

from typing import TYPE_CHECKING

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


A_						:     Any			  =				{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       A_						:     List[str]			  =				[
           'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DonutSwinModel',
           'DonutSwinPreTrainedModel',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       A_						:     str			  =				['DonutFeatureExtractor']
       A_						:     List[str]			  =				['DonutImageProcessor']


if TYPE_CHECKING:
       from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
       from .processing_donut import DonutProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_donut_swin import (
                  DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DonutSwinModel,
                  DonutSwinPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_donut import DonutFeatureExtractor
              from .image_processing_donut import DonutImageProcessor

else:
       import sys

       A_						:     Dict			  =				_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




