



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   ["MarkupLMTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MarkupLMForQuestionAnswering",
							    "MarkupLMForSequenceClassification",
							    "MarkupLMForTokenClassification",
							    "MarkupLMModel",
							    "MarkupLMPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)








"""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)





