
import math

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = 3    )			->   qiskit.result.counts.Counts:
       if isinstance(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              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(SCREAMING_SNAKE_CASE__    ) != 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)."""    )

       lowercase							:					str	     =				QuantumRegister(SCREAMING_SNAKE_CASE__ ,     """qr"""    )
       lowercase							:					Optional[Any]	     =				ClassicalRegister(SCREAMING_SNAKE_CASE__ ,     """cr"""    )

       lowercase							:					str	     =				QuantumCircuit(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       lowercase							:					List[str]	     =				number_of_qubits

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

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

       # measure all the qubits
       quantum_circuit.measure(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       # simulate with 10000 shots
       lowercase							:					List[str]	     =				Aer.get_backend("""qasm_simulator"""    )
       lowercase							:					Optional[int]	     =				execute(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     shots=10_000    )

       return job.result().get_counts(SCREAMING_SNAKE_CASE__    )


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


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           if depth_multiplier <= 0:
                  raise ValueError("""depth_multiplier must be greater than zero."""				)

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.task == "image-classification":
                  return OrderedDict([("""logits""", {0: """batch"""})]				)
           else:
                  return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
