


"""simple docstring"""


a_       							=    frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

a_       							=    frozenset(["prompt", "negative_prompt"])

a_       							=    frozenset([])

a_       							=    frozenset(["image"])

a_       							=    frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

a_       							=    frozenset(["image"])

a_       							=    frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

a_       							=    frozenset(["prompt", "image", "negative_prompt"])

a_       							=    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_       							=    frozenset(["prompt", "image", "mask_image", "negative_prompt"])

a_       							=    frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

a_       							=    frozenset(["image", "mask_image"])

a_       							=    frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

a_       							=    frozenset(["example_image", "image", "mask_image"])

a_       							=    frozenset(["class_labels"])

a_       							=    frozenset(["class_labels"])

a_       							=    frozenset(["batch_size"])

a_       							=    frozenset([])

a_       							=    frozenset(["batch_size"])

a_       							=    frozenset([])

a_       							=    frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

a_       							=    frozenset(["prompt", "negative_prompt"])
a_       							=    frozenset(["input_tokens"])

a_       							=    frozenset(["input_tokens"])



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)