

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    			=						{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase    			=						[
          '''UMT5EncoderModel''',
          '''UMT5ForConditionalGeneration''',
          '''UMT5ForQuestionAnswering''',
          '''UMT5Model''',
          '''UMT5PreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_umta import UMTaConfig, UMTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_umta import (
                UMTaEncoderModel,
                UMTaForConditionalGeneration,
                UMTaForQuestionAnswering,
                UMTaModel,
                UMTaPreTrainedModel,
            )
else:
      import sys

      _lowerCAmelCase    			=						_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 ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available

@dataclass
class 					__A   (  a			):
				__A            = 42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
							from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
							from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline





