






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


_a   			=						1000





class __lowerCamelCase				:





	"""simple docstring"""






	def __init__(							self ,					UpperCAmelCase ,					UpperCAmelCase=1.2 ,					UpperCAmelCase="g" ,					UpperCAmelCase=0.5 ,					UpperCAmelCase="-" ,					UpperCAmelCase="test_out.jpg" ,					UpperCAmelCase=True ,					UpperCAmelCase=False ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=0.7 ,					):
							"""simple docstring"""







							if isinstance(UpperCAmelCase ,					torch.Tensor					):
													_UpperCAmelCase             =    img.numpy().astype('np.uint8'					)
							if isinstance(UpperCAmelCase ,					UpperCAmelCase					):
													_UpperCAmelCase             =    img_tensorize(UpperCAmelCase					)
							assert isinstance(UpperCAmelCase ,					np.ndarray					)

							_UpperCAmelCase      ,				_UpperCAmelCase             =    img.shape[1], img.shape[0]
							_UpperCAmelCase             =    mplfigure.Figure(frameon=UpperCAmelCase					)
							_UpperCAmelCase             =    fig.get_dpi()
							_UpperCAmelCase             =    (width * scale + 1e-2) / dpi
							_UpperCAmelCase             =    (height * scale + 1e-2) / dpi
							fig.set_size_inches(UpperCAmelCase ,					UpperCAmelCase					)
							_UpperCAmelCase             =    fig.add_axes([0.0, 0.0, 1.0, 1.0]					)
							ax.axis('off'					)
							ax.set_xlim(0.0 ,					UpperCAmelCase					)
							ax.set_ylim(UpperCAmelCase					)

							_UpperCAmelCase             =    saveas
							_UpperCAmelCase             =    rgb
							_UpperCAmelCase             =    pynb
							_UpperCAmelCase             =    img
							_UpperCAmelCase             =    edgecolor
							_UpperCAmelCase             =    0.5
							_UpperCAmelCase             =    linestyle
							_UpperCAmelCase             =    int(np.sqrt(min(UpperCAmelCase ,					UpperCAmelCase					)					) * scale // 3					)
							_UpperCAmelCase             =    width
							_UpperCAmelCase             =    height
							_UpperCAmelCase             =    scale
							_UpperCAmelCase             =    fig
							_UpperCAmelCase             =    ax
							_UpperCAmelCase             =    pad
							_UpperCAmelCase             =    idaobj
							_UpperCAmelCase             =    idaattr
							_UpperCAmelCase             =    FigureCanvasAgg(UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase=None					):
							"""simple docstring"""







							if color is None:
													_UpperCAmelCase             =    self.edgecolor
							((_UpperCAmelCase)      ,				(_UpperCAmelCase)      ,				(_UpperCAmelCase)      ,				(_UpperCAmelCase))             =    box
							_UpperCAmelCase             =    xa - xa
							_UpperCAmelCase             =    ya - ya
							self.ax.add_patch(
							    mpl.patches.Rectangle(
							        (xa, ya) ,					UpperCAmelCase ,					UpperCAmelCase ,					fill=UpperCAmelCase ,					edgecolor=UpperCAmelCase ,					linewidth=self.font_size // 3 ,					alpha=self.alpha ,					linestyle=self.linestyle ,					)					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=None					):
							"""simple docstring"""







							if len(boxes.shape					) > 2:
													_UpperCAmelCase             =    boxes[0]
							if len(obj_ids.shape					) > 1:
													_UpperCAmelCase             =    obj_ids[0]
							if len(obj_scores.shape					) > 1:
													_UpperCAmelCase             =    obj_scores[0]
							if len(attr_ids.shape					) > 1:
													_UpperCAmelCase             =    attr_ids[0]
							if len(attr_scores.shape					) > 1:
													_UpperCAmelCase             =    attr_scores[0]
							if isinstance(UpperCAmelCase ,					torch.Tensor					):
													_UpperCAmelCase             =    boxes.numpy()
							if isinstance(UpperCAmelCase ,					UpperCAmelCase					):
													_UpperCAmelCase             =    np.array(UpperCAmelCase					)
							assert isinstance(UpperCAmelCase ,					np.ndarray					)
							_UpperCAmelCase             =    np.prod(boxes[:, 2:] - boxes[:, :2] ,					axis=1					)
							_UpperCAmelCase             =    np.argsort(-areas					).tolist()
							_UpperCAmelCase             =    boxes[sorted_idxs] if boxes is not None else None
							_UpperCAmelCase             =    obj_ids[sorted_idxs] if obj_ids is not None else None
							_UpperCAmelCase             =    obj_scores[sorted_idxs] if obj_scores is not None else None
							_UpperCAmelCase             =    attr_ids[sorted_idxs] if attr_ids is not None else None
							_UpperCAmelCase             =    attr_scores[sorted_idxs] if attr_scores is not None else None

							_UpperCAmelCase             =    [self._random_color(maximum=1					) for _ in range(len(UpperCAmelCase					)					)]
							_UpperCAmelCase             =    [assigned_colors[idx] for idx in sorted_idxs]
							if obj_ids is not None:
													_UpperCAmelCase             =    self._create_text_labels_attr(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)
													for i in range(len(UpperCAmelCase					)					):
																			_UpperCAmelCase             =    assigned_colors[i]
																			self.add_box(boxes[i] ,					UpperCAmelCase					)
																			self.draw_labels(labels[i] ,					boxes[i] ,					UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					):
							"""simple docstring"""







							_UpperCAmelCase      ,				_UpperCAmelCase      ,				_UpperCAmelCase      ,				_UpperCAmelCase             =    box
							_UpperCAmelCase             =    (xa, ya)
							_UpperCAmelCase             =    (ya - ya) * (xa - xa)
							_UpperCAmelCase             =    _SMALL_OBJ * self.scale
							if instance_area < small or ya - ya < 40 * self.scale:
													if ya >= self.height - 5:
																			_UpperCAmelCase             =    (xa, ya)
													else:
																			_UpperCAmelCase             =    (xa, ya)

							_UpperCAmelCase             =    (ya - ya) / np.sqrt(self.height * self.width					)
							_UpperCAmelCase             =    self._change_color_brightness(UpperCAmelCase ,					brightness_factor=0.7					)
							_UpperCAmelCase             =    np.clip((height_ratio - 0.02) / 0.08 + 1 ,					1.2 ,					2					)
							font_size *= 0.75 * self.font_size

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

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase="g" ,					UpperCAmelCase="left" ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    0
							_UpperCAmelCase             =    self.font_size
							_UpperCAmelCase             =    np.maximum(list(mplc.to_rgb(UpperCAmelCase					)					) ,					0.2					)
							_UpperCAmelCase             =    max(0.8 ,					np.max(UpperCAmelCase					)					)
							_UpperCAmelCase             =    {
							    'facecolor': 'black',
							    'alpha': self.alpha,
							    'pad': self.pad,
							    'edgecolor': 'none',
							}
							_UpperCAmelCase      ,				_UpperCAmelCase             =    position
							self.ax.text(
							    UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					size=font_size * self.scale ,					family='sans-serif' ,					bbox=UpperCAmelCase ,					verticalalignment='top' ,					horizontalalignment=UpperCAmelCase ,					color=UpperCAmelCase ,					zorder=10 ,					rotation=UpperCAmelCase ,					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase=None					):
							"""simple docstring"""







							if saveas is None:
													_UpperCAmelCase             =    self.saveas
							if saveas.lower().endswith('.jpg'					) or saveas.lower().endswith('.png'					):
													cva.imwrite(
													    UpperCAmelCase ,					self._get_buffer()[:, :, ::-1] ,					)
							else:
													self.fig.savefig(UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					):
							"""simple docstring"""







							_UpperCAmelCase             =    [self.idaobj[i] for i in classes]
							_UpperCAmelCase             =    [self.idaattr[i] for i in attr_classes]
							_UpperCAmelCase             =    [
							    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
							    for label, score, attr, attr_score in zip(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)
							]
							return labels

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase					):
							"""simple docstring"""







							_UpperCAmelCase             =    [self.idaobj[i] for i in classes]
							if scores is not None:
													if labels is None:
																			_UpperCAmelCase             =    ['{:.0f}%'.format(s * 100					) for s in scores]
													else:
																			_UpperCAmelCase             =    ['{} {:.0f}%'.format(UpperCAmelCase ,					s * 100					) for li, s in zip(UpperCAmelCase ,					UpperCAmelCase					)]
							return labels

	def  UpperCamelCase					(							self ,					UpperCAmelCase=255					):
							"""simple docstring"""







							_UpperCAmelCase             =    np.random.randint(0 ,					len(_COLORS					)					)
							_UpperCAmelCase             =    _COLORS[idx] * maximum
							if not self.rgb:
													_UpperCAmelCase             =    ret[::-1]
							return ret

