





"""simple docstring"""




import numpy as np
import qiskit





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

    # 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     :   str      =       np.random.randint(2  )
												if output == 1:
																		oracle_qc.x(_lowercase  )

						_UpperCamelCase     :   Union[str, Any]      =       oracle_qc.to_gate()
						_UpperCamelCase     :   Dict      =       '''Oracle'''  # To show when we display the circuit
						return oracle_gate





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

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

						return dj_circuit





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       qiskit.Aer.get_backend('''aer_simulator'''  )

						_UpperCamelCase     :   Tuple      =       dj_oracle(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       dj_algorithm(_lowercase			,		_lowercase  )

						# Execute the circuit on the simulator
						_UpperCamelCase     :   Dict      =       qiskit.execute(_lowercase			,		_lowercase			,		shots=1000  )

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


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











import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class __lowerCamelCase					( snake_case__	,			unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								= GPTNeoXJapaneseTokenizer
	UpperCamelCase__								= False
	UpperCamelCase__								= {"do_clean_text": False, "add_prefix_space": False}

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase							   =     [
							    'こん',
							    'こんに',
							    'にちは',
							    'ばんは',
							    '世界,㔺界',
							    '、',
							    '。',
							    '<BR>',
							    '<SP>',
							    '<TAB>',
							    '<URL>',
							    '<EMAIL>',
							    '<TEL>',
							    '<DATE>',
							    '<PRICE>',
							    '<BLOCK>',
							    '<KIGOU>',
							    '<U2000U2BFF>',
							    '<|emoji1|>',
							    '<unk>',
							    '<|startoftext|>',
							    '<|endoftext|>',
							]
							_UpperCAmelCase							   =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
							_UpperCAmelCase							   =     {'unk_token': '<unk>'}

							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['vocab_file']       )
							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['emoji_file']       )
							with open(self.vocab_file			,       'w'			,       encoding='utf-8'       ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
							with open(self.emoji_file			,       'w'       ) as emoji_writer:
													emoji_writer.write(json.dumps(UpperCAmelCase       )       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							kwargs.update(self.special_tokens_map       )
							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、世界。😀'
							return input_text, output_text

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_input_output_texts(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode(UpperCAmelCase			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.decode(UpperCAmelCase			,       clean_up_tokenization_spaces=UpperCAmelCase       )
							return text, ids

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_tokenizer()

							# Testing tokenization
							_UpperCAmelCase							   =     'こんにちは、世界。　こんばんは、㔺界。'
							_UpperCAmelCase							   =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
							_UpperCAmelCase							   =     tokenizer.tokenize(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids without special tokens
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids with special tokens
							_UpperCAmelCase							   =     tokens + [tokenizer.unk_token]
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

	@slow
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'       )

							_UpperCAmelCase							   =     tokenizer.encode('ありがとう。'			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode('どういたしまして。'			,       add_special_tokens=UpperCAmelCase       )

							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase			,       UpperCAmelCase       )

							assert encoded_sentence == ids_a
							assert encoded_pair == ids_a + ids_a

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass
