



'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__          =  {
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Dict     =       "deformable_detr"
       lowerCAmelCase__	:str     =       {
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }



       def __init__(					self  ,UpperCAmelCase_=True  ,UpperCAmelCase_=None  ,UpperCAmelCase_=3  ,UpperCAmelCase_=300  ,UpperCAmelCase_=1_024  ,UpperCAmelCase_=6  ,UpperCAmelCase_=1_024  ,UpperCAmelCase_=8  ,UpperCAmelCase_=6  ,UpperCAmelCase_=1_024  ,UpperCAmelCase_=8  ,UpperCAmelCase_=0.0  ,UpperCAmelCase_=True  ,UpperCAmelCase_="relu"  ,UpperCAmelCase_=256  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=0.0  ,UpperCAmelCase_=0.0  ,UpperCAmelCase_=0.02  ,UpperCAmelCase_=1.0  ,UpperCAmelCase_=True  ,UpperCAmelCase_=False  ,UpperCAmelCase_="sine"  ,UpperCAmelCase_="resnet50"  ,UpperCAmelCase_=True  ,UpperCAmelCase_=False  ,UpperCAmelCase_=4  ,UpperCAmelCase_=4  ,UpperCAmelCase_=4  ,UpperCAmelCase_=False  ,UpperCAmelCase_=300  ,UpperCAmelCase_=False  ,UpperCAmelCase_=1  ,UpperCAmelCase_=5  ,UpperCAmelCase_=2  ,UpperCAmelCase_=1  ,UpperCAmelCase_=1  ,UpperCAmelCase_=5  ,UpperCAmelCase_=2  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=0.25  ,UpperCAmelCase_=False  ,**UpperCAmelCase_  ,)	->				Optional[int]:
           if backbone_config is not None and use_timm_backbone:
               raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."   )

           if not use_timm_backbone:
               if backbone_config is None:
                   logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."   )
                   lowercase__									=    CONFIG_MAPPING["resnet"](out_features=["stage4"]   )
               elif isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
                   lowercase__									=    backbone_config.get("model_type"   )
                   lowercase__									=    CONFIG_MAPPING[backbone_model_type]
                   lowercase__									=    config_class.from_dict(UpperCAmelCase_   )
           lowercase__									=    use_timm_backbone
           lowercase__									=    backbone_config
           lowercase__									=    num_channels
           lowercase__									=    num_queries
           lowercase__									=    max_position_embeddings
           lowercase__									=    d_model
           lowercase__									=    encoder_ffn_dim
           lowercase__									=    encoder_layers
           lowercase__									=    encoder_attention_heads
           lowercase__									=    decoder_ffn_dim
           lowercase__									=    decoder_layers
           lowercase__									=    decoder_attention_heads
           lowercase__									=    dropout
           lowercase__									=    attention_dropout
           lowercase__									=    activation_dropout
           lowercase__									=    activation_function
           lowercase__									=    init_std
           lowercase__									=    init_xavier_std
           lowercase__									=    encoder_layerdrop
           lowercase__									=    auxiliary_loss
           lowercase__									=    position_embedding_type
           lowercase__									=    backbone
           lowercase__									=    use_pretrained_backbone
           lowercase__									=    dilation
           # deformable attributes
           lowercase__									=    num_feature_levels
           lowercase__									=    encoder_n_points
           lowercase__									=    decoder_n_points
           lowercase__									=    two_stage
           lowercase__									=    two_stage_num_proposals
           lowercase__									=    with_box_refine
           if two_stage is True and with_box_refine is False:
               raise ValueError("If two_stage is True, with_box_refine must be True."   )
           # Hungarian matcher
           lowercase__									=    class_cost
           lowercase__									=    bbox_cost
           lowercase__									=    giou_cost
           # Loss coefficients
           lowercase__									=    mask_loss_coefficient
           lowercase__									=    dice_loss_coefficient
           lowercase__									=    bbox_loss_coefficient
           lowercase__									=    giou_loss_coefficient
           lowercase__									=    eos_coefficient
           lowercase__									=    focal_alpha
           lowercase__									=    disable_custom_kernels
           super().__init__(is_encoder_decoder=UpperCAmelCase_  ,**UpperCAmelCase_   )



       @property
       def 				_a   (					self   )	->				int:
           return self.encoder_attention_heads



       @property
       def 				_a   (					self   )	->				int:
           return self.d_model







       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    copy.deepcopy(self.__dict__   )
           if self.backbone_config is not None:
               lowercase__									=    self.backbone_config.to_dict()
           lowercase__									=    self.__class__.model_type
           return output









'''simple docstring'''





SCREAMING_SNAKE_CASE__          =  256
# Modulus to hash a string
SCREAMING_SNAKE_CASE__          =  100_0003




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    len(_snake_case							)
    lowercase__									=    len(_snake_case							)
    if p_len > t_len:
        return False

    lowercase__									=    0
    lowercase__									=    0
    lowercase__									=    1

    # Calculating the hash of pattern and substring of text
    for i in range(_snake_case							):
        lowercase__									=    (ord(pattern[i]							) + p_hash * alphabet_size) % modulus
        lowercase__									=    (ord(text[i]							) + text_hash * alphabet_size) % modulus
        if i == p_len - 1:
            continue
        lowercase__									=    (modulus_power * alphabet_size) % modulus

    for i in range(0  ,t_len - p_len + 1							):
        if text_hash == p_hash and text[i : i + p_len] == pattern:
            return True
        if i == t_len - p_len:
            continue
        # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
        lowercase__									=    (
            (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
            + ord(text[i + p_len]							)
        ) % modulus
    return False




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    "abc1abc12"
    lowercase__									=    "alskfjaldsabc1abc1abc12k23adsfabcabc"
    lowercase__									=    "alskfjaldsk23adsfabcabc"
    assert rabin_karp(_snake_case  ,_snake_case							) and not rabin_karp(_snake_case  ,_snake_case							)

    # Test 2)
    lowercase__									=    "ABABX"
    lowercase__									=    "ABABZABABYABABX"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 3)
    lowercase__									=    "AAAB"
    lowercase__									=    "ABAAAAAB"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 4)
    lowercase__									=    "abcdabcy"
    lowercase__									=    "abcxabcdabxabcdabcdabcy"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 5)
    lowercase__									=    "Lü"
    lowercase__									=    "Lüsai"
    assert rabin_karp(_snake_case  ,_snake_case							)
    lowercase__									=    "Lue"
    assert not rabin_karp(_snake_case  ,_snake_case							)
    print("Success."							)


if __name__ == "__main__":
 test_rabin_karp()




