

__snake_case			:     Union[str, Any]										=      frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__snake_case			:     int										=      frozenset(['prompt', 'negative_prompt'])

__snake_case			:     Optional[int]										=      frozenset([])

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

__snake_case			:     int										=      frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__snake_case			:     Union[str, Any]										=      frozenset(['image'])

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

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

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

__snake_case			:     Tuple										=      frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

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

__snake_case			:     Optional[int]										=      frozenset(['image', 'mask_image'])

__snake_case			:     List[Any]										=      frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__snake_case			:     Optional[int]										=      frozenset(['example_image', 'image', 'mask_image'])

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

__snake_case			:     int										=      frozenset(['class_labels'])

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

__snake_case			:     int										=      frozenset([])

__snake_case			:     str										=      frozenset(['batch_size'])

__snake_case			:     Any										=      frozenset([])

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

__snake_case			:     List[str]										=      frozenset(['prompt', 'negative_prompt'])
__snake_case			:     Optional[int]										=      frozenset(['input_tokens'])

__snake_case			:     Optional[Any]										=      frozenset(['input_tokens'])




'''simple docstring'''



from typing import TYPE_CHECKING

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


__snake_case			:     Any										=      {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     Optional[int]										=      [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     List[Any]										=      ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

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

