


import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor

class 						__a (							UpperCAmelCase     ):
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'hidden_sizes'		)		)
									self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'num_attention_heads'		)		)
									self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'num_encoder_blocks'		)		)



class 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=13     ,       _SCREAMING_SNAKE_CASE=64     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]     ,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]     ,       _SCREAMING_SNAKE_CASE=[16, 32, 64, 128]     ,       _SCREAMING_SNAKE_CASE=[1, 4, 8, 16]     ,       _SCREAMING_SNAKE_CASE=[1, 2, 4, 8]     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=None     ,       )					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     image_size
									_UpperCAmelCase											=     num_channels
									_UpperCAmelCase											=     num_encoder_blocks
									_UpperCAmelCase											=     sr_ratios
									_UpperCAmelCase											=     depths
									_UpperCAmelCase											=     hidden_sizes
									_UpperCAmelCase											=     downsampling_rates
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     is_training
									_UpperCAmelCase											=     use_labels
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     num_labels
									_UpperCAmelCase											=     scope
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_UpperCAmelCase											=     None
									if self.use_labels:
																_UpperCAmelCase											=     ids_tensor([self.batch_size, self.image_size, self.image_size]     ,       self.num_labels		)

									_UpperCAmelCase											=     self.get_config()
									return config, pixel_values, labels
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									return SegformerConfig(
									    image_size=self.image_size     ,       num_channels=self.num_channels     ,       num_encoder_blocks=self.num_encoder_blocks     ,       depths=self.depths     ,       hidden_sizes=self.hidden_sizes     ,       num_attention_heads=self.num_attention_heads     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       initializer_range=self.initializer_range     ,       )
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     SegformerModel(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     _UpperCAmelCase											=     self.image_size // (self.downsampling_rates[-1] * 2)
									self.parent.assertEqual(
									    result.last_hidden_state.shape     ,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     SegformerForSemanticSegmentation(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(
									    result.logits.shape     ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(
									    result.logits.shape     ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
									self.parent.assertGreater(result.loss     ,       0.0		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     1
									_UpperCAmelCase											=     SegformerForSemanticSegmentation(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     torch.randint(0     ,       1     ,       (self.batch_size, self.image_size, self.image_size)		).to(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertGreater(result.loss     ,       0.0		)






		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.prepare_config_and_inputs()
									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     config_and_inputs
									_UpperCAmelCase											=     {'pixel_values': pixel_values}
									return config, inputs_dict



@require_torch
class 						__a (							UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase     ):
		_a      :    str           =      (
		    (
		        SegformerModel,
		        SegformerForSemanticSegmentation,
		        SegformerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_a      :    Union[str, Any]           =      (
		    {
		        'feature-extraction': SegformerModel,
		        'image-classification': SegformerForImageClassification,
		        'image-segmentation': SegformerForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_a      :    List[Any]           =      True
		_a      :    List[str]           =      False
		_a      :    Optional[int]           =      False
		_a      :    Any           =      False
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     SegformerModelTester(self		)
									_UpperCAmelCase											=     SegformerConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_binary_image_segmentation(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*_SCREAMING_SNAKE_CASE		)
		@unittest.skip('SegFormer does not use inputs_embeds'		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									pass
		@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									pass
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     inspect.signature(model.forward		)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCAmelCase											=     [*signature.parameters.keys()]

																_UpperCAmelCase											=     ['pixel_values']
																self.assertListEqual(arg_names[:1]     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase											=     True

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     True
																_UpperCAmelCase											=     False
																_UpperCAmelCase											=     True
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																model.to(_SCREAMING_SNAKE_CASE		)
																model.eval()
																with torch.no_grad():
																							_UpperCAmelCase											=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     outputs.attentions

																_UpperCAmelCase											=     sum(self.model_tester.depths		)
																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       _SCREAMING_SNAKE_CASE		)

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																_UpperCAmelCase											=     True
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																model.to(_SCREAMING_SNAKE_CASE		)
																model.eval()
																with torch.no_grad():
																							_UpperCAmelCase											=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     outputs.attentions

																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       _SCREAMING_SNAKE_CASE		)

																# verify the first attentions (first block, first layer)
																_UpperCAmelCase											=     (self.model_tester.image_size // 4) ** 2
																_UpperCAmelCase											=     (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																self.assertListEqual(
																    list(attentions[0].shape[-3:]		)     ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,       )

																# verify the last attentions (last block, last layer)
																_UpperCAmelCase											=     (self.model_tester.image_size // 32) ** 2
																_UpperCAmelCase											=     (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
																self.assertListEqual(
																    list(attentions[-1].shape[-3:]		)     ,       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]     ,       )
																_UpperCAmelCase											=     len(_SCREAMING_SNAKE_CASE		)

