code: 
  
  
  
  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
  
  
  lowerCamelCase			:							Tuple       =1000
  
  
  
  
  
  class     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     Any							,     SCREAMING_SNAKE_CASE      :     List[Any]=1.2							,     SCREAMING_SNAKE_CASE      :     Tuple="g"							,     SCREAMING_SNAKE_CASE      :     Tuple=0.5							,     SCREAMING_SNAKE_CASE      :     Optional[int]="-"							,     SCREAMING_SNAKE_CASE      :     Optional[Any]="test_out.jpg"							,     SCREAMING_SNAKE_CASE      :     List[str]=True							,     SCREAMING_SNAKE_CASE      :     Optional[int]=False							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=None							,     SCREAMING_SNAKE_CASE      :     Dict=None							,     SCREAMING_SNAKE_CASE      :     str=0.7							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if isinstance(__snake_case							,     torch.Tensor		):
             UpperCamelCase__    :     Union[str, Any]        =						img.numpy().astype("np.uint8"		)
        if isinstance(__snake_case							,     __snake_case		):
             UpperCamelCase__    :     int        =						img_tensorize(__snake_case		)
        assert isinstance(__snake_case							,     np.ndarray		)
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Any        =						img.shape[1], img.shape[0]
        UpperCamelCase__    :     Optional[Any]        =						mplfigure.Figure(frameon=__snake_case		)
        UpperCamelCase__    :     Union[str, Any]        =						fig.get_dpi()
        UpperCamelCase__    :     Union[str, Any]        =						(width * scale + 1e-2) / dpi
        UpperCamelCase__    :     Optional[int]        =						(height * scale + 1e-2) / dpi
        fig.set_size_inches(__snake_case							,     __snake_case		)
        UpperCamelCase__    :     Union[str, Any]        =						fig.add_axes([0.0, 0.0, 1.0, 1.0]		)
        ax.axis("off"		)
        ax.set_xlim(0.0							,     __snake_case		)
        ax.set_ylim(__snake_case		)
  
        UpperCamelCase__    :     Tuple        =						saveas
        UpperCamelCase__    :     int        =						rgb
        UpperCamelCase__    :     Optional[int]        =						pynb
        UpperCamelCase__    :     Any        =						img
        UpperCamelCase__    :     Optional[int]        =						edgecolor
        UpperCamelCase__    :     Dict        =						0.5
        UpperCamelCase__    :     int        =						linestyle
        UpperCamelCase__    :     Union[str, Any]        =						int(np.sqrt(min(__snake_case							,     __snake_case		)		) * scale // 3		)
        UpperCamelCase__    :     Optional[int]        =						width
        UpperCamelCase__    :     Dict        =						height
        UpperCamelCase__    :     List[Any]        =						scale
        UpperCamelCase__    :     List[Any]        =						fig
        UpperCamelCase__    :     List[Any]        =						ax
        UpperCamelCase__    :     Dict        =						pad
        UpperCamelCase__    :     List[str]        =						idaobj
        UpperCamelCase__    :     Optional[Any]        =						idaattr
        UpperCamelCase__    :     Union[str, Any]        =						FigureCanvasAgg(__snake_case		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Dict							,     SCREAMING_SNAKE_CASE      :     int=None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if color is None:
             UpperCamelCase__    :     Optional[int]        =						self.edgecolor
        ((UpperCamelCase__)       ,  (UpperCamelCase__)       ,  (UpperCamelCase__)       ,  (UpperCamelCase__))    :     Union[str, Any]        =						box
        UpperCamelCase__    :     int        =						xa - xa
        UpperCamelCase__    :     List[Any]        =						ya - ya
        self.ax.add_patch(
            mpl.patches.Rectangle(
                (xa, ya)							,     __snake_case							,     __snake_case							,     fill=__snake_case							,     edgecolor=__snake_case							,     linewidth=self.font_size // 3							,     alpha=self.alpha							,     linestyle=self.linestyle							,     )		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     int=None							,     SCREAMING_SNAKE_CASE      :     Any=None							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=None							,     SCREAMING_SNAKE_CASE      :     Optional[int]=None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if len(boxes.shape		) > 2:
             UpperCamelCase__    :     List[Any]        =						boxes[0]
        if len(obj_ids.shape		) > 1:
             UpperCamelCase__    :     Union[str, Any]        =						obj_ids[0]
        if len(obj_scores.shape		) > 1:
             UpperCamelCase__    :     Any        =						obj_scores[0]
        if len(attr_ids.shape		) > 1:
             UpperCamelCase__    :     Any        =						attr_ids[0]
        if len(attr_scores.shape		) > 1:
             UpperCamelCase__    :     List[str]        =						attr_scores[0]
        if isinstance(__snake_case							,     torch.Tensor		):
             UpperCamelCase__    :     int        =						boxes.numpy()
        if isinstance(__snake_case							,     __snake_case		):
             UpