




'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   ScoreSdeVeScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE = 1     ,	__SCREAMING_SNAKE_CASE = 2000     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	self.unet.config.sample_size
            __snake_case            =	(batch_size, 3, img_size, img_size)

            __snake_case            =	self.unet

            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE	) * self.scheduler.init_noise_sigma
            __snake_case            =	sample.to(self.device	)

            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
            self.scheduler.set_sigmas(__SCREAMING_SNAKE_CASE	)

            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
                 __snake_case            =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,	device=self.device	)

                 # correction step
                 for _ in range(self.scheduler.config.correct_steps	):
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample
                      __snake_case            =	self.scheduler.step_correct(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE	).prev_sample

                 # prediction step
                 __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample
                 __snake_case            =	self.scheduler.step_pred(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE	)

                 __snake_case       ,     __snake_case            =	output.prev_sample, output.prev_sample_mean

            __snake_case            =	sample_mean.clamp(0     ,	1	)
            __snake_case            =	sample.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (sample,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)










import os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case__				:	List[Any]					       =					'\nimport os\n'

snake_case__				:	List[str]					       =					'\ndef foo():\n    import os\n    return False\n'

snake_case__				:	List[Any]					       =					'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case__				:	List[str]					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case__				:	Tuple					       =					'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case__				:	str					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case__				:	Any					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Union[str, Any]					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case__				:	Any					       =					[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""						,						A__					)
def    __lowerCamelCase   (      A__						: Dict						,						A__						: int					)     ->    List[Any]:
   lowerCamelCase_						:   List[str]		= os.path.join(A__						,						"""test_file.py"""					)
   with open(A__						,						"""w"""					) as _tmp_file:
      _tmp_file.write(A__					)

   lowerCamelCase_						:   str		= get_imports(A__					)
   assert parsed_imports == ["os"]





