

"""simple docstring"""




import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__UpperCAmelCase	=			1000







class        __lowercase     :

  def __init__(					self						:     Optional[Any]   ,A						:     Tuple   ,A						:     List[Any]=1.2   ,A						:     Optional[int]="g"   ,A						:     str=0.5   ,A						:     Optional[Any]="-"   ,A						:     Tuple="test_out.jpg"   ,A						:     Optional[int]=True   ,A						:     Dict=False   ,A						:     List[str]=None   ,A						:     List[Any]=None   ,A						:     Union[str, Any]=0.7   ,):






   '''simple docstring'''
   if isinstance(SCREAMING_SNAKE_CASE__   ,torch.Tensor      ):
    UpperCAmelCase__   :					Tuple   				=   img.numpy().astype("""np.uint8"""      )
   if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__      ):
    UpperCAmelCase__   :					Dict   				=   img_tensorize(SCREAMING_SNAKE_CASE__      )
   assert isinstance(SCREAMING_SNAKE_CASE__   ,np.ndarray      )

   UpperCAmelCase__   :					List[Any]   				=   img.shape[1], img.shape[0]
   UpperCAmelCase__   :					Any   				=   mplfigure.Figure(frameon=SCREAMING_SNAKE_CASE__      )
   UpperCAmelCase__   :					Optional[int]   				=   fig.get_dpi()
   UpperCAmelCase__   :					Union[str, Any]   				=   (width * scale + 1e-2) / dpi
   UpperCAmelCase__   :					Optional[int]   				=   (height * scale + 1e-2) / dpi
   fig.set_size_inches(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__      )
   UpperCAmelCase__   :					Any   				=   fig.add_axes([0.0, 0.0, 1.0, 1.0]      )
   ax.axis("""off"""      )
   ax.set_xlim(0.0   ,SCREAMING_SNAKE_CASE__      )
   ax.set_ylim(SCREAMING_SNAKE_CASE__      )

   UpperCAmelCase__   :					str   				=   saveas
   UpperCAmelCase__   :					List[Any]   				=   rgb
   UpperCAmelCase__   :					Optional[Any]   				=   pynb
   UpperCAmelCase__   :					Optional[Any]   				=   img
   UpperCAmelCase__   :					List[Any]   				=   edgecolor
   UpperCAmelCase__   :					List[Any]   				=   0.5
   UpperCAmelCase__   :					int   				=   linestyle
   UpperCAmelCase__   :					List[str]   				=   int(np.sqrt(min(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__      )      ) * scale // 3      )
   UpperCAmelCase__   :					Tuple   				=   width
   UpperCAmelCase__   :					List[str]   				=   height
   UpperCAmelCase__   :					Any   				=   scale
   UpperCAmelCase__   :					str   				=   fig
   UpperCAmelCase__   :					Optional[Any]   				=   ax
   UpperCAmelCase__   :					Optional[int]   				=   pad
   UpperCAmelCase__   :					int   				=   idaobj
   UpperCAmelCase__   :					List[Any]   				=   idaattr
   UpperCAmelCase__   :					Dict   				=   FigureCanvasAgg(SCREAMING_SNAKE_CASE__      )

  def 				__lowercase    (					self						:     List[str]   ,A						:     Optional[int]   ,A						:     Tuple=None      ):






   '''simple docstring'''
   if color is None:
    UpperCAmelCase__   :					Optional[int]   				=   self.edgecolor
   (UpperCAmelCase__)   :					Optional[Any]   				=   box
   UpperCAmelCase__   :					List[Any]   				=   xa - xa
   UpperCAmelCase__   :					int   				=   ya - ya
   self.ax.add_patch(
       mpl.patches.Rectangle(
           (xa, ya)   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,fill=SCREAMING_SNAKE_CASE__   ,edgecolor=SCREAMING_SNAKE_CASE__   ,linewidth=self.font_size // 3   ,alpha=self.alpha   ,linestyle=self.linestyle   ,)      )

  def 				__lowercase    (					self						:     Optional[Any]   ,A						:     int   ,A						:     List[str]=None   ,A						:     Tuple=None   ,A						:     str=None   ,A						:     Union[str, Any]=None      ):






