





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

import pytest

from .sola import PokerHand

_A					:				str				=			(
    """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					:				List[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"""),
)

_A					:				Optional[int]				=			(
    ("""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					:				List[Any]				=			(
    ("""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					:				List[str]				=			(
    ("""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					:				Union[str, 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),
)

_A					:				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 							__snake_case							(    )				->						str:
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			randrange(len(lowerCAmelCase_	)	), randrange(len(lowerCAmelCase_	)	)
    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 							__snake_case							(    lowerCAmelCase_ = 1_0_0	)				->						List[Any]:
    return (generate_random_hand() for _ in range(lowerCAmelCase_	))


@pytest.mark.parametrize('''hand, expected''' ,							lowerCAmelCase_	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    assert PokerHand(lowerCAmelCase_	)._is_flush() == expected


@pytest.mark.parametrize('''hand, expected''' ,							lowerCAmelCase_	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[Any]:
    assert PokerHand(lowerCAmelCase_	)._is_straight() == expected


@pytest.mark.parametrize('''hand, expected, card_values''' ,							lowerCAmelCase_	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			PokerHand(lowerCAmelCase_	)
    assert player._is_five_high_straight() == expected
    assert player._card_values == card_values


@pytest.mark.parametrize('''hand, expected''' ,							lowerCAmelCase_	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    assert PokerHand(lowerCAmelCase_	)._is_same_kind() == expected


@pytest.mark.parametrize('''hand, expected''' ,							lowerCAmelCase_	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    assert PokerHand(lowerCAmelCase_	)._hand_type == expected


@pytest.mark.parametrize('''hand, other, expected''' ,							lowerCAmelCase_	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    assert PokerHand(lowerCAmelCase_	).compare_with(PokerHand(lowerCAmelCase_	)	) == expected


@pytest.mark.parametrize('''hand, other, expected''' ,							generate_random_hands()	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    assert PokerHand(lowerCAmelCase_	).compare_with(PokerHand(lowerCAmelCase_	)	) == expected


def 							__snake_case							(    )				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			[PokerHand(lowerCAmelCase_	) for hand in SORTED_HANDS]
    SCREAMING_SNAKE_CASE__											=			poker_hands.copy()
    shuffle(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			chain(sorted(lowerCAmelCase_	)	)
    for index, hand in enumerate(lowerCAmelCase_	):
        assert hand == poker_hands[index]


def 							__snake_case							(    )				->						Tuple:
    # Test that five high straights are compared correctly.
    SCREAMING_SNAKE_CASE__											=			[PokerHand('''2D AC 3H 4H 5S'''	), PokerHand('''2S 3H 4H 5S 6C'''	)]
    pokerhands.sort(reverse=lowerCAmelCase_	)
    assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"


def 							__snake_case							(    )				->						Tuple:
    # Multiple calls to five_high_straight function should still return True
    # and shouldn't mutate the list in every call other than the first.
    SCREAMING_SNAKE_CASE__											=			PokerHand('''2C 4S AS 3D 5C'''	)
    SCREAMING_SNAKE_CASE__											=			True
    SCREAMING_SNAKE_CASE__											=			[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 							__snake_case							(    )				->						str:
    # Problem number 54 from Project Euler
    # Testing from poker_hands.txt file
    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			os.path.abspath(os.path.dirname(lowerCAmelCase_	)	)
    SCREAMING_SNAKE_CASE__											=			os.path.join(lowerCAmelCase_ ,							'''poker_hands.txt'''	)
    with open(lowerCAmelCase_	) as file_hand:
        for line in file_hand:
            SCREAMING_SNAKE_CASE__											=			line[:1_4].strip()
            SCREAMING_SNAKE_CASE__											=			line[1_5:].strip()
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			PokerHand(lowerCAmelCase_	), PokerHand(lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			player.compare_with(lowerCAmelCase_	)
            if output == "Win":
                answer += 1
    assert answer == 3_7_6



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			:					int						=			logging.get_logger(__name__)


__A			:					List[str]						=			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			:					Optional[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			:					Tuple						=			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			:					Dict						=			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			:					Any						=			OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

__A			:					Optional[int]						=			OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

__A			:					Union[str, 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			:					Optional[int]						=			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			:					str						=			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			:					Dict						=			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			:					Dict						=			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			:					Any						=			OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

__A			:					Optional[int]						=			OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

__A			:					List[str]						=			OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

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





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_MAPPING




__A			:					Optional[int]						=			auto_class_update(FlaxAutoModel)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_PRETRAINING_MAPPING




__A			:					Optional[Any]						=			auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




__A			:					Tuple						=			auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MASKED_LM_MAPPING




__A			:					List[Any]						=			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




__A			:					int						=			auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




__A			:					List[Any]						=			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




__A			:					Any						=			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




__A			:					Union[str, Any]						=			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




__A			:					Optional[Any]						=			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)