from typing import TYPE_CHECKING

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


__lowercase							 =					{
    '''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:
    __lowercase							 =					[
        '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''Data2VecAudioForAudioFrameClassification''',
        '''Data2VecAudioForCTC''',
        '''Data2VecAudioForSequenceClassification''',
        '''Data2VecAudioForXVector''',
        '''Data2VecAudioModel''',
        '''Data2VecAudioPreTrainedModel''',
    ]
    __lowercase							 =					[
        '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''Data2VecTextForCausalLM''',
        '''Data2VecTextForMaskedLM''',
        '''Data2VecTextForMultipleChoice''',
        '''Data2VecTextForQuestionAnswering''',
        '''Data2VecTextForSequenceClassification''',
        '''Data2VecTextForTokenClassification''',
        '''Data2VecTextModel''',
        '''Data2VecTextPreTrainedModel''',
    ]
    __lowercase							 =					[
        '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''Data2VecVisionForImageClassification''',
        '''Data2VecVisionForMaskedImageModeling''',
        '''Data2VecVisionForSemanticSegmentation''',
        '''Data2VecVisionModel''',
        '''Data2VecVisionPreTrainedModel''',
    ]

if is_tf_available():
    __lowercase							 =					[
        '''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

    __lowercase							 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''







from math import isqrt, loga
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Any													=       [True] * max_number
						for i in range(2      ,      isqrt(max_number - 1							) + 1							):
												if is_prime[i]:
																		for j in range(i**2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																								lowercase_	:     Optional[Any]													=       False

						return [i for i in range(2      ,      __SCREAMING_SNAKE_CASE							) if is_prime[i]]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 800800      ,      __SCREAMING_SNAKE_CASE					: int = 800800							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       degree * loga(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Any													=       int(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[Any]													=       calculate_prime_numbers(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Optional[Any]													=       0
						lowercase_	:     List[Any]													=       0
						lowercase_	:     Union[str, Any]													=       len(__SCREAMING_SNAKE_CASE							) - 1
						while left < right:
												while (
												    prime_numbers[right] * loga(prime_numbers[left]							)
												    + prime_numbers[left] * loga(prime_numbers[right]							)
												    > upper_bound
												):
																		right -= 1
												hybrid_integers_count += right - left
												left += 1

						return hybrid_integers_count


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