   '''simple docstring'''
   if len(boxes.shape      ) > 2:
    UpperCAmelCase__   :					Union[str, Any]   				=   boxes[0]
   if len(obj_ids.shape      ) > 1:
    UpperCAmelCase__   :					str   				=   obj_ids[0]
   if len(obj_scores.shape      ) > 1:
    UpperCAmelCase__   :					int   				=   obj_scores[0]
   if len(attr_ids.shape      ) > 1:
    UpperCAmelCase__   :					int   				=   attr_ids[0]
   if len(attr_scores.shape      ) > 1:
    UpperCAmelCase__   :					int   				=   attr_scores[0]
   if isinstance(SCREAMING_SNAKE_CASE__   ,torch.Tensor      ):
    UpperCAmelCase__   :					Optional[Any]   				=   boxes.numpy()
   if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__      ):
    UpperCAmelCase__   :					str   				=   np.array(SCREAMING_SNAKE_CASE__      )
   assert isinstance(SCREAMING_SNAKE_CASE__   ,np.ndarray      )
   UpperCAmelCase__   :					int   				=   np.prod(boxes[:, 2:] - boxes[:, :2]   ,axis=1      )
   UpperCAmelCase__   :					List[str]   				=   np.argsort(-areas      ).tolist()
   UpperCAmelCase__   :					Any   				=   boxes[sorted_idxs] if boxes is not None else None
   UpperCAmelCase__   :					Dict   				=   obj_ids[sorted_idxs] if obj_ids is not None else None
   UpperCAmelCase__   :					Optional[int]   				=   obj_scores[sorted_idxs] if obj_scores is not None else None
   UpperCAmelCase__   :					Dict   				=   attr_ids[sorted_idxs] if attr_ids is not None else None
   UpperCAmelCase__   :					int   				=   attr_scores[sorted_idxs] if attr_scores is not None else None

   UpperCAmelCase__   :					List[Any]   				=   [self._random_color(maximum=1      ) for _ in range(len(SCREAMING_SNAKE_CASE__      )      )]
   UpperCAmelCase__   :					Optional[int]   				=   [assigned_colors[idx] for idx in sorted_idxs]
   if obj_ids is not None:
    UpperCAmelCase__   :					str   				=   self._create_text_labels_attr(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__      )
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
     UpperCAmelCase__   :					int   				=   assigned_colors[i]
     self.add_box(boxes[i]   ,SCREAMING_SNAKE_CASE__      )
     self.draw_labels(labels[i]   ,boxes[i]   ,SCREAMING_SNAKE_CASE__      )

  def 				__lowercase    (					self						:     Any   ,A						:     Tuple   ,A						:     Any   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   box
   UpperCAmelCase__   :					str   				=   (xa, ya)
   UpperCAmelCase__   :					Optional[Any]   				=   (ya - ya) * (xa - xa)
   UpperCAmelCase__   :					Optional[Any]   				=   _SMALL_OBJ * self.scale
   if instance_area < small or ya - ya < 40 * self.scale:
    if ya >= self.height - 5:
     UpperCAmelCase__   :					Optional[Any]   				=   (xa, ya)
    else:
     UpperCAmelCase__   :					Dict   				=   (xa, ya)

   UpperCAmelCase__   :					Optional[int]   				=   (ya - ya) / np.sqrt(self.height * self.width      )
   UpperCAmelCase__   :					Optional[Any]   				=   self._change_color_brightness(SCREAMING_SNAKE_CASE__   ,brightness_factor=0.7      )
   UpperCAmelCase__   :					Optional[int]   				=   np.clip((height_ratio - 0.0_2) / 0.0_8 + 1   ,1.2   ,2      )
   font_size *= 0.7_5 * self.font_size

   self.draw_text(
       text=SCREAMING_SNAKE_CASE__   ,position=SCREAMING_SNAKE_CASE__   ,color=SCREAMING_SNAKE_CASE__   ,)

  def 				__lowercase    (					self						:     List[str]   ,A						:     Any   ,A						:     List[Any]   ,A						:     Optional[Any]="g"   ,A						:     Tuple="left"   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   0
   UpperCAmelCase__   :					List[str]   				=   self.font_size
   UpperCAmelCase__   :					Dict   				=   np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE__      )      )   ,0.2      )
   UpperCAmelCase__   :					Optional[Any]   				=   max(0.8   ,np.max(SCREAMING_SNAKE_CASE__      )      )
   UpperCAmelCase__   :					Union[str, Any]   				=   {
       "facecolor": "black",
       "alpha": self.alpha,
       "pad": self.pad,
       "edgecolor": "none",
   }
   UpperCAmelCase__   :					Any   				=   position
   self.ax.text(
       SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,size=font_size * self.scale   ,family="""sans-serif"""   ,bbox=SCREAMING_SNAKE_CASE__   ,verticalalignment="""top"""   ,horizontalalignment=SCREAMING_SNAKE_CASE__   ,color=SCREAMING_SNAKE_CASE__   ,zorder=10   ,rotation=SCREAMING_SNAKE_CASE__   ,)

  def 				__lowercase    (					self						:     List[Any]   ,A						:     str=None      ):






