




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_							)
class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       str						=     field(default="image-classification"						,     metadata={"include_in_asdict_even_if_is_default": True}							)
				__a     :       ClassVar[Features]						=     Features({"image": Image()}							)
				__a     :       ClassVar[Features]						=     Features({"labels": ClassLabel}							)
				__a     :       str						=     "image"
				__a     :       str						=     "labels"






				def 		snake_case	(			self      ,snake_case__ ):
							if self.label_column not in features:
										raise ValueError(F'Column {self.label_column} is not present in features.' )
							if not isinstance(features[self.label_column]      ,snake_case__ ):
										raise ValueError(F'Column {self.label_column} is not a ClassLabel.' )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      copy.deepcopy(self )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.label_schema.copy()
							SCREAMING_SNAKE_CASE_   : int					  =      features[self.label_column]
							SCREAMING_SNAKE_CASE_   : Tuple					  =      label_schema
							return task_template






				@property
				def 		snake_case	(			self ):
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}









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__ :   List[str]						=							logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase_							)
class 				lowerCAmelCase_       (					lowerCamelCase_							):






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






				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {}, {}
							if padding is not None:
										SCREAMING_SNAKE_CASE_   : Any					  =      padding
							if truncation is not None:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      truncation
							if top_k is not None:
										SCREAMING_SNAKE_CASE_   : int					  =      top_k
							return preprocess_params, {}, postprocess_params






				def __call__(			self      ,snake_case__      ,snake_case__ = None      ,**snake_case__ ):
							if isinstance(snake_case__      ,(Image.Image, str) ) and isinstance(snake_case__      ,snake_case__ ):
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'image': image, 'question': question}
							else:
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      super().__call__(snake_case__      ,**snake_case__ )
							return results






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=False      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      load_image(inputs['image'] )
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.tokenizer(
							    inputs['question']      ,return_tensors=self.framework      ,padding=snake_case__      ,truncation=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      self.image_processor(images=snake_case__      ,return_tensors=self.framework )
							model_inputs.update(snake_case__ )
							return model_inputs






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.model(**snake_case__ )
							return model_outputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=5 ):
							if top_k > self.model.config.num_labels:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.model.config.num_labels

							if self.framework == "pt":
										SCREAMING_SNAKE_CASE_   : Any					  =      model_outputs.logits.sigmoid()[0]
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      probs.topk(snake_case__ )
							else:
										raise ValueError(F'Unsupported framework: {self.framework}' )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      scores.tolist()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__      ,snake_case__ )]



