

"""simple docstring"""

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

import pytest

from .sola import PokerHand

a_			   =					(
    """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)
)

a_			   =					(
    ("""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"""),
)

a_			   =					(
    ("""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),
)

a_			   =					(
    ("""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),
)

a_			   =					(
    ("""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]),
)

a_			   =					(
    ("""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),
)

a_			   =					(
    ("""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__      (		):



					"""simple docstring"""





					snake_case_					,			snake_case_					:		Any 					=					randrange(len(SCREAMING_SNAKE_CASE__			)			), randrange(len(SCREAMING_SNAKE_CASE__			)			)
					snake_case_					:		Tuple 					=					["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
					snake_case_					,			snake_case_					:		Dict 					=					SORTED_HANDS[play], SORTED_HANDS[oppo]
					return hand, other, expected







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int = 1_0_0			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""hand, expected"""				,    SCREAMING_SNAKE_CASE__			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""hand, expected"""				,    SCREAMING_SNAKE_CASE__			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""hand, expected, card_values"""				,    SCREAMING_SNAKE_CASE__			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		Dict 					=					PokerHand(SCREAMING_SNAKE_CASE__			)
					assert player._is_five_high_straight() == expected
					assert player._card_values == card_values







@pytest.mark.parametrize("""hand, expected"""				,    SCREAMING_SNAKE_CASE__			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""hand, expected"""				,    SCREAMING_SNAKE_CASE__			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





					assert PokerHand(SCREAMING_SNAKE_CASE__			)._hand_type == expected







@pytest.mark.parametrize("""hand, other, expected"""				,    SCREAMING_SNAKE_CASE__			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""hand, other, expected"""				,    generate_random_hands()			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





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







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		int 					=					[PokerHand(SCREAMING_SNAKE_CASE__			) for hand in SORTED_HANDS]
					snake_case_					:		Dict 					=					poker_hands.copy()
					shuffle(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		int 					=					chain(sorted(SCREAMING_SNAKE_CASE__			)			)
					for index, hand in enumerate(SCREAMING_SNAKE_CASE__			):
										assert hand == poker_hands[index]







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					[PokerHand("""2D AC 3H 4H 5S"""			), PokerHand("""2S 3H 4H 5S 6C"""			)]
					pokerhands.sort(reverse=SCREAMING_SNAKE_CASE__			)
					assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					PokerHand("""2C 4S AS 3D 5C"""			)
					snake_case_					:		int 					=					True
					snake_case_					:		Tuple 					=					[5, 4, 3, 2, 1_4]
					for _ in range(1_0			):
										assert pokerhand._is_five_high_straight() == expected
										assert pokerhand._card_values == expected_card_values







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Any 					=					0
					snake_case_					:		int 					=					os.path.abspath(os.path.dirname(SCREAMING_SNAKE_CASE__			)			)
					snake_case_					:		List[str] 					=					os.path.join(SCREAMING_SNAKE_CASE__				,    """poker_hands.txt"""			)
					with open(SCREAMING_SNAKE_CASE__			) as file_hand:
										for line in file_hand:
															snake_case_					:		Dict 					=					line[:1_4].strip()
															snake_case_					:		Any 					=					line[1_5:].strip()
															snake_case_					,			snake_case_					:		int 					=					PokerHand(SCREAMING_SNAKE_CASE__			), PokerHand(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		Union[str, Any] 					=					player.compare_with(SCREAMING_SNAKE_CASE__			)
															if output == "Win":
																				answer += 1
					assert answer == 3_7_6









"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


a_			   =					r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''







@add_start_docstrings(_UpperCAmelCase)
class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""rag"""
						_A		:							Optional[Any]							       =		True







						def __init__(self   ,		lowercase__=None   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=" / "   ,		lowercase__=" // "   ,		lowercase__=5   ,		lowercase__=3_00   ,		lowercase__=7_68   ,		lowercase__=8   ,		lowercase__="wiki_dpr"   ,		lowercase__="train"   ,		lowercase__="compressed"   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=0.0   ,		lowercase__=True   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=None   ,		**lowercase__   ,		):
											super().__init__(
											    bos_token_id=lowercase__   ,		pad_token_id=lowercase__   ,		eos_token_id=lowercase__   ,		decoder_start_token_id=lowercase__   ,		forced_eos_token_id=lowercase__   ,		is_encoder_decoder=lowercase__   ,		prefix=lowercase__   ,		vocab_size=lowercase__   ,		**lowercase__   ,		)
											assert (
											    "question_encoder" in kwargs and "generator" in kwargs
											), "Config has to be initialized with question_encoder and generator config"
											snake_case_					:		List[Any] 					=					kwargs.pop("""question_encoder"""       )
											snake_case_					:		Tuple 					=					question_encoder_config.pop("""model_type"""       )
											snake_case_					:		List[str] 					=					kwargs.pop("""generator"""       )
											snake_case_					:		List[str] 					=					decoder_config.pop("""model_type"""       )

											from ..auto.configuration_auto import AutoConfig

											snake_case_					:		List[str] 					=					AutoConfig.for_model(lowercase__   ,		**lowercase__       )
											snake_case_					:		Tuple 					=					AutoConfig.for_model(lowercase__   ,		**lowercase__       )

											snake_case_					:		int 					=					reduce_loss
											snake_case_					:		Optional[int] 					=					label_smoothing
											snake_case_					:		Dict 					=					exclude_bos_score
											snake_case_					:		Union[str, Any] 					=					do_marginalize

											snake_case_					:		Union[str, Any] 					=					title_sep
											snake_case_					:		int 					=					doc_sep
											snake_case_					:		int 					=					n_docs
											snake_case_					:		List[str] 					=					max_combined_length

											snake_case_					:		Tuple 					=					dataset
											snake_case_					:		int 					=					dataset_split
											snake_case_					:		str 					=					index_name

											snake_case_					:		List[str] 					=					retrieval_vector_size
											snake_case_					:		Dict 					=					retrieval_batch_size
											snake_case_					:		str 					=					passages_path
											snake_case_					:		Union[str, Any] 					=					index_path
											snake_case_					:		Tuple 					=					use_dummy_dataset

											snake_case_					:		Dict 					=					output_retrieved

											snake_case_					:		str 					=					do_deduplication

											snake_case_					:		Any 					=					use_cache

											if self.forced_eos_token_id is None:
																snake_case_					:		Any 					=					getattr(self.generator   ,		"""forced_eos_token_id"""   ,		lowercase__       )







						@classmethod
						def 						__UpperCamelCase							(cls   ,		lowercase__   ,		lowercase__   ,		**lowercase__       ):
											return cls(question_encoder=question_encoder_config.to_dict()   ,		generator=generator_config.to_dict()   ,		**lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					copy.deepcopy(self.__dict__       )
											snake_case_					:		Any 					=					self.question_encoder.to_dict()
											snake_case_					:		Dict 					=					self.generator.to_dict()
											snake_case_					:		Union[str, Any] 					=					self.__class__.model_type
											return output




