






A__			:    Optional[int]         =				frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

A__			:    str         =				frozenset(['prompt', 'negative_prompt'])

A__			:    Tuple         =				frozenset([])

A__			:    Optional[Any]         =				frozenset(['image'])

A__			:    Union[str, Any]         =				frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

A__			:    Tuple         =				frozenset(['image'])

A__			:    Any         =				frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

A__			:    Union[str, Any]         =				frozenset(['prompt', 'image', 'negative_prompt'])

A__			:    Optional[Any]         =				frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

A__			:    List[str]         =				frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

A__			:    Dict         =				frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

A__			:    Tuple         =				frozenset(['image', 'mask_image'])

A__			:    Optional[int]         =				frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

A__			:    List[str]         =				frozenset(['example_image', 'image', 'mask_image'])

A__			:    str         =				frozenset(['class_labels'])

A__			:    List[str]         =				frozenset(['class_labels'])

A__			:    Dict         =				frozenset(['batch_size'])

A__			:    int         =				frozenset([])

A__			:    Dict         =				frozenset(['batch_size'])

A__			:    Optional[Any]         =				frozenset([])

A__			:    str         =				frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

A__			:    Union[str, Any]         =				frozenset(['prompt', 'negative_prompt'])
A__			:    Union[str, Any]         =				frozenset(['input_tokens'])

A__			:    int         =				frozenset(['input_tokens'])












from typing import TYPE_CHECKING

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


A__			:    Any         =				{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Union[str, 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

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




