


"""simple docstring"""







import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowerCamelCase_   :   Tuple									=  logging.get_logger(__name__)

lowerCamelCase_   :   str									=  OrderedDict(
    [
        ("""align""", """EfficientNetImageProcessor"""),
        ("""beit""", """BeitImageProcessor"""),
        ("""bit""", """BitImageProcessor"""),
        ("""blip""", """BlipImageProcessor"""),
        ("""blip-2""", """BlipImageProcessor"""),
        ("""bridgetower""", """BridgeTowerImageProcessor"""),
        ("""chinese_clip""", """ChineseCLIPImageProcessor"""),
        ("""clip""", """CLIPImageProcessor"""),
        ("""clipseg""", """ViTImageProcessor"""),
        ("""conditional_detr""", """ConditionalDetrImageProcessor"""),
        ("""convnext""", """ConvNextImageProcessor"""),
        ("""convnextv2""", """ConvNextImageProcessor"""),
        ("""cvt""", """ConvNextImageProcessor"""),
        ("""data2vec-vision""", """BeitImageProcessor"""),
        ("""deformable_detr""", """DeformableDetrImageProcessor"""),
        ("""deit""", """DeiTImageProcessor"""),
        ("""deta""", """DetaImageProcessor"""),
        ("""detr""", """DetrImageProcessor"""),
        ("""dinat""", """ViTImageProcessor"""),
        ("""donut-swin""", """DonutImageProcessor"""),
        ("""dpt""", """DPTImageProcessor"""),
        ("""efficientformer""", """EfficientFormerImageProcessor"""),
        ("""efficientnet""", """EfficientNetImageProcessor"""),
        ("""flava""", """FlavaImageProcessor"""),
        ("""focalnet""", """BitImageProcessor"""),
        ("""git""", """CLIPImageProcessor"""),
        ("""glpn""", """GLPNImageProcessor"""),
        ("""groupvit""", """CLIPImageProcessor"""),
        ("""imagegpt""", """ImageGPTImageProcessor"""),
        ("""instructblip""", """BlipImageProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
        ("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
        ("""levit""", """LevitImageProcessor"""),
        ("""mask2former""", """Mask2FormerImageProcessor"""),
        ("""maskformer""", """MaskFormerImageProcessor"""),
        ("""mgp-str""", """ViTImageProcessor"""),
        ("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
        ("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevitv2""", """MobileViTImageProcessor"""),
        ("""nat""", """ViTImageProcessor"""),
        ("""oneformer""", """OneFormerImageProcessor"""),
        ("""owlvit""", """OwlViTImageProcessor"""),
        ("""perceiver""", """PerceiverImageProcessor"""),
        ("""pix2struct""", """Pix2StructImageProcessor"""),
        ("""poolformer""", """PoolFormerImageProcessor"""),
        ("""regnet""", """ConvNextImageProcessor"""),
        ("""resnet""", """ConvNextImageProcessor"""),
        ("""sam""", """SamImageProcessor"""),
        ("""segformer""", """SegformerImageProcessor"""),
        ("""swiftformer""", """ViTImageProcessor"""),
        ("""swin""", """ViTImageProcessor"""),
        ("""swin2sr""", """Swin2SRImageProcessor"""),
        ("""swinv2""", """ViTImageProcessor"""),
        ("""table-transformer""", """DetrImageProcessor"""),
        ("""timesformer""", """VideoMAEImageProcessor"""),
        ("""tvlt""", """TvltImageProcessor"""),
        ("""upernet""", """SegformerImageProcessor"""),
        ("""van""", """ConvNextImageProcessor"""),
        ("""videomae""", """VideoMAEImageProcessor"""),
        ("""vilt""", """ViltImageProcessor"""),
        ("""vit""", """ViTImageProcessor"""),
        ("""vit_hybrid""", """ViTHybridImageProcessor"""),
        ("""vit_mae""", """ViTImageProcessor"""),
        ("""vit_msn""", """ViTImageProcessor"""),
        ("""xclip""", """CLIPImageProcessor"""),
        ("""yolos""", """YolosImageProcessor"""),
    ]
)

lowerCamelCase_   :   Union[str, Any]									=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def _A						(						lowercase		):







