






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__UpperCamelCase					:		Optional[Any]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		Optional[Any]	  =						{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}


class   __SCREAMING_SNAKE_CASE(	lowercase_	):
							_UpperCAmelCase     =       '''table-transformer'''
							_UpperCAmelCase     =       ['''past_key_values''']
							_UpperCAmelCase     =       {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}
							def __init__(     self:      Dict       ,	UpperCamelCase:      Optional[Any]=True       ,	UpperCamelCase:      Tuple=None       ,	UpperCamelCase:      Optional[Any]=3       ,	UpperCamelCase:      Tuple=1_00       ,	UpperCamelCase:      Tuple=6       ,	UpperCamelCase:      Tuple=20_48       ,	UpperCamelCase:      List[Any]=8       ,	UpperCamelCase:      List[Any]=6       ,	UpperCamelCase:      Tuple=20_48       ,	UpperCamelCase:      Union[str, Any]=8       ,	UpperCamelCase:      Tuple=0.0       ,	UpperCamelCase:      Dict=0.0       ,	UpperCamelCase:      str=True       ,	UpperCamelCase:      str="relu"       ,	UpperCamelCase:      List[str]=2_56       ,	UpperCamelCase:      List[Any]=0.1       ,	UpperCamelCase:      Optional[Any]=0.0       ,	UpperCamelCase:      str=0.0       ,	UpperCamelCase:      List[Any]=0.02       ,	UpperCamelCase:      int=1.0       ,	UpperCamelCase:      Tuple=False       ,	UpperCamelCase:      Tuple="sine"       ,	UpperCamelCase:      str="resnet50"       ,	UpperCamelCase:      List[Any]=True       ,	UpperCamelCase:      str=False       ,	UpperCamelCase:      Tuple=1       ,	UpperCamelCase:      Any=5       ,	UpperCamelCase:      List[Any]=2       ,	UpperCamelCase:      Dict=1       ,	UpperCamelCase:      Optional[Any]=1       ,	UpperCamelCase:      Any=5       ,	UpperCamelCase:      int=2       ,	UpperCamelCase:      Optional[Any]=0.1       ,	**UpperCamelCase:      Dict       ,	)		->		List[Any]:







											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.'						)
																			snake_case__									=     CONFIG_MAPPING['resnet'](out_features=['stage4']						)
															elif isinstance(UpperCamelCase__       ,	UpperCamelCase__						):
																			snake_case__									=     backbone_config.get('model_type'						)
																			snake_case__									=     CONFIG_MAPPING[backbone_model_type]
																			snake_case__									=     config_class.from_dict(UpperCamelCase__						)
															# set timm attributes to None
															snake_case__							,						snake_case__							,						snake_case__									=     None, None, None

											snake_case__									=     use_timm_backbone
											snake_case__									=     backbone_config
											snake_case__									=     num_channels
											snake_case__									=     num_queries
											snake_case__									=     d_model
											snake_case__									=     encoder_ffn_dim
											snake_case__									=     encoder_layers
											snake_case__									=     encoder_attention_heads
											snake_case__									=     decoder_ffn_dim
											snake_case__									=     decoder_layers
											snake_case__									=     decoder_attention_heads
											snake_case__									=     dropout
											snake_case__									=     attention_dropout
											snake_case__									=     activation_dropout
											snake_case__									=     activation_function
											snake_case__									=     init_std
											snake_case__									=     init_xavier_std
											snake_case__									=     encoder_layerdrop
											snake_case__									=     decoder_layerdrop
											snake_case__									=     encoder_layers
											snake_case__									=     auxiliary_loss
											snake_case__									=     position_embedding_type
											snake_case__									=     backbone
											snake_case__									=     use_pretrained_backbone
											snake_case__									=     dilation
											# Hungarian matcher
											snake_case__									=     class_cost
											snake_case__									=     bbox_cost
											snake_case__									=     giou_cost
											# Loss coefficients
											snake_case__									=     mask_loss_coefficient
											snake_case__									=     dice_loss_coefficient
											snake_case__									=     bbox_loss_coefficient
											snake_case__									=     giou_loss_coefficient
											snake_case__									=     eos_coefficient
											super().__init__(is_encoder_decoder=UpperCamelCase__       ,	**UpperCamelCase__						)
							@property
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											return self.encoder_attention_heads


							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[str]:
											return self.d_model


class   __SCREAMING_SNAKE_CASE(	lowercase_	):
							_UpperCAmelCase     =       version.parse("1.11"	)
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		Tuple:
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											        ('pixel_mask', {0: 'batch'}),
											    ]						)
							@property
							def lowerCAmelCase_ (     self:      List[str]						)		->		str:
											return 1e-5


							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											return 12












__UpperCamelCase					:		List[str]	  =						256
# Modulus to hash a string
__UpperCamelCase					:		int	  =						1000003
def      a_ (      _A      ,   _A  )			->			bool:







				"""simple docstring"""
				snake_case__									=     len(_A  )
				snake_case__									=     len(_A  )
				if p_len > t_len:
								return False

				snake_case__									=     0
				snake_case__									=     0
				snake_case__									=     1

				# Calculating the hash of pattern and substring of text
				for i in range(_A  ):
								snake_case__									=     (ord(pattern[i]  ) + p_hash * alphabet_size) % modulus
								snake_case__									=     (ord(text[i]  ) + text_hash * alphabet_size) % modulus
								if i == p_len - 1:
												continue
								snake_case__									=     (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
								snake_case__									=     (
								    (text_hash - ord(text[i]  ) * modulus_power) * alphabet_size
								    + ord(text[i + p_len]  )
								) % modulus
				return False
def      a_ (      )			->			None:







				"""simple docstring"""
				snake_case__									=     'abc1abc12'
				snake_case__									=     'alskfjaldsabc1abc1abc12k23adsfabcabc'
				snake_case__									=     'alskfjaldsk23adsfabcabc'
				assert rabin_karp(_A      ,   _A  ) and not rabin_karp(_A      ,   _A  )

				# Test 2)
				snake_case__									=     'ABABX'
				snake_case__									=     'ABABZABABYABABX'
				assert rabin_karp(_A      ,   _A  )

				# Test 3)
				snake_case__									=     'AAAB'
				snake_case__									=     'ABAAAAAB'
				assert rabin_karp(_A      ,   _A  )

				# Test 4)
				snake_case__									=     'abcdabcy'
				snake_case__									=     'abcxabcdabxabcdabcdabcy'
				assert rabin_karp(_A      ,   _A  )

				# Test 5)
				snake_case__									=     'Lü'
				snake_case__									=     'Lüsai'
				assert rabin_karp(_A      ,   _A  )
				snake_case__									=     'Lue'
				assert not rabin_karp(_A      ,   _A  )
				print('Success.'  )


if __name__ == "__main__":
							test_rabin_karp()




