

import copy

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


lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      {
    '''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       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Union[str, Any]		     =							"dpt"







			def __init__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Tuple=7_6_8				,  _UpperCamelCase					:							Dict=1_2				,  _UpperCamelCase					:							Union[str, Any]=1_2				,  _UpperCamelCase					:							List[Any]=3_0_7_2				,  _UpperCamelCase					:							Dict="gelu"				,  _UpperCamelCase					:							Union[str, Any]=0.0				,  _UpperCamelCase					:							Optional[int]=0.0				,  _UpperCamelCase					:							Optional[int]=0.02				,  _UpperCamelCase					:							List[str]=1e-12				,  _UpperCamelCase					:							Any=3_8_4				,  _UpperCamelCase					:							int=1_6				,  _UpperCamelCase					:							Any=3				,  _UpperCamelCase					:							Dict=False				,  _UpperCamelCase					:							str=True				,  _UpperCamelCase					:							Union[str, Any]=[2, 5, 8, 1_1]				,  _UpperCamelCase					:							List[str]="project"				,  _UpperCamelCase					:							Optional[int]=[4, 2, 1, 0.5]				,  _UpperCamelCase					:							Dict=[9_6, 1_9_2, 3_8_4, 7_6_8]				,  _UpperCamelCase					:							Dict=2_5_6				,  _UpperCamelCase					:							Optional[Any]=-1				,  _UpperCamelCase					:							int=False				,  _UpperCamelCase					:							Optional[int]=True				,  _UpperCamelCase					:							str=0.4				,  _UpperCamelCase					:							Tuple=2_5_5				,  _UpperCamelCase					:							Union[str, Any]=0.1				,  _UpperCamelCase					:							Tuple=[1, 1_0_2_4, 2_4, 2_4]				,  _UpperCamelCase					:							List[str]=[0, 1]				,  _UpperCamelCase					:							List[Any]=None				,  **_UpperCamelCase					:							Dict				,  ) ->Any:
									super().__init__(**_UpperCamelCase				)

									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(**_UpperCamelCase				)
															elif isinstance(_UpperCamelCase				,  _UpperCamelCase				):
																					logger.info('''Initializing the config with a `BiT` backbone.'''				)
																					snake_case_  =      BitConfig(**_UpperCamelCase				)
															elif isinstance(_UpperCamelCase				,  _UpperCamelCase				):
																					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 							snake_case__(	self					:							List[str]				) ->List[Any]:
									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



import math

import qiskit

def 			lowerCAmelCase__					(     a__:						int = 1     ,    a__:						int = 1     ,    a__:						int = 1			)    ->						qiskit.result.counts.Counts:


							'''simple docstring'''





							if (
							    isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							):
														raise TypeError('inputs must be integers.'			)

							if (input_a < 0) or (input_a < 0) or (carry_in < 0):
														raise ValueError('inputs must be positive.'			)

							if (
							    (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != carry_in)
							):
														raise ValueError('inputs must be exact integers.'			)

							if (input_a > 2) or (input_a > 2) or (carry_in > 2):
														raise ValueError('inputs must be less or equal to 2.'			)

							# build registers
							_UpperCAmelCase											=     qiskit.QuantumRegister(4     ,    'qr'			)
							_UpperCAmelCase											=     qiskit.ClassicalRegister(2     ,    'cr'			)
							# list the entries
							_UpperCAmelCase											=     [input_a, input_a, carry_in]

							_UpperCAmelCase											=     qiskit.QuantumCircuit(a__     ,    a__			)

							for i in range(0     ,    3			):
														if entry[i] == 2:
																					quantum_circuit.h(a__			)  # for hadamard entries
														elif entry[i] == 1:
																					quantum_circuit.x(a__			)  # for 1 entries
														elif entry[i] == 0:
																					quantum_circuit.i(a__			)  # for 0 entries

    # build the circuit
							quantum_circuit.ccx(0     ,    1     ,    3			)  # ccx = toffoli gate
							quantum_circuit.cx(0     ,    1			)
							quantum_circuit.ccx(1     ,    2     ,    3			)
							quantum_circuit.cx(1     ,    2			)
							quantum_circuit.cx(0     ,    1			)

							quantum_circuit.measure([2, 3]     ,    a__			)  # measure the last two qbits

							_UpperCAmelCase											=     qiskit.Aer.get_backend('aer_simulator'			)
							_UpperCAmelCase											=     qiskit.execute(a__     ,    a__     ,    shots=1_0_0_0			)

							return job.result().get_counts(a__			)


if __name__ == "__main__":
		print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