																# Check attention is always last and order is fine
																_UpperCAmelCase											=     True
																_UpperCAmelCase											=     True
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																model.to(_SCREAMING_SNAKE_CASE		)
																model.eval()
																with torch.no_grad():
																							_UpperCAmelCase											=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)

																self.assertEqual(out_len + 1     ,       len(_SCREAMING_SNAKE_CASE		)		)

																_UpperCAmelCase											=     outputs.attentions

																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       _SCREAMING_SNAKE_CASE		)
																# verify the first attentions (first block, first layer)
																_UpperCAmelCase											=     (self.model_tester.image_size // 4) ** 2
																_UpperCAmelCase											=     (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]		)     ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,       )
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									def check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																model.to(_SCREAMING_SNAKE_CASE		)
																model.eval()

																with torch.no_grad():
																							_UpperCAmelCase											=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)

																_UpperCAmelCase											=     outputs.hidden_states

																_UpperCAmelCase											=     self.model_tester.num_encoder_blocks
																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       _SCREAMING_SNAKE_CASE		)

																# verify the first hidden states (first block)
																self.assertListEqual(
																    list(hidden_states[0].shape[-3:]		)     ,       [
																        self.model_tester.hidden_sizes[0],
																        self.model_tester.image_size // 4,
																        self.model_tester.image_size // 4,
																    ]     ,       )

									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     True
																check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_UpperCAmelCase											=     True

																check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									if not self.model_tester.is_training:
																return

									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase											=     True

									for model_class in self.all_model_classes:
																if model_class in get_values(_SCREAMING_SNAKE_CASE		):
																							continue

																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																model.to(_SCREAMING_SNAKE_CASE		)
																model.train()
																_UpperCAmelCase											=     self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_labels=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     model(**_SCREAMING_SNAKE_CASE		).loss
																loss.backward()
		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									pass






		@slow
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase											=     SegformerModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)



def 			lowerCAmelCase__					(     )    ->						Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
							return image







@require_torch
class 						__a (							unittest.TestCase     ):
		@slow
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     SegformerImageProcessor(
									    image_scale=(512, 512)     ,       keep_ratio=_SCREAMING_SNAKE_CASE     ,       align=_SCREAMING_SNAKE_CASE     ,       do_random_crop=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'		).to(
									    _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     prepare_img()
									_UpperCAmelCase											=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'		)
									_UpperCAmelCase											=     encoded_inputs.pixel_values.to(_SCREAMING_SNAKE_CASE		)

									with torch.no_grad():
																_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     torch.Size((1, model.config.num_labels, 128, 128)		)
									self.assertEqual(outputs.logits.shape     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     torch.tensor(
									    [
									        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
									        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
									        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
									    ]		).to(_SCREAMING_SNAKE_CASE		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,       _SCREAMING_SNAKE_CASE     ,       atol=1e-4		)		)
		@slow
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     SegformerImageProcessor(
									    image_scale=(512, 512)     ,       keep_ratio=_SCREAMING_SNAKE_CASE     ,       align=_SCREAMING_SNAKE_CASE     ,       do_random_crop=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     SegformerForSemanticSegmentation.from_pretrained(
									    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'		).to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     prepare_img()
									_UpperCAmelCase											=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'		)
									_UpperCAmelCase											=     encoded_inputs.pixel_values.to(_SCREAMING_SNAKE_CASE		)

									with torch.no_grad():
																_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     torch.Size((1, model.config.num_labels, 128, 128)		)
									self.assertEqual(outputs.logits.shape     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     torch.tensor(
									    [
									        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
									        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
									        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
									    ]		).to(_SCREAMING_SNAKE_CASE		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,       _SCREAMING_SNAKE_CASE     ,       atol=1e-1		)		)






		@slow
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     SegformerImageProcessor(
									    image_scale=(512, 512)     ,       keep_ratio=_SCREAMING_SNAKE_CASE     ,       align=_SCREAMING_SNAKE_CASE     ,       do_random_crop=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'		).to(
									    _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     prepare_img()
									_UpperCAmelCase											=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'		)
									_UpperCAmelCase											=     encoded_inputs.pixel_values.to(_SCREAMING_SNAKE_CASE		)

									with torch.no_grad():
																_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     outputs.logits.detach().cpu()

									_UpperCAmelCase											=     image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE     ,       target_sizes=[(500, 300)]		)
									_UpperCAmelCase											=     torch.Size((500, 300)		)
									self.assertEqual(segmentation[0].shape     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     torch.Size((128, 128)		)
									self.assertEqual(segmentation[0].shape     ,       _SCREAMING_SNAKE_CASE		)




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)}''')
