






"""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


UpperCamelCase_						:					Dict      				=			{
    """configuration_ernie_m""": ["""ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieMConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			["""ErnieMTokenizer"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					str      				=			[
        """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

    UpperCamelCase_						:					Union[str, Any]      				=			_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    UpperCAmelCase__	(							_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




