





from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

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

UpperCAmelCase__						:			Any									=logging.get_logger(__name__)

@add_end_docstrings(a    )
class       __A     (						a    ):


				def __init__(		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								super().__init__(*UpperCAmelCase_   , **UpperCAmelCase_							)
								self.check_model_type(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase ,  lowerCamelCase	 ={}, {}
								if padding is not None:
												lowerCamelCase	 =padding
								if truncation is not None:
												lowerCamelCase	 =truncation
								if top_k is not None:
												lowerCamelCase	 =top_k
								return preprocess_params, {}, postprocess_params


				def __call__(		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , **UpperCAmelCase_							):
								if isinstance(UpperCAmelCase_   , (Image.Image, str)							) and isinstance(UpperCAmelCase_   , UpperCAmelCase_							):
												lowerCamelCase	 ={"""image""": image, """question""": question}
								else:
												lowerCamelCase	 =image
								lowerCamelCase	 =super().__call__(UpperCAmelCase_   , **UpperCAmelCase_							)
								return results


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=False   , UpperCAmelCase_=False							):
								lowerCamelCase	 =load_image(inputs["""image"""]							)
								lowerCamelCase	 =self.tokenizer(
								    inputs["""question"""]   , return_tensors=self.framework   , padding=UpperCAmelCase_   , truncation=UpperCAmelCase_							)
								lowerCamelCase	 =self.image_processor(images=UpperCAmelCase_   , return_tensors=self.framework							)
								model_inputs.update(UpperCAmelCase_							)
								return model_inputs


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =self.model(**UpperCAmelCase_							)
								return model_outputs


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=5							):
								if top_k > self.model.config.num_labels:
												lowerCamelCase	 =self.model.config.num_labels

								if self.framework == "pt":
												lowerCamelCase	 =model_outputs.logits.sigmoid()[0]
												lowerCamelCase ,  lowerCamelCase	 =probs.topk(UpperCAmelCase_							)
								else:
												raise ValueError(f"""Unsupported framework: {self.framework}"""							)

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












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
							from .pipeline_kandinsky import KandinskyPipeline
							from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
							from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
							from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
							from .text_encoder import MultilingualCLIP





