

from typing import TYPE_CHECKING

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


A_						:     Optional[int]			  =				{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:     Optional[int]			  =				[
							    'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTSanJapaneseForConditionalGeneration',
							    'GPTSanJapaneseModel',
							    'GPTSanJapanesePreTrainedModel',
							]
							A_						:     List[str]			  =				[
							    'GPTSanJapaneseTokenizer',
							]


if TYPE_CHECKING:
							from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
							from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptsan_japanese import (
														    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTSanJapaneseForConditionalGeneration,
														    GPTSanJapaneseModel,
														    GPTSanJapanesePreTrainedModel,
														)
														from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
							import sys

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







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