   '''simple docstring'''
   if saveas is None:
    UpperCAmelCase__   :					Optional[Any]   				=   self.saveas
   if saveas.lower().endswith(""".jpg"""      ) or saveas.lower().endswith(""".png"""      ):
    cva.imwrite(
        SCREAMING_SNAKE_CASE__   ,self._get_buffer()[:, :, ::-1]   ,)
   else:
    self.fig.savefig(SCREAMING_SNAKE_CASE__      )

  def 				__lowercase    (					self						:     Optional[int]   ,A						:     str   ,A						:     Dict   ,A						:     Optional[int]   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   [self.idaobj[i] for i in classes]
   UpperCAmelCase__   :					Dict   				=   [self.idaattr[i] for i in attr_classes]
   UpperCAmelCase__   :					int   				=   [
       f"{label} {score:.2f} {attr} {attr_score:.2f}"
       for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__      )
   ]
   return labels

  def 				__lowercase    (					self						:     List[str]   ,A						:     Optional[int]   ,A						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   [self.idaobj[i] for i in classes]
   if scores is not None:
    if labels is None:
     UpperCAmelCase__   :					List[Any]   				=   ["{:.0f}%".format(s * 100      ) for s in scores]
    else:
     UpperCAmelCase__   :					Any   				=   ["{} {:.0f}%".format(SCREAMING_SNAKE_CASE__   ,s * 100      ) for li, s in zip(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__      )]
   return labels

  def 				__lowercase    (					self						:     List[Any]   ,A						:     Tuple=255      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   np.random.randint(0   ,len(_COLORS      )      )
   UpperCAmelCase__   :					Any   				=   _COLORS[idx] * maximum
   if not self.rgb:
    UpperCAmelCase__   :					int   				=   ret[::-1]
   return ret

  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   if not self.pynb:
    UpperCAmelCase__   :					Optional[int]   				=   self.canvas.print_to_buffer()
    if (width, height) != (self.width, self.height):
     UpperCAmelCase__   :					str   				=   cva.resize(self.img   ,(width, height)      )
    else:
     UpperCAmelCase__   :					Any   				=   self.img
   else:
    UpperCAmelCase__   :					int   				=   io.BytesIO()  # works for cairo backend
    self.canvas.print_rgba(SCREAMING_SNAKE_CASE__      )
    UpperCAmelCase__   :					Optional[Any]   				=   self.width, self.height
    UpperCAmelCase__   :					Union[str, Any]   				=   buf.getvalue()
    UpperCAmelCase__   :					str   				=   self.img

   UpperCAmelCase__   :					List[Any]   				=   np.frombuffer(SCREAMING_SNAKE_CASE__   ,dtype="""uint8"""      )
   UpperCAmelCase__   :					Union[str, Any]   				=   buffer.reshape(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,4      )
   UpperCAmelCase__   :					Tuple   				=   np.split(SCREAMING_SNAKE_CASE__   ,[3]   ,axis=2      )

   try:
    import numexpr as ne  # fuse them with numexpr

    UpperCAmelCase__   :					Tuple   				=   ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""      )
   except ImportError:
    UpperCAmelCase__   :					int   				=   alpha.astype("""float32"""      ) / 2_5_5.0
    UpperCAmelCase__   :					Optional[int]   				=   img * (1 - alpha) + rgb * alpha

   return visualized_image.astype("""uint8"""      )




  def 				__lowercase    (					self						:     int   ,A						:     Dict   ,A						:     Optional[int]      ):






   '''simple docstring'''
   assert brightness_factor >= -1.0 and brightness_factor <= 1.0
   UpperCAmelCase__   :					str   				=   mplc.to_rgb(SCREAMING_SNAKE_CASE__      )
   UpperCAmelCase__   :					Tuple   				=   colorsys.rgb_to_hls(*mplc.to_rgb(SCREAMING_SNAKE_CASE__      )      )
   UpperCAmelCase__   :					List[Any]   				=   polygon_color[1] + (brightness_factor * polygon_color[1])
   UpperCAmelCase__   :					Union[str, Any]   				=   0.0 if modified_lightness < 0.0 else modified_lightness
   UpperCAmelCase__   :					int   				=   1.0 if modified_lightness > 1.0 else modified_lightness
   UpperCAmelCase__   :					List[str]   				=   colorsys.hls_to_rgb(polygon_color[0]   ,SCREAMING_SNAKE_CASE__   ,polygon_color[2]      )
   return modified_color







__UpperCAmelCase	=			(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)








"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


__UpperCAmelCase	=			logging.get_logger(__name__)







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    ["""pixel_values"""]

  def __init__(					self						:     Tuple   ,A						:     bool = True   ,A						:     Dict[str, int] = None   ,A						:     PILImageResampling = PIL.Image.NEAREST   ,A						:     bool = False   ,A						:     Dict[str, int] = None   ,A						:     Union[int, float] = 1 / 255   ,A						:     bool = False   ,A						:     bool = True   ,A						:     bool = True   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     bool = True   ,**A						:     List[str]   ,):






