'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:     Optional[Any]  ={
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:     List[Any]  =[
        """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """M2M100ForConditionalGeneration""",
        """M2M100Model""",
        """M2M100PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
    from .tokenization_mam_aaa import MaMaaaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mam_aaa import (
            M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaMaaaForConditionalGeneration,
            MaMaaaModel,
            MaMaaaPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:     List[str]  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





