



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						:List[Any]      		=     logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Any     ,		*__SCREAMING_SNAKE_CASE							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           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 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any=None):


           '''simple docstring'''

           __a						       =				{}
           if top_k is not None:
               __a						       =				top_k
           return {}, {}, postprocess_params



       def __call__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     Union[str, List[str], "Image.Image", List["Image.Image"]]     ,		**__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				load_image(__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors=self.framework)
           return model_inputs



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a						       =				self.model(**__SCREAMING_SNAKE_CASE)
           return model_outputs





       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]=5):


           '''simple docstring'''

           if top_k > self.model.config.num_labels:
               __a						       =				self.model.config.num_labels

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

           __a						       =				scores.tolist()
           __a						       =				ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)]









"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

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



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


