






import numpy as np
import qiskit



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							qiskit.QuantumCircuit(num_qubits + 1	)

		# First, let's deal with the case in which oracle is balanced
		if case == "balanced":
				# First generate a random number that tells us which CNOTs to
				# wrap in X-gates:
				__A    =							np.random.randint(1					,     2**num_qubits	)
				# Next, format 'b' as a binary string of length 'n', padded with zeros:
				__A    =							format(lowerCAmelCase__					,     F"""0{num_qubits}b"""	)
				# Next, we place the first X-gates. Each digit in our binary string
				# corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
				# we apply an X-gate to that qubit:
				for index, bit in enumerate(lowerCAmelCase__	):
						if bit == "1":
								oracle_qc.x(lowerCAmelCase__	)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
				for index in range(lowerCAmelCase__	):
						oracle_qc.cx(lowerCAmelCase__					,     lowerCAmelCase__	)
				# Next, place the final X-gates
				for index, bit in enumerate(lowerCAmelCase__	):
						if bit == "1":
								oracle_qc.x(lowerCAmelCase__	)

    # Case in which oracle is constant
		if case == "constant":
				# First decide what the fixed output of the oracle will be
				# (either always 0 or always 1)
				__A    =							np.random.randint(2	)
				if output == 1:
						oracle_qc.x(lowerCAmelCase__	)

		__A    =							oracle_qc.to_gate()
		__A    =							"Oracle"  # To show when we display the circuit
		return oracle_gate



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							qiskit.QuantumCircuit(num_qubits + 1					,     lowerCAmelCase__	)
		# Set up the output qubit:
		dj_circuit.x(lowerCAmelCase__	)
		dj_circuit.h(lowerCAmelCase__	)
		# And set up the input register:
		for qubit in range(lowerCAmelCase__	):
				dj_circuit.h(lowerCAmelCase__	)
		# Let's append the oracle gate to our circuit:
		dj_circuit.append(lowerCAmelCase__					,     range(num_qubits + 1	)	)
		# Finally, perform the H-gates again and measure:
		for qubit in range(lowerCAmelCase__	):
				dj_circuit.h(lowerCAmelCase__	)

		for i in range(lowerCAmelCase__	):
				dj_circuit.measure(lowerCAmelCase__					,     lowerCAmelCase__	)

		return dj_circuit



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							qiskit.Aer.get_backend("aer_simulator"	)

		__A    =							dj_oracle(lowerCAmelCase__					,     lowerCAmelCase__	)
		__A    =							dj_algorithm(lowerCAmelCase__					,     lowerCAmelCase__	)

		# Execute the circuit on the simulator
		__A    =							qiskit.execute(lowerCAmelCase__					,     lowerCAmelCase__					,     shots=1000	)

		# Return the histogram data of the results of the experiment.
		return job.result().get_counts(lowerCAmelCase__	)


if __name__ == "__main__":
					print(f"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}""")
					print(f"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}""")








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 			__A(    unittest.TestCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    tempfile.mkdtemp()

            _UpperCamelCase														=    CLIPImageProcessor()
            _UpperCamelCase														=    BertTokenizer.from_pretrained(
                '''hf-internal-testing/tiny-random-BertModel''',							model_input_names=['''input_ids''', '''attention_mask''']   )

            _UpperCamelCase														=    GitProcessor(A,							A   )

            processor.save_pretrained(self.tmpdirname   )

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,							**A   ).tokenizer

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,							**A   ).image_processor

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    [np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta   )]

            _UpperCamelCase														=    [Image.fromarray(np.moveaxis(A,							0,							-1   )   ) for x in image_inputs]

            return image_inputs

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GitProcessor(tokenizer=self.get_tokenizer(),							image_processor=self.get_image_processor()   )
            processor.save_pretrained(self.tmpdirname   )

            _UpperCamelCase														=    self.get_tokenizer(bos_token='''(BOS)''',							eos_token='''(EOS)'''   )
            _UpperCamelCase														=    self.get_image_processor(do_normalize=A,							padding_value=1.0   )

            _UpperCamelCase														=    GitProcessor.from_pretrained(
                self.tmpdirname,							bos_token='''(BOS)''',							eos_token='''(EOS)''',							do_normalize=A,							padding_value=1.0   )

            self.assertEqual(processor.tokenizer.get_vocab(),							tokenizer_add_kwargs.get_vocab()   )
            self.assertIsInstance(processor.tokenizer,							A   )

            self.assertEqual(processor.image_processor.to_json_string(),							image_processor_add_kwargs.to_json_string()   )
            self.assertIsInstance(processor.image_processor,							A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    self.prepare_image_inputs()

            _UpperCamelCase														=    image_processor(A,							return_tensors='''np'''   )
            _UpperCamelCase														=    processor(images=A,							return_tensors='''np'''   )

            for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum(),							input_processor[key].sum(),							delta=1E-2   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    '''lower newer'''

            _UpperCamelCase														=    processor(text=A   )

            _UpperCamelCase														=    tokenizer(A,							return_token_type_ids=A   )

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key],							encoded_processor[key]   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    '''lower newer'''
            _UpperCamelCase														=    self.prepare_image_inputs()

            _UpperCamelCase														=    processor(text=A,							images=A   )

            self.assertListEqual(list(inputs.keys()   ),							['''input_ids''', '''attention_mask''', '''pixel_values''']   )

            # test if it raises when no input is passed
            with pytest.raises(A   ):
                   processor()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _UpperCamelCase														=    processor.batch_decode(A   )
            _UpperCamelCase														=    tokenizer.batch_decode(A   )

            self.assertListEqual(A,							A   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    '''lower newer'''
            _UpperCamelCase														=    self.prepare_image_inputs()

            _UpperCamelCase														=    processor(text=A,							images=A   )

            # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
            self.assertListEqual(list(inputs.keys()   ),							['''input_ids''', '''attention_mask''', '''pixel_values''']   )






