
import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute






def 	a_		(	__lowercase       :     int = 3			)			->  qiskit.result.counts.Counts:
			if isinstance(__lowercase	, __lowercase			):
						raise TypeError('number of qubits must be a integer.'			)
			if number_of_qubits <= 0:
						raise ValueError('number of qubits must be > 0.'			)
			if math.floor(__lowercase			) != number_of_qubits:
						raise ValueError('number of qubits must be exact integer.'			)
			if number_of_qubits > 10:
						raise ValueError('number of qubits too large to simulate(>10).'			)

			_snake_case          =      QuantumRegister(__lowercase	, 'qr'			)
			_snake_case          =      ClassicalRegister(__lowercase	, 'cr'			)

			_snake_case          =      QuantumCircuit(__lowercase	, __lowercase			)

			_snake_case          =      number_of_qubits

			for i in range(__lowercase			):
						quantum_circuit.h(number_of_qubits - i - 1			)
						counter -= 1
						for j in range(__lowercase			):
									quantum_circuit.cp(np.pi / 2 ** (counter - j)	, __lowercase	, __lowercase			)

			for k in range(number_of_qubits // 2			):
						quantum_circuit.swap(__lowercase	, number_of_qubits - k - 1			)

			# measure all the qubits
			quantum_circuit.measure(__lowercase	, __lowercase			)
			# simulate with 10000 shots
			_snake_case          =      Aer.get_backend('qasm_simulator'			)
			_snake_case          =      execute(__lowercase	, __lowercase	, shots=10_000			)

			return job.result().get_counts(__lowercase			)


if __name__ == "__main__":
			print(
			    F'Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'
			)

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:    Tuple          =      logging.get_logger(__name__)

_lowerCamelCase	:    Union[str, Any]          =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "canine"



	def __init__(  self     :   int      ,  lowercase     :   Optional[int]=768      ,  lowercase     :   Tuple=12      ,  lowercase     :   Union[str, Any]=12      ,  lowercase     :   Optional[int]=3_072      ,  lowercase     :   Tuple="gelu"      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   Tuple=0.1      ,  lowercase     :   int=16_384      ,  lowercase     :   Optional[int]=16      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   Optional[Any]=1E-12      ,  lowercase     :   Optional[Any]=0      ,  lowercase     :   Dict=0xE000      ,  lowercase     :   Optional[Any]=0xE001      ,  lowercase     :   Union[str, Any]=4      ,  lowercase     :   str=4      ,  lowercase     :   Optional[int]=8      ,  lowercase     :   List[str]=16_384      ,  lowercase     :   Union[str, Any]=128      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase      ,  bos_token_id=lowercase      ,  eos_token_id=lowercase      ,  **lowercase     )

				_snake_case          =      max_position_embeddings
				_snake_case          =      hidden_size
				_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          =      type_vocab_size
				_snake_case          =      layer_norm_eps

				# Character config:
				_snake_case          =      downsampling_rate
				_snake_case          =      upsampling_kernel_size
				_snake_case          =      num_hash_functions
				_snake_case          =      num_hash_buckets
				_snake_case          =      local_transformer_stride