



"""simple docstring"""







from typing import TYPE_CHECKING

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


UpperCAmelCase:  str			     =				{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase:  Union[str, Any]			     =				[
		    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """YosoForMaskedLM""",
		    """YosoForMultipleChoice""",
		    """YosoForQuestionAnswering""",
		    """YosoForSequenceClassification""",
		    """YosoForTokenClassification""",
		    """YosoLayer""",
		    """YosoModel""",
		    """YosoPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yoso import (
				    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YosoForMaskedLM,
				    YosoForMultipleChoice,
				    YosoForQuestionAnswering,
				    YosoForSequenceClassification,
				    YosoForTokenClassification,
				    YosoLayer,
				    YosoModel,
				    YosoPreTrainedModel,
				)


else:
		import sys

		UpperCAmelCase:  Dict			     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)




'''simple docstring'''




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

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




