






def _UpperCAmelCase (						a    :						int			):
							snake_case__							=							len(__lowerCamelCase			)
							snake_case__							=							sum(__lowerCamelCase			)

							snake_case__							=							[[False for x in range(s + 1			)] for y in range(n + 1			)]

							for i in range(1	,       n + 1			):
														snake_case__							=							True

							for i in range(1	,       s + 1			):
														snake_case__							=							False

							for i in range(1	,       n + 1			):
														for j in range(1	,       s + 1			):
																					snake_case__							=							dp[i][j - 1]

																					if arr[i - 1] <= j:
																												snake_case__							=							dp[i][j] or dp[i - 1][j - arr[i - 1]]

							for j in range(int(s / 2			)	,       -1	,       -1			):
														if dp[n][j] is True:
																					snake_case__							=							s - 2 * j
																					break

							return diff




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

