





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		'''dpt'''




					def __init__(						self			,  lowerCAmelCase_=768			,  lowerCAmelCase_=12			,  lowerCAmelCase_=12			,  lowerCAmelCase_=3072			,  lowerCAmelCase_="gelu"			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.02			,  lowerCAmelCase_=1E-12			,  lowerCAmelCase_=384			,  lowerCAmelCase_=16			,  lowerCAmelCase_=3			,  lowerCAmelCase_=False			,  lowerCAmelCase_=True			,  lowerCAmelCase_=[2, 5, 8, 11]			,  lowerCAmelCase_="project"			,  lowerCAmelCase_=[4, 2, 1, 0.5]			,  lowerCAmelCase_=[96, 192, 384, 768]			,  lowerCAmelCase_=256			,  lowerCAmelCase_=-1			,  lowerCAmelCase_=False			,  lowerCAmelCase_=True			,  lowerCAmelCase_=0.4			,  lowerCAmelCase_=255			,  lowerCAmelCase_=0.1			,  lowerCAmelCase_=[1, 1024, 24, 24]			,  lowerCAmelCase_=[0, 1]			,  lowerCAmelCase_=None			,  **lowerCAmelCase_			,  )						->							Tuple:
										super().__init__(**lowerCAmelCase_					)

										_snake_case													=			hidden_size
										_snake_case													=			is_hybrid

										if self.is_hybrid:
															if backbone_config is None:
																				logger.info('Initializing the config with a `BiT` backbone.'					)
																				_snake_case													=			{
																				    'global_padding': 'same',
																				    'layer_type': 'bottleneck',
																				    'depths': [3, 4, 9],
																				    'out_features': ['stage1', 'stage2', 'stage3'],
																				    'embedding_dynamic_padding': True,
																				}
																				_snake_case													=			BitConfig(**lowerCAmelCase_					)
															elif isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
																				logger.info('Initializing the config with a `BiT` backbone.'					)
																				_snake_case													=			BitConfig(**lowerCAmelCase_					)
															elif isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
																				_snake_case													=			backbone_config
															else:
																				raise ValueError(
																				    F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''					)

															_snake_case													=			backbone_featmap_shape
															_snake_case													=			neck_ignore_stages

															if readout_type != "project":
																				raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'					)
										else:
															_snake_case													=			None
															_snake_case													=			None
															_snake_case													=			[]

										_snake_case													=			num_hidden_layers
										_snake_case													=			num_attention_heads
										_snake_case													=			intermediate_size
										_snake_case													=			hidden_act
										_snake_case													=			hidden_dropout_prob
										_snake_case													=			attention_probs_dropout_prob
										_snake_case													=			initializer_range
										_snake_case													=			layer_norm_eps
										_snake_case													=			image_size
										_snake_case													=			patch_size
										_snake_case													=			num_channels
										_snake_case													=			qkv_bias
										_snake_case													=			backbone_out_indices
										if readout_type not in ["ignore", "add", "project"]:
															raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'					)
										_snake_case													=			readout_type
										_snake_case													=			reassemble_factors
										_snake_case													=			neck_hidden_sizes
										_snake_case													=			fusion_hidden_size
										_snake_case													=			head_in_index
										_snake_case													=			use_batch_norm_in_fusion_residual
										# auxiliary head attributes (semantic segmentation)
										_snake_case													=			use_auxiliary_head
										_snake_case													=			auxiliary_loss_weight
										_snake_case													=			semantic_loss_ignore_index
										_snake_case													=			semantic_classifier_dropout






					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			copy.deepcopy(self.__dict__					)

										if output["backbone_config"] is not None:
															_snake_case													=			self.backbone_config.to_dict()

										_snake_case													=			self.__class__.model_type
										return output










def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			[0 for i in range(r + 1  )]
					# nc0 = 1
					_snake_case													=			1
					for i in range(1    ,      n + 1  ):
										# to compute current row from previous row.
										_snake_case													=			min(UpperCamelCase__    ,      UpperCamelCase__  )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]




print(binomial_coefficient(n=10, r=5))



