


'''simple docstring'''
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__  =     {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__  =     ['''ErnieMTokenizer''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__  =     [
         '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ErnieMForMultipleChoice''',
         '''ErnieMForQuestionAnswering''',
         '''ErnieMForSequenceClassification''',
         '''ErnieMForTokenClassification''',
         '''ErnieMModel''',
         '''ErnieMPreTrainedModel''',
         '''ErnieMForInformationExtraction''',
     ]


if TYPE_CHECKING:
     from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ernie_m import ErnieMTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ernie_m import (
              ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
              ErnieMForInformationExtraction,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
              ErnieMModel,
              ErnieMPreTrainedModel,
          )


else:
     import sys

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












'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
