





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_     :						Any						=					{
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InstructBlipQFormerModel',
       'InstructBlipPreTrainedModel',
       'InstructBlipForConditionalGeneration',
       'InstructBlipVisionModel',
   ]

if TYPE_CHECKING:
   from .configuration_instructblip import (
       INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       InstructBlipConfig,
       InstructBlipQFormerConfig,
       InstructBlipVisionConfig,
   )
   from .processing_instructblip import InstructBlipProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_instructblip import (
          INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          InstructBlipForConditionalGeneration,
          InstructBlipPreTrainedModel,
          InstructBlipQFormerModel,
          InstructBlipVisionModel,
      )

else:
   import sys

   lowerCamelCase_     :						Any						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result