for index, hand in enumerate(A__    ):
      assert hand == poker_hands[index]







def 				__lowerCamelCase				(   )       ->	Optional[int]:






   """simple docstring"""
   # Test that five high straights are compared correctly.
   UpperCamelCase					=  [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 				__lowerCamelCase				(   )       ->	str:






   """simple docstring"""
   # 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					=  PokerHand('2C 4S AS 3D 5C'    )
   UpperCamelCase					=  True
   UpperCamelCase					=  [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				(   )       ->	List[str]:






   """simple docstring"""
   # Problem number 54 from Project Euler
   # Testing from poker_hands.txt file
   UpperCamelCase					=  0
   UpperCamelCase					=  os.path.abspath(os.path.dirname(A__    )    )
   UpperCamelCase					=  os.path.join(A__    ,						'poker_hands.txt'    )
   with open(A__    ) as file_hand:
      for line in file_hand:
         UpperCamelCase					=  line[:14].strip()
         UpperCamelCase					=  line[15:].strip()
         UpperCamelCase				,      UpperCamelCase					=  PokerHand(A__    ), PokerHand(A__    )
         UpperCamelCase					=  player.compare_with(A__    )
         if output == "Win":
            answer += 1
   assert answer == 376

