"""simple docstring"""







from typing import TYPE_CHECKING

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


_a					:					List[str]=			{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a					:					List[Any]=			[
	    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Data2VecAudioForAudioFrameClassification",
	    "Data2VecAudioForCTC",
	    "Data2VecAudioForSequenceClassification",
	    "Data2VecAudioForXVector",
	    "Data2VecAudioModel",
	    "Data2VecAudioPreTrainedModel",
	]
	_a					:					Any=			[
	    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Data2VecTextForCausalLM",
	    "Data2VecTextForMaskedLM",
	    "Data2VecTextForMultipleChoice",
	    "Data2VecTextForQuestionAnswering",
	    "Data2VecTextForSequenceClassification",
	    "Data2VecTextForTokenClassification",
	    "Data2VecTextModel",
	    "Data2VecTextPreTrainedModel",
	]
	_a					:					List[Any]=			[
	    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Data2VecVisionForImageClassification",
	    "Data2VecVisionForMaskedImageModeling",
	    "Data2VecVisionForSemanticSegmentation",
	    "Data2VecVisionModel",
	    "Data2VecVisionPreTrainedModel",
	]

if is_tf_available():
	_a					:					Optional[int]=			[
	    "TFData2VecVisionForImageClassification",
	    "TFData2VecVisionForSemanticSegmentation",
	    "TFData2VecVisionModel",
	    "TFData2VecVisionPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	_a					:					List[str]=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""simple docstring"""







from __future__ import annotations

import math
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      bool:

	'''simple docstring'''





	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5      ,							int(math.sqrt(UpperCAmelCase_  ) + 1  )      ,							6  ):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True


_a					:					Any=			[num for num in range(3, 100_001, 2) if not is_prime(num)]
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      list[int]:

	'''simple docstring'''





	if not isinstance(UpperCAmelCase_      ,							UpperCAmelCase_  ):
		raise ValueError('n must be an integer'  )
	if n <= 0:
		raise ValueError('n must be >= 0'  )

	__snake_case      :							int						=			[]
	for num in range(len(UpperCAmelCase_  )  ):
		__snake_case      :							List[str]						=			0
		while 2 * i * i <= odd_composites[num]:
			__snake_case      :							List[Any]						=			odd_composites[num] - 2 * i * i
			if is_prime(UpperCAmelCase_  ):
				break
			i += 1
		else:
			list_nums.append(odd_composites[num]  )
			if len(UpperCAmelCase_  ) == n:
				return list_nums

	return []
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	return compute_nums(1  )[0]


if __name__ == "__main__":
	print(f'''{solution() = }''')