    """simple docstring"""
    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            a 		=model_type_to_module_name(lowercase		)

            a 		=importlib.import_module(f'''.{module_name}'''       ,    '''transformers.models'''		)
            try:
                return getattr(lowercase       ,    lowercase		)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(lowercase       ,    '''__name__'''       ,    lowercase		) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    a 		=importlib.import_module('''transformers'''		)
    if hasattr(lowercase       ,    lowercase		):
        return getattr(lowercase       ,    lowercase		)

    return None
def _A						(						lowercase       ,    lowercase = None       ,    lowercase = False       ,    lowercase = False       ,    lowercase = None       ,    lowercase = None       ,    lowercase = None       ,    lowercase = False       ,    **lowercase       ,    ):







    """simple docstring"""
    a 		=get_file_from_repo(
        lowercase       ,    lowercase       ,    cache_dir=lowercase       ,    force_download=lowercase       ,    resume_download=lowercase       ,    proxies=lowercase       ,    use_auth_token=lowercase       ,    revision=lowercase       ,    local_files_only=lowercase       ,    )
    if resolved_config_file is None:
        logger.info(
            '''Could not locate the image processor configuration file, will try to use the model config instead.'''		)
        return {}

    with open(lowercase       ,    encoding='''utf-8'''		) as reader:
        return json.load(lowercase		)






class       __A				:






 """simple docstring"""




 def __init__(		self						)					->     Optional[Any]:
     raise EnvironmentError(
         '''AutoImageProcessor is designed to be instantiated '''
         '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''						)


 @classmethod
 @replace_list_option_in_docstrings(__A						)
 def       SCREAMING_SNAKE_CASE							(		cls ,    __A ,    **__A						)					->     Dict:
     a 		=kwargs.pop('''config''' ,    __A						)
     a 		=kwargs.pop('''trust_remote_code''' ,    __A						)
     a 		=True

     a		,      a 		=ImageProcessingMixin.get_image_processor_dict(__A ,    **__A						)
     a 		=config_dict.get('''image_processor_type''' ,    __A						)
     a 		=None
     if "AutoImageProcessor" in config_dict.get('''auto_map''' ,    {}						):
         a 		=config_dict['''auto_map''']['''AutoImageProcessor''']

     # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
     # and if so, infer the image processor class from there.
     if image_processor_class is None and image_processor_auto_map is None:
         a 		=config_dict.pop('''feature_extractor_type''' ,    __A						)
         if feature_extractor_class is not None:
             logger.warning(
                 '''Could not find image processor class in the image processor config or the model config. Loading'''
                 ''' based on pattern matching with the model\'s feature extractor configuration.'''						)
             a 		=feature_extractor_class.replace('''FeatureExtractor''' ,    '''ImageProcessor'''						)
         if "AutoFeatureExtractor" in config_dict.get('''auto_map''' ,    {}						):
             a 		=config_dict['''auto_map''']['''AutoFeatureExtractor''']
             a 		=feature_extractor_auto_map.replace('''FeatureExtractor''' ,    '''ImageProcessor'''						)
             logger.warning(
                 '''Could not find image processor auto map in the image processor config or the model config.'''
                 ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''						)

