
'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowercase				:						Dict												= {
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						Tuple												= [
							    "DPRContextEncoderTokenizerFast",
							    "DPRQuestionEncoderTokenizerFast",
							    "DPRReaderTokenizerFast",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						List[Any]												= [
							    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DPRContextEncoder",
							    "DPRPretrainedContextEncoder",
							    "DPRPreTrainedModel",
							    "DPRPretrainedQuestionEncoder",
							    "DPRPretrainedReader",
							    "DPRQuestionEncoder",
							    "DPRReader",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						List[Any]												= [
							    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDPRContextEncoder",
							    "TFDPRPretrainedContextEncoder",
							    "TFDPRPretrainedQuestionEncoder",
							    "TFDPRPretrainedReader",
							    "TFDPRQuestionEncoder",
							    "TFDPRReader",
							]


if TYPE_CHECKING:
							from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
							from .tokenization_dpr import (
							    DPRContextEncoderTokenizer,
							    DPRQuestionEncoderTokenizer,
							    DPRReaderOutput,
							    DPRReaderTokenizer,
							)

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_dpr_fast import (
														    DPRContextEncoderTokenizerFast,
														    DPRQuestionEncoderTokenizerFast,
														    DPRReaderTokenizerFast,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dpr import (
														    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DPRContextEncoder,
														    DPRPretrainedContextEncoder,
														    DPRPreTrainedModel,
														    DPRPretrainedQuestionEncoder,
														    DPRPretrainedReader,
														    DPRQuestionEncoder,
														    DPRReader,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_dpr import (
														    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDPRContextEncoder,
														    TFDPRPretrainedContextEncoder,
														    TFDPRPretrainedQuestionEncoder,
														    TFDPRPretrainedReader,
														    TFDPRQuestionEncoder,
														    TFDPRReader,
														)

else:
							import sys

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





'''simple docstring'''







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 1000							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= 0
						for i in range(__SCREAMING_SNAKE_CASE							):
												if i % 3 == 0 or i % 5 == 0:
																		result += i
						return result


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



