
'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase    :							str      =					{"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Any      =					[
					    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "UniSpeechForCTC",
					    "UniSpeechForPreTraining",
					    "UniSpeechForSequenceClassification",
					    "UniSpeechModel",
					    "UniSpeechPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_unispeech import (
										    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
										    UniSpeechForCTC,
										    UniSpeechForPreTraining,
										    UniSpeechForSequenceClassification,
										    UniSpeechModel,
										    UniSpeechPreTrainedModel,
										)

else:
					import sys

					lowercase    :							Optional[int]      =					_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

