




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 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    tempfile.mkdtemp()

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

          __UpperCamelCase			=    GitProcessor(snake_case			,							snake_case					)

          processor.save_pretrained(self.tmpdirname					)







      def    snake_case						(  self       :			Optional[Any]			,							**snake_case       :			List[Any]					):
          return AutoProcessor.from_pretrained(self.tmpdirname			,							**snake_case					).tokenizer







      def    snake_case						(  self       :			Optional[int]			,							**snake_case       :			Dict					):
          return AutoProcessor.from_pretrained(self.tmpdirname			,							**snake_case					).image_processor







      def    snake_case						(  self       :			List[Any]					):
          shutil.rmtree(self.tmpdirname					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta					)]

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

          return image_inputs







      def    snake_case						(  self       :			List[Any]					):
          __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=snake_case			,							padding_value=1.0					)

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

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

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







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    self.prepare_image_inputs()

          __UpperCamelCase			=    image_processor(snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(images=snake_case			,							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    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    '''lower newer'''

          __UpperCamelCase			=    processor(text=snake_case					)

          __UpperCamelCase			=    tokenizer(snake_case			,							return_token_type_ids=snake_case					)

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







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

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

          __UpperCamelCase			=    processor(text=snake_case			,							images=snake_case					)

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

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







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

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

          __UpperCamelCase			=    processor.batch_decode(snake_case					)
          __UpperCamelCase			=    tokenizer.batch_decode(snake_case					)

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

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

          __UpperCamelCase			=    processor(text=snake_case			,							images=snake_case					)

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











import numpy as np
import qiskit







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.QuantumCircuit:



						'''simple docstring'''






						UpperCAmelCase_					=	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:
												UpperCAmelCase_					=	np.random.randint(1				,   2**num_qubits      )
												# Next, format 'b' as a binary string of length 'n', padded with zeros:
												UpperCAmelCase_					=	format(__UpperCAmelCase				,   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(__UpperCAmelCase      ):
																		if bit == "1":
																								oracle_qc.x(__UpperCAmelCase      )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
												for index in range(__UpperCAmelCase      ):
																		oracle_qc.cx(__UpperCAmelCase				,   __UpperCAmelCase      )
												# Next, place the final X-gates
												for index, bit in enumerate(__UpperCAmelCase      ):
																		if bit == "1":
																								oracle_qc.x(__UpperCAmelCase      )

    # 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)
												UpperCAmelCase_					=	np.random.randint(2      )
												if output == 1:
																		oracle_qc.x(__UpperCAmelCase      )

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







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.QuantumCircuit:



						'''simple docstring'''






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

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

						return dj_circuit







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.result.counts.Counts:



						'''simple docstring'''






						UpperCAmelCase_					=	qiskit.Aer.get_backend('''aer_simulator'''      )

						UpperCAmelCase_					=	dj_oracle(__UpperCAmelCase				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	dj_algorithm(__UpperCAmelCase				,   __UpperCAmelCase      )

						# Execute the circuit on the simulator
						UpperCAmelCase_					=	qiskit.execute(__UpperCAmelCase				,   __UpperCAmelCase				,   shots=1000      )

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


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





