

"""simple docstring"""




from typing import TYPE_CHECKING

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


__A     :    Any    =		{
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Any    =		['DebertaV2TokenizerFast']

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Any    =		[
	    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDebertaV2ForMaskedLM',
	    'TFDebertaV2ForQuestionAnswering',
	    'TFDebertaV2ForSequenceClassification',
	    'TFDebertaV2ForTokenClassification',
	    'TFDebertaV2Model',
	    'TFDebertaV2PreTrainedModel',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    List[Any]    =		[
	    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DebertaV2ForMaskedLM',
	    'DebertaV2ForMultipleChoice',
	    'DebertaV2ForQuestionAnswering',
	    'DebertaV2ForSequenceClassification',
	    'DebertaV2ForTokenClassification',
	    'DebertaV2Model',
	    'DebertaV2PreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deberta_va import (
	    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DebertaVaConfig,
	    DebertaVaOnnxConfig,
	)
	from .tokenization_deberta_va import DebertaVaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deberta_va import (
		    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDebertaVaForMaskedLM,
		    TFDebertaVaForQuestionAnswering,
		    TFDebertaVaForSequenceClassification,
		    TFDebertaVaForTokenClassification,
		    TFDebertaVaModel,
		    TFDebertaVaPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deberta_va import (
		    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DebertaVaForMaskedLM,
		    DebertaVaForMultipleChoice,
		    DebertaVaForQuestionAnswering,
		    DebertaVaForSequenceClassification,
		    DebertaVaForTokenClassification,
		    DebertaVaModel,
		    DebertaVaPreTrainedModel,
		)

else:
	import sys

	__A     :    List[Any]    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""




import doctest

import projectq
from projectq.ops import H, Measure
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					quantum_engine.allocate_qubit()
					H | qubit
					Measure | qubit
					return int(lowercase__      )


if __name__ == "__main__":
	doctest.testmod()

	# initialises a new quantum backend
	__A     :    Dict    =		projectq.MainEngine()

	# Generate a list of 10 random numbers
	__A     :    Any    =		[get_random_number(quantum_engine) for _ in range(10)]

	# Flushes the quantum engine from memory
	quantum_engine.flush()

	print('Random numbers', random_numbers_list)