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							if not self.pynb:
													_UpperCAmelCase      ,				_UpperCAmelCase             =    self.canvas.print_to_buffer()
													if (width, height) != (self.width, self.height):
																			_UpperCAmelCase             =    cva.resize(self.img ,					(width, height)					)
													else:
																			_UpperCAmelCase             =    self.img
							else:
													_UpperCAmelCase             =    io.BytesIO()  # works for cairo backend
													self.canvas.print_rgba(UpperCAmelCase					)
													_UpperCAmelCase      ,				_UpperCAmelCase             =    self.width, self.height
													_UpperCAmelCase             =    buf.getvalue()
													_UpperCAmelCase             =    self.img

							_UpperCAmelCase             =    np.frombuffer(UpperCAmelCase ,					dtype='uint8'					)
							_UpperCAmelCase             =    buffer.reshape(UpperCAmelCase ,					UpperCAmelCase ,					4					)
							_UpperCAmelCase      ,				_UpperCAmelCase             =    np.split(UpperCAmelCase ,					[3] ,					axis=2					)

							try:
													import numexpr as ne  # fuse them with numexpr

													_UpperCAmelCase             =    ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'					)
							except ImportError:
													_UpperCAmelCase             =    alpha.astype('float32'					) / 2_55.0
													_UpperCAmelCase             =    img * (1 - alpha) + rgb * alpha

							return visualized_image.astype('uint8'					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase					):
							"""simple docstring"""







							assert brightness_factor >= -1.0 and brightness_factor <= 1.0
							_UpperCAmelCase             =    mplc.to_rgb(UpperCAmelCase					)
							_UpperCAmelCase             =    colorsys.rgb_to_hls(*mplc.to_rgb(UpperCAmelCase					)					)
							_UpperCAmelCase             =    polygon_color[1] + (brightness_factor * polygon_color[1])
							_UpperCAmelCase             =    0.0 if modified_lightness < 0.0 else modified_lightness
							_UpperCAmelCase             =    1.0 if modified_lightness > 1.0 else modified_lightness
							_UpperCAmelCase             =    colorsys.hls_to_rgb(polygon_color[0] ,					UpperCAmelCase ,					polygon_color[2]					)
							return modified_color



_a   			=						(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)








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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_a   			=						logging.get_logger(__name__)


if is_vision_available():
		import PIL





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   ["pixel_values"]

	def __init__(							self ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = PILImageResampling.BICUBIC ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = True ,					UpperCAmelCase = 1 / 255 ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = True ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase					)
							_UpperCAmelCase             =    size if size is not None else {'shortest_edge': 224}
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)

							_UpperCAmelCase             =    do_resize
							_UpperCAmelCase             =    size
							_UpperCAmelCase             =    resample
							_UpperCAmelCase             =    do_center_crop
							_UpperCAmelCase             =    crop_size
							_UpperCAmelCase             =    do_rescale
							_UpperCAmelCase             =    rescale_factor
							_UpperCAmelCase             =    do_normalize
							_UpperCAmelCase             =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							_UpperCAmelCase             =    image_std if image_std is not None else OPENAI_CLIP_STD
							_UpperCAmelCase             =    do_convert_rgb

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = PILImageResampling.BICUBIC ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    get_resize_output_image_size(
							    UpperCAmelCase ,					size=(size['height'], size['width']) ,					default_to_square=UpperCAmelCase					)
							return resize(UpperCAmelCase ,					size=UpperCAmelCase ,					resample=UpperCAmelCase ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)
							return center_crop(UpperCAmelCase ,					size=(size['height'], size['width']) ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							return rescale(UpperCAmelCase ,					scale=UpperCAmelCase ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							return normalize(UpperCAmelCase ,					mean=UpperCAmelCase ,					std=UpperCAmelCase ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = ChannelDimension.FIRST ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    do_resize if do_resize is not None else self.do_resize
							_UpperCAmelCase             =    size if size is not None else self.size
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    resample if resample is not None else self.resample
							_UpperCAmelCase             =    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCAmelCase             =    crop_size if crop_size is not None else self.crop_size
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)
							_UpperCAmelCase             =    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCAmelCase             =    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCAmelCase             =    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCAmelCase             =    image_mean if image_mean is not None else self.image_mean
							_UpperCAmelCase             =    image_std if image_std is not None else self.image_std
							_UpperCAmelCase             =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							_UpperCAmelCase             =    make_list_of_images(UpperCAmelCase					)

							if not valid_images(UpperCAmelCase					):
													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.'					)

							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.'					)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													_UpperCAmelCase             =    [convert_to_rgb(UpperCAmelCase					) for image in images]

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

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

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

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

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

							_UpperCAmelCase             =    [to_channel_dimension_format(UpperCAmelCase ,					UpperCAmelCase					) for image in images]

							_UpperCAmelCase             =    {'pixel_values': images}
							return BatchFeature(data=UpperCAmelCase ,					tensor_type=UpperCAmelCase					)
