from typing import TYPE_CHECKING

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


UpperCamelCase      			=     {
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase      			=     [
					    'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MobileViTV2ForImageClassification',
					    'MobileViTV2ForSemanticSegmentation',
					    'MobileViTV2Model',
					    'MobileViTV2PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileViTVaConfig,
					    MobileViTVaOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevitva import (
										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTVaForImageClassification,
										    MobileViTVaForSemanticSegmentation,
										    MobileViTVaModel,
										    MobileViTVaPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase      			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





