

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a       :      Optional[Any]  	=			{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a       :      Union[str, Any]  	=			["LayoutLMTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a       :      str  	=			[
			    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "LayoutLMForMaskedLM",
			    "LayoutLMForSequenceClassification",
			    "LayoutLMForTokenClassification",
			    "LayoutLMForQuestionAnswering",
			    "LayoutLMModel",
			    "LayoutLMPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a       :      Union[str, Any]  	=			[
			    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFLayoutLMForMaskedLM",
			    "TFLayoutLMForSequenceClassification",
			    "TFLayoutLMForTokenClassification",
			    "TFLayoutLMForQuestionAnswering",
			    "TFLayoutLMMainLayer",
			    "TFLayoutLMModel",
			    "TFLayoutLMPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
			from .tokenization_layoutlm import LayoutLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlm import (
						    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMForMaskedLM,
						    LayoutLMForQuestionAnswering,
						    LayoutLMForSequenceClassification,
						    LayoutLMForTokenClassification,
						    LayoutLMModel,
						    LayoutLMPreTrainedModel,
						)
			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_layoutlm import (
						    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMForMaskedLM,
						    TFLayoutLMForQuestionAnswering,
						    TFLayoutLMForSequenceClassification,
						    TFLayoutLMForTokenClassification,
						    TFLayoutLMMainLayer,
						    TFLayoutLMModel,
						    TFLayoutLMPreTrainedModel,
						)

else:
			import sys

			a       :      Union[str, Any]  	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



'''simple docstring'''

import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def 				lowercase (					__magic_name__ = 1			,   __magic_name__ = 1							):



					'''simple docstring'''







					if isinstance(__magic_name__			,   __magic_name__							) or isinstance(__magic_name__			,   __magic_name__							):
										raise TypeError("inputs must be integers."							)
					if (bit_a < 0) or (bit_a < 0):
										raise ValueError("inputs must be positive."							)
					if (math.floor(__magic_name__							) != bit_a) or (math.floor(__magic_name__							) != bit_a):
										raise ValueError("inputs must be exact integers."							)
					if (bit_a > 1) or (bit_a > 1):
										raise ValueError("inputs must be less or equal to 1."							)

					# build registers
					UpperCAmelCase		:							List[Any]        =       QuantumRegister(2			,   "qr"							)
					UpperCAmelCase		:							Optional[int]        =       ClassicalRegister(2			,   "cr"							)

					UpperCAmelCase		:							Optional[int]        =       QuantumCircuit(__magic_name__			,   __magic_name__							)

					# entanglement the qubits
					quantum_circuit.h(1							)
					quantum_circuit.cx(1			,   0							)

					# send the information
					UpperCAmelCase		:							Any        =       str(__magic_name__							) + str(__magic_name__							)

					if c_information == "11":
										quantum_circuit.x(1							)
										quantum_circuit.z(1							)
					elif c_information == "10":
										quantum_circuit.z(1							)
					elif c_information == "01":
										quantum_circuit.x(1							)
					else:
										quantum_circuit.i(1							)

					# unentangled the circuit
					quantum_circuit.cx(1			,   0							)
					quantum_circuit.h(1							)

					# measure the circuit
					quantum_circuit.measure(__magic_name__			,   __magic_name__							)

					UpperCAmelCase		:							Tuple        =       Aer.get_backend("aer_simulator"							)
					UpperCAmelCase		:							Any        =       execute(__magic_name__			,   __magic_name__			,   shots=1000							)

					return job.result().get_counts(__magic_name__							)


if __name__ == "__main__":
				print(F'Counts for classical state send: {superdense_coding(1,1)}')