        # If we don't find the image processor class in the image processor config, let's try the model config.
     if image_processor_class is None and image_processor_auto_map is None:
         if not isinstance(__A ,    __A						):
             a 		=AutoConfig.from_pretrained(__A ,    **__A						)
         # It could be in `config.image_processor_type``
         a 		=getattr(__A ,    '''image_processor_type''' ,    __A						)
         if hasattr(__A ,    '''auto_map'''						) and "AutoImageProcessor" in config.auto_map:
             a 		=config.auto_map['''AutoImageProcessor''']

     if image_processor_class is not None:
         a 		=image_processor_class_from_name(__A						)

     a 		=image_processor_auto_map is not None
     a 		=image_processor_class is not None or type(__A						) in IMAGE_PROCESSOR_MAPPING
     a 		=resolve_trust_remote_code(
         __A ,    __A ,    __A ,    __A						)

     if has_remote_code and trust_remote_code:
         a 		=get_class_from_dynamic_module(
             __A ,    __A ,    **__A						)
         a 		=kwargs.pop('''code_revision''' ,    __A						)
         if os.path.isdir(__A						):
             image_processor_class.register_for_auto_class()
         return image_processor_class.from_dict(__A ,    **__A						)
     elif image_processor_class is not None:
         return image_processor_class.from_dict(__A ,    **__A						)
     # Last try: we use the IMAGE_PROCESSOR_MAPPING.
     elif type(__A						) in IMAGE_PROCESSOR_MAPPING:
         a 		=IMAGE_PROCESSOR_MAPPING[type(__A						)]
         return image_processor_class.from_dict(__A ,    **__A						)

     raise ValueError(
         f'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
         f'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
         f'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()						)}'''						)





 @staticmethod
 def       SCREAMING_SNAKE_CASE							(		__A ,    __A						)					->     Any:
     IMAGE_PROCESSOR_MAPPING.register(__A ,    __A						)






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

import pytest

from .sola import PokerHand

lowerCAmelCase_    =				(
    '''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_    =				(
    ('''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_    =				(
    ('''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_    =				(
    ('''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_    =				(
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
    ('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

lowerCAmelCase_    =				(
    ('''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_    =				(
    ('''JH AH TH KH QH''', 2_3),
    ('''JH 9H TH KH QH''', 2_2),
    ('''JC KH JS JD JH''', 2_1),
    ('''KH KC 3S 3H 3D''', 2_0),
    ('''8C 9C 5C 3C TC''', 1_9),
    ('''JS QS 9H TS KH''', 1_8),
    ('''7C 7S KH 2H 7H''', 1_7),
    ('''3C KH 5D 5S KH''', 1_6),
    ('''QH 8H KD JH 8S''', 1_5),
    ('''2D 6D 9D TH 7D''', 1_4),
)






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase = 100 )		->						str:






				"""simple docstring"""


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






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


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






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






				"""simple docstring"""


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






@pytest.mark.parametrize('''hand, expected, card_values''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    str				=  PokerHand(_UpperCamelCase )
				assert player._is_five_high_straight() == expected
				assert player._card_values == card_values






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


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






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






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase )._hand_type == expected






@pytest.mark.parametrize('''hand, other, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Any:






				"""simple docstring"""


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






@pytest.mark.parametrize('''hand, other, expected''' ,				generate_random_hands() )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[Any]:






				"""simple docstring"""


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






def      lowerCamelCase_						(				)		->						str:






				"""simple docstring"""


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






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  [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_						(				)		->						str:






				"""simple docstring"""


				snake_case_						:    Dict				=  PokerHand('''2C 4S AS 3D 5C''' )
				snake_case_						:    str				=  True
				snake_case_						:    Tuple				=  [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"""


				snake_case_						:    List[str]				=  0
				snake_case_						:    Union[str, Any]				=  os.path.abspath(os.path.dirname(_UpperCamelCase ) )
				snake_case_						:    Dict				=  os.path.join(_UpperCamelCase ,				'''poker_hands.txt''' )
				with open(_UpperCamelCase ) as file_hand:
								for line in file_hand:
												snake_case_						:    Dict				=  line[:14].strip()
												snake_case_						:    List[str]				=  line[15:].strip()
												snake_case_			,							snake_case_						:    str				=  PokerHand(_UpperCamelCase ), PokerHand(_UpperCamelCase )
												snake_case_						:    int				=  player.compare_with(_UpperCamelCase )
												if output == "Win":
																answer += 1
				assert answer == 376
