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

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

__SCREAMING_SNAKE_CASE						:   str         =			frozenset([])

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

__SCREAMING_SNAKE_CASE						:   str         =			frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

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

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

__SCREAMING_SNAKE_CASE						:   Any         =			frozenset(['prompt', 'image', 'negative_prompt'])

__SCREAMING_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',
    ]
)

__SCREAMING_SNAKE_CASE						:   int         =			frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

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

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

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

__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			frozenset(['example_image', 'image', 'mask_image'])

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

__SCREAMING_SNAKE_CASE						:   List[Any]         =			frozenset(['class_labels'])

__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			frozenset(['batch_size'])

__SCREAMING_SNAKE_CASE						:   Tuple         =			frozenset([])

__SCREAMING_SNAKE_CASE						:   List[Any]         =			frozenset(['batch_size'])

__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			frozenset([])

__SCREAMING_SNAKE_CASE						:   List[Any]         =			frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

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

__SCREAMING_SNAKE_CASE						:   int         =			frozenset(['input_tokens'])
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

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