
'''simple docstring'''







import math

import tensorflow as tf
from packaging import version





def 					UpperCAmelCase_		(__a      :    Tuple ):






   """simple docstring"""

   _a				:	Optional[int]			=   tf.convert_to_tensor(__a )
   _a				:	Any			=   0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) ,	x.dtype ) ))

   return x * cdf





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ):






   """simple docstring"""

   _a				:	Any			=   tf.convert_to_tensor(__a )
   _a				:	Optional[Any]			=   tf.cast(math.pi ,	x.dtype )
   _a				:	Any			=   tf.cast(0.044715 ,	x.dtype )
   _a				:	str			=   0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__a ,	3 )) ))

   return x * cdf





def 					UpperCAmelCase_		(__a      :    List[str] ):






   """simple docstring"""

   _a				:	List[Any]			=   tf.convert_to_tensor(__a )

   return x * tf.tanh(tf.math.softplus(__a ) )





def 					UpperCAmelCase_		(__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	Any			=   tf.convert_to_tensor(__a )
   _a				:	int			=   tf.cast(0.044715 ,	x.dtype )
   _a				:	str			=   tf.cast(0.7978845608 ,	x.dtype )

   return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   tf.convert_to_tensor(__a )
   _a				:	List[str]			=   tf.cast(1.702 ,	x.dtype )
   return x * tf.math.sigmoid(coeff * x )





def 					UpperCAmelCase_		(__a      :    List[str] ):






   """simple docstring"""

   return tf.clip_by_value(_gelu(__a ) ,	-1_0 ,	1_0 )





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ,	__a      :    Optional[Any]=-1 ):






   """simple docstring"""

   _a,  _a				:	int			=   tf.split(__a ,	2 ,	axis=__a )
   return a * tf.math.sigmoid(__a )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):





  def 					UpperCAmelCase_		(__a      :    List[str] ):






     """simple docstring"""

     return tf.keras.activations.gelu(__a ,	approximate=__a )

  __lowerCAmelCase         =    tf.keras.activations.gelu
  __lowerCAmelCase         =    approximate_gelu_wrap
else:
  __lowerCAmelCase         =    _gelu
  __lowerCAmelCase         =    _gelu_new


__lowerCAmelCase         =    {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}





def 					UpperCAmelCase_		(__a      :    Any ):






   """simple docstring"""

   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )





'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

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







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				UNetaDModel
  __UpperCAmelCase				:				KarrasVeScheduler


  def __init__(		self      :     Union[str, Any]							,_a      :     UNetaDModel							,_a      :     KarrasVeScheduler       ):


     '''simple docstring'''






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


  @torch.no_grad()
  def __call__(		self      :     List[Any]							,_a      :     int = 1							,_a      :     int = 50							,_a      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,_a      :     Optional[str] = "pil"							,_a      :     bool = True							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Any			=   self.unet.config.sample_size
     _a				:	Optional[int]			=   (batch_size, 3, img_size, img_size)

     _a				:	Dict			=   self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     _a				:	Dict			=   randn_tensor(_a							,generator=_a							,device=self.device       ) * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(_a       )

     for t in self.progress_bar(self.scheduler.timesteps       ):
        # here sigma_t == t_i from the paper
        _a				:	Optional[int]			=   self.scheduler.schedule[t]
        _a				:	List[str]			=   self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        _a,  _a				:	List[Any]			=   self.scheduler.add_noise_to_input(_a							,_a							,generator=_a       )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        _a				:	Optional[int]			=   (sigma_hat / 2) * model((sample_hat + 1) / 2							,sigma_hat / 2       ).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        _a				:	Tuple			=   self.scheduler.step(_a							,_a							,_a							,_a       )

        if sigma_prev != 0:
           # 6. Apply 2nd order correction
           # The model inputs and output are adjusted by following eq. (213) in [1].
           _a				:	Optional[int]			=   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,sigma_prev / 2       ).sample
           _a				:	Optional[Any]			=   self.scheduler.step_correct(
               _a							,_a							,_a							,_a							,step_output.prev_sample							,step_output['derivative']							,)
        _a				:	Dict			=   step_output.prev_sample

     _a				:	Tuple			=   (sample / 2 + 0.5).clamp(0							,1       )
     _a				:	Optional[Any]			=   sample.cpu().permute(0							,2							,3							,1       ).numpy()
     if output_type == "pil":
        _a				:	List[str]			=   self.numpy_to_pil(_a       )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_a       )