   '''simple docstring'''
   super().__init__(**A      )
   UpperCAmelCase__   :					Optional[Any]   				=   size if size is not None else {"""height""": 346, """width""": 346}
   UpperCAmelCase__   :					Union[str, Any]   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					Optional[int]   				=   do_resize
   UpperCAmelCase__   :					Optional[int]   				=   size
   UpperCAmelCase__   :					Optional[int]   				=   resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop
   UpperCAmelCase__   :					int   				=   crop_size
   UpperCAmelCase__   :					List[Any]   				=   do_rescale
   UpperCAmelCase__   :					List[str]   				=   rescale_factor
   UpperCAmelCase__   :					List[str]   				=   rescale_offset
   UpperCAmelCase__   :					Any   				=   do_normalize
   UpperCAmelCase__   :					Tuple   				=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase__   :					int   				=   image_std if image_std is not None else IMAGENET_STANDARD_STD
   UpperCAmelCase__   :					Optional[int]   				=   include_top

  def 				__lowercase    (					self						:     Tuple   ,A						:     np.ndarray   ,A						:     Dict[str, int]   ,A						:     PILImageResampling = PIL.Image.NEAREST   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   get_size_dict(A      )
   if "height" not in size or "width" not in size:
    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
   return resize(
       A   ,size=(size["""height"""], size["""width"""])   ,resample=A   ,data_format=A   ,**A      )

  def 				__lowercase    (					self						:     Union[str, Any]   ,A						:     np.ndarray   ,A						:     Dict[str, int]   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Union[str, Any]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A      )
   if "height" not in size or "width" not in size:
    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
   return center_crop(A   ,size=(size["""height"""], size["""width"""])   ,data_format=A   ,**A      )

  def 				__lowercase    (					self						:     List[Any]   ,A						:     np.ndarray   ,A						:     Union[int, float]   ,A						:     bool = True   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Any   ,):






   '''simple docstring'''
   if offset:
    UpperCAmelCase__   :					Tuple   				=   (image - 1_2_7.5) * scale
    if data_format is not None:
     UpperCAmelCase__   :					Any   				=   to_channel_dimension_format(A   ,A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   rescaled_image.astype(np.floataa      )
   else:
    UpperCAmelCase__   :					List[str]   				=   rescale(A   ,scale=A   ,data_format=A   ,**A      )
   return rescaled_image

  def 				__lowercase    (					self						:     Optional[Any]   ,A						:     np.ndarray   ,A						:     Union[float, List[float]]   ,A						:     Union[float, List[float]]   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     List[Any]   ,):






   '''simple docstring'''
   return normalize(A   ,mean=A   ,std=A   ,data_format=A   ,**A      )




  def 				__lowercase    (					self						:     Optional[int]   ,A						:     ImageInput   ,A						:     bool = None   ,A						:     Dict[str, int] = None   ,A						:     str=None   ,A						:     bool = None   ,A						:     Dict[str, int] = None   ,A						:     bool = None   ,A						:     float = None   ,A						:     bool = None   ,A						:     bool = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     bool = None   ,A						:     Optional[Union[str, TensorType]] = None   ,A						:     ChannelDimension = ChannelDimension.FIRST   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__   :					Optional[int]   				=   resample if resample is not None else self.resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase__   :					List[str]   				=   do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__   :					Any   				=   rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__   :					str   				=   rescale_offset if rescale_offset is not None else self.rescale_offset
   UpperCAmelCase__   :					Union[str, Any]   				=   do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase__   :					int   				=   image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase__   :					Any   				=   image_std if image_std is not None else self.image_std
   UpperCAmelCase__   :					List[Any]   				=   include_top if include_top is not None else self.include_top

   UpperCAmelCase__   :					List[Any]   				=   size if size is not None else self.size
   UpperCAmelCase__   :					int   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Any   				=   crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase__   :					str   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					Any   				=   make_list_of_images(A      )

   if not valid_images(A      ):
    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 or resample is None:
    raise ValueError("""Size and resample 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."""      )

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

   # All transformations expect numpy arrays.
   UpperCAmelCase__   :					List[str]   				=   [to_numpy_array(A      ) for image in images]

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

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

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

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

   if include_top:
    UpperCAmelCase__   :					Dict   				=   [self.normalize(image=A   ,mean=[0, 0, 0]   ,std=A      ) for image in images]

   UpperCAmelCase__   :					Optional[Any]   				=   [to_channel_dimension_format(A   ,A      ) for image in images]

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





