

import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax





@require_flax
class A__     :



 """simple docstring"""







 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       Optional[Any]	   =     self.prepare_init_args_and_inputs_for_common()

      _lowerCAmelCase :       str	   =     self.model_class(**A_							)
      _lowerCAmelCase :       str	   =     model.init(inputs_dict["prng_key"]  ,    inputs_dict["sample"]							)
      jax.lax.stop_gradient(A_							)

      _lowerCAmelCase :       Optional[Any]	   =     model.apply(A_  ,    inputs_dict["sample"]							)

      if isinstance(A_  ,    A_							):
           _lowerCAmelCase :       Tuple	   =     output.sample

      self.assertIsNotNone(A_							)
      _lowerCAmelCase :       List[Any]	   =     inputs_dict["sample"].shape
      self.assertEqual(output.shape  ,    A_  ,    "Input and output shapes do not match"							)

 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       str	   =     self.prepare_init_args_and_inputs_for_common()

      _lowerCAmelCase :       Tuple	   =     1_6
      _lowerCAmelCase :       List[Any]	   =     (1_6, 3_2)

      _lowerCAmelCase :       str	   =     self.model_class(**A_							)
      _lowerCAmelCase :       List[str]	   =     model.init(inputs_dict["prng_key"]  ,    inputs_dict["sample"]							)
      jax.lax.stop_gradient(A_							)

      _lowerCAmelCase :       Union[str, Any]	   =     model.apply(A_  ,    inputs_dict["sample"]							)

      if isinstance(A_  ,    A_							):
           _lowerCAmelCase :       str	   =     output.sample

      self.assertIsNotNone(A_							)
      _lowerCAmelCase :       int	   =     inputs_dict["sample"].shape
      self.assertEqual(output.shape  ,    A_  ,    "Input and output shapes do not match"							)


 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     "kwargs" in inspect.signature(self.model_class.__init__							).parameters
      _lowerCAmelCase :       Any	   =     len(self.model_class._deprecated_kwargs							) > 0

      if has_kwarg_in_model_class and not has_deprecated_kwarg:
           raise ValueError(
               F'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
               " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
               " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
               " [<deprecated_argument>]`"							)

      if not has_kwarg_in_model_class and has_deprecated_kwarg:
           raise ValueError(
               F'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
               " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
               F''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
               " from `_deprecated_kwargs = [<deprecated_argument>]`"							)









import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






