

"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase													=logging.get_logger(__name__)





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   ['''pixel_values''']




    def __init__( self ,lowerCamelCase_ = True ,lowerCamelCase_ = None ,lowerCamelCase_ = PILImageResampling.BICUBIC ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,lowerCamelCase_ = 1 / 2_5_5 ,lowerCamelCase_ = None ,lowerCamelCase_ = True ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      None:
          super().__init__(**lowerCamelCase_    )
          A				  =		size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
          A				  =		get_size_dict(lowerCamelCase_    )
          A				  =		crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
          A				  =		get_size_dict(lowerCamelCase_ ,default_to_square=lowerCamelCase_ ,param_name="""crop_size"""    )

          A				  =		do_resize
          A				  =		do_rescale
          A				  =		do_normalize
          A				  =		do_center_crop
          A				  =		crop_size
          A				  =		size
          A				  =		resample
          A				  =		rescale_factor
          A				  =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          A				  =		image_std if image_std is not None else IMAGENET_DEFAULT_STD




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = PILImageResampling.BILINEAR ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      np.ndarray:
          A				  =		get_size_dict(lowerCamelCase_    )

          if "shortest_edge" in size:
                A				  =		get_resize_output_image_size(lowerCamelCase_ ,size=size["""shortest_edge"""] ,default_to_square=lowerCamelCase_    )
                # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
          elif "height" in size and "width" in size:
                A				  =		(size["""height"""], size["""width"""])
          else:
                raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'    )
          return resize(lowerCamelCase_ ,size=lowerCamelCase_ ,resample=lowerCamelCase_ ,data_format=lowerCamelCase_ ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      np.ndarray:
          A				  =		get_size_dict(lowerCamelCase_    )
          if "height" not in size or "width" not in size:
                raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'    )
          return center_crop(lowerCamelCase_ ,size=(size["""height"""], size["""width"""]) ,data_format=lowerCamelCase_ ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_    )    ->      np.ndarray:
          return rescale(lowerCamelCase_ ,scale=lowerCamelCase_ ,data_format=lowerCamelCase_ ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      np.ndarray:
          return normalize(lowerCamelCase_ ,mean=lowerCamelCase_ ,std=lowerCamelCase_ ,data_format=lowerCamelCase_ ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = ChannelDimension.FIRST ,**lowerCamelCase_ ,)    ->      BatchFeature:
          A				  =		do_resize if do_resize is not None else self.do_resize
          A				  =		do_rescale if do_rescale is not None else self.do_rescale
          A				  =		do_normalize if do_normalize is not None else self.do_normalize
          A				  =		do_center_crop if do_center_crop is not None else self.do_center_crop
          A				  =		crop_size if crop_size is not None else self.crop_size
          A				  =		get_size_dict(lowerCamelCase_ ,param_name="""crop_size""" ,default_to_square=lowerCamelCase_    )
          A				  =		resample if resample is not None else self.resample
          A				  =		rescale_factor if rescale_factor is not None else self.rescale_factor
          A				  =		image_mean if image_mean is not None else self.image_mean
          A				  =		image_std if image_std is not None else self.image_std

          A				  =		size if size is not None else self.size
          A				  =		get_size_dict(lowerCamelCase_    )

          if not is_batched(lowerCamelCase_    ):
                A				  =		[images]

          if not valid_images(lowerCamelCase_    ):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

          if do_resize and size is None:
                raise ValueError("""Size must be specified if do_resize is True."""    )

          if do_center_crop and crop_size is None:
                raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

          if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

          # All transformations expect numpy arrays.
          A				  =		[to_numpy_array(lowerCamelCase_    ) for image in images]

          if do_resize:
                A				  =		[self.resize(image=lowerCamelCase_ ,size=lowerCamelCase_ ,resample=lowerCamelCase_    ) for image in images]

          if do_center_crop:
                A				  =		[self.center_crop(image=lowerCamelCase_ ,size=lowerCamelCase_    ) for image in images]

          if do_rescale:
                A				  =		[self.rescale(image=lowerCamelCase_ ,scale=lowerCamelCase_    ) for image in images]

          if do_normalize:
                A				  =		[self.normalize(image=lowerCamelCase_ ,mean=lowerCamelCase_ ,std=lowerCamelCase_    ) for image in images]

          A				  =		[to_channel_dimension_format(lowerCamelCase_ ,lowerCamelCase_    ) for image in images]

          A				  =		{"""pixel_values""": images}
          return BatchFeature(data=lowerCamelCase_ ,tensor_type=lowerCamelCase_    )







"""simple docstring"""


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													=logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      str:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )
          self.check_model_type(lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_    )    ->      List[Any]:
          A  ,			A				  =		{}, {}
          if padding is not None:
                A				  =		padding
          if truncation is not None:
                A				  =		truncation
          if top_k is not None:
                A				  =		top_k
          return preprocess_params, {}, postprocess_params




    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_    )    ->      Dict:
          if isinstance(lowerCamelCase_ ,(Image.Image, str)    ) and isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                A				  =		{"""image""": image, """question""": question}
          else:
                A				  =		image
          A				  =		super().__call__(lowerCamelCase_ ,**lowerCamelCase_    )
          return results




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False ,lowerCamelCase_=False    )    ->      List[Any]:
          A				  =		load_image(inputs["""image"""]    )
          A				  =		self.tokenizer(
              inputs["""question"""] ,return_tensors=self.framework ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_    )
          A				  =		self.image_processor(images=lowerCamelCase_ ,return_tensors=self.framework    )
          model_inputs.update(lowerCamelCase_    )
          return model_inputs




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Union[str, Any]:
          A				  =		self.model(**lowerCamelCase_    )
          return model_outputs




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=5    )    ->      int:
          if top_k > self.model.config.num_labels:
                A				  =		self.model.config.num_labels

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

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




