


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 		_snake_case  (				_snake_case							):
   SCREAMING_SNAKE_CASE__				=	42
   SCREAMING_SNAKE_CASE__				=	42




   def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
         super().__init__()
         self.register_modules(unet=_lowerCamelCase ,  scheduler=_lowerCamelCase   )




   @torch.no_grad()
   def __call__(		self ,  _lowerCamelCase = 1 ,  _lowerCamelCase = 50 ,  _lowerCamelCase = None ,  _lowerCamelCase = "pil" ,  _lowerCamelCase = True ,  **_lowerCamelCase ,  ):
         a						:Optional[Any]										=	self.unet.config.sample_size
         a						:List[str]										=	(batch_size, 3, img_size, img_size)

         a						:int										=	self.unet

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

         self.scheduler.set_timesteps(_lowerCamelCase   )

         for t in self.progress_bar(self.scheduler.timesteps   ):
               # here sigma_t == t_i from the paper
               a						:Dict										=	self.scheduler.schedule[t]
               a						:List[Any]										=	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						:Tuple										=	self.scheduler.add_noise_to_input(_lowerCamelCase ,  _lowerCamelCase ,  generator=_lowerCamelCase   )

               # 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						:Tuple										=	(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						:str										=	self.scheduler.step(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )

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

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

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase   )









from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

snake_case    :   str	    =					logging.get_logger(__name__)







@add_end_docstrings(_snake_case							)
class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       super().__init__(*_lowerCamelCase ,  **_lowerCamelCase   )
       requires_backends(self ,  '''vision'''   )
       self.check_model_type(
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase=None   ):
       a						:Tuple										=	{}
       if top_k is not None:
             a						:int										=	top_k
       return {}, {}, postprocess_params




 def __call__(		self ,  _lowerCamelCase ,  **_lowerCamelCase   ):
       return super().__call__(_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	load_image(_lowerCamelCase   )
       a						:Any										=	self.image_processor(images=_lowerCamelCase ,  return_tensors=self.framework   )
       return model_inputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[str]										=	self.model(**_lowerCamelCase   )
       return model_outputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=5   ):
       if top_k > self.model.config.num_labels:
             a						:List[Any]										=	self.model.config.num_labels

       if self.framework == "pt":
             a						:int										=	model_outputs.logits.softmax(-1   )[0]
             a							,    a						:Union[str, Any]										=	probs.topk(_lowerCamelCase   )
       elif self.framework == "tf":
             a						:Optional[Any]										=	stable_softmax(model_outputs.logits ,  axis=-1   )[0]
             a						:Union[str, Any]										=	tf.math.top_k(_lowerCamelCase ,  k=_lowerCamelCase   )
             a							,    a						:Optional[int]										=	topk.values.numpy(), topk.indices.numpy()
       else:
             raise ValueError(F'''Unsupported framework: {self.framework}'''   )

       a						:Optional[int]										=	scores.tolist()
       a						:str										=	ids.tolist()
       return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowerCamelCase ,  _lowerCamelCase   )]





