

from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


a_     :    Tuple     		=   logging.get_logger(__name__)


a_     :    Any     		=   OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

a_     :    List[Any]     		=   OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

a_     :    str     		=   OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

a_     :    Optional[Any]     		=   OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

a_     :    str     		=   OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

a_     :    List[str]     		=   OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

a_     :    Any     		=   OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

a_     :    str     		=   OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

a_     :    Any     		=   OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

a_     :    Tuple     		=   OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

a_     :    Tuple     		=   OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

a_     :    Optional[Any]     		=   OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

a_     :    List[Any]     		=   OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

a_     :    List[Any]     		=   OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

a_     :    List[Any]     		=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a_     :    Optional[int]     		=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a_     :    Tuple     		=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a_     :    Optional[int]     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a_     :    Any     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a_     :    Union[str, Any]     		=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a_     :    Any     		=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a_     :    List[Any]     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a_     :    Any     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a_     :    Tuple     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a_     :    Tuple     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a_     :    Union[str, Any]     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a_     :    Tuple     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a_     :    List[Any]     		=   _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_MAPPING


a_     :    int     		=   auto_class_update(FlaxAutoModel)




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_PRETRAINING_MAPPING


a_     :    Union[str, Any]     		=   auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


a_     :    List[Any]     		=   auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_MASKED_LM_MAPPING


a_     :    List[str]     		=   auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


a_     :    Dict     		=   auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


a_     :    Optional[Any]     		=   auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


a_     :    int     		=   auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


a_     :    Tuple     		=   auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


a_     :    Union[str, Any]     		=   auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


a_     :    List[Any]     		=   auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


a_     :    str     		=   auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


a_     :    Union[str, Any]     		=   auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")




class   UpperCamelCase			(  _BaseAutoModelClass ):
    __UpperCamelCase    =FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


a_     :    Union[str, Any]     		=   auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)




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

import pytest

from .sola import PokerHand

a_     :    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)
)

a_     :    int     		=   (
    ("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_     :    Tuple     		=   (
    ("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_     :    Tuple     		=   (
    ("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_     :    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]),
)

a_     :    List[str]     		=   (
    ("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_     :    Optional[Any]     		=   (
    ("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     (							)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      randrange(len(_UpperCamelCase						)						), randrange(len(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      SORTED_HANDS[play], SORTED_HANDS[oppo]
 return hand, other, expected


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int = 1_00						)		->				int:
 '''simple docstring'''


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


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]						)		->				int:
 '''simple docstring'''


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


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


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


@pytest.mark.parametrize('hand, expected, card_values'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand(_UpperCamelCase						)
 assert player._is_five_high_straight() == expected
 assert player._card_values == card_values


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str						)		->				int:
 '''simple docstring'''


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


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._hand_type == expected


@pytest.mark.parametrize('hand, other, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Dict						)		->				Tuple:
 '''simple docstring'''


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


@pytest.mark.parametrize('hand, other, expected'		,				generate_random_hands()						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Tuple						)		->				List[Any]:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [PokerHand(_UpperCamelCase						) for hand in SORTED_HANDS]
 SCREAMING_SNAKE_CASE		    =      poker_hands.copy()
 shuffle(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      chain(sorted(_UpperCamelCase						)						)
 for index, hand in enumerate(_UpperCamelCase						):
  assert hand == poker_hands[index]


def 							__lowerCAmelCase     (							)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [PokerHand('2D AC 3H 4H 5S'						), PokerHand('2S 3H 4H 5S 6C'						)]
 pokerhands.sort(reverse=_UpperCamelCase						)
 assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"


def 							__lowerCAmelCase     (							)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand('2C 4S AS 3D 5C'						)
 SCREAMING_SNAKE_CASE		    =      True
 SCREAMING_SNAKE_CASE		    =      [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     (							)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      0
 SCREAMING_SNAKE_CASE		    =      os.path.abspath(os.path.dirname(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      os.path.join(_UpperCamelCase		,				'poker_hands.txt'						)
 with open(_UpperCamelCase						) as file_hand:
  for line in file_hand:
   SCREAMING_SNAKE_CASE		    =      line[:14].strip()
   SCREAMING_SNAKE_CASE		    =      line[15:].strip()
   SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      PokerHand(_UpperCamelCase						), PokerHand(_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      player.compare_with(_UpperCamelCase						)
   if output == "Win":
    answer += 1
 assert answer == 3_76

