from typing import TYPE_CHECKING

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


lowerCAmelCase__	:  Union[str, Any]						=  {
    'agents': ['Agent', 'AzureOpenAiAgent', 'HfAgent', 'LocalAgent', 'OpenAiAgent'],
    'base': ['PipelineTool', 'RemoteTool', 'Tool', 'launch_gradio_demo', 'load_tool'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  List[str]						=  ['DocumentQuestionAnsweringTool']
				lowerCAmelCase__	:  str						=  ['ImageCaptioningTool']
				lowerCAmelCase__	:  Optional[Any]						=  ['ImageQuestionAnsweringTool']
				lowerCAmelCase__	:  List[str]						=  ['ImageSegmentationTool']
				lowerCAmelCase__	:  str						=  ['SpeechToTextTool']
				lowerCAmelCase__	:  Tuple						=  ['TextClassificationTool']
				lowerCAmelCase__	:  Any						=  ['TextQuestionAnsweringTool']
				lowerCAmelCase__	:  Optional[Any]						=  ['TextSummarizationTool']
				lowerCAmelCase__	:  Union[str, Any]						=  ['TextToSpeechTool']
				lowerCAmelCase__	:  str						=  ['TranslationTool']

if TYPE_CHECKING:
				from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
				from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .document_question_answering import DocumentQuestionAnsweringTool
								from .image_captioning import ImageCaptioningTool
								from .image_question_answering import ImageQuestionAnsweringTool
								from .image_segmentation import ImageSegmentationTool
								from .speech_to_text import SpeechToTextTool
								from .text_classification import TextClassificationTool
								from .text_question_answering import TextQuestionAnsweringTool
								from .text_summarization import TextSummarizationTool
								from .text_to_speech import TextToSpeechTool
								from .translation import TranslationTool
else:
				import sys

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









"""simple docstring"""
from typing import TYPE_CHECKING

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


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'UniSpeechSatForAudioFrameClassification',
		    'UniSpeechSatForCTC',
		    'UniSpeechSatForPreTraining',
		    'UniSpeechSatForSequenceClassification',
		    'UniSpeechSatForXVector',
		    'UniSpeechSatModel',
		    'UniSpeechSatPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_snake_case	:						Union[str, Any]					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


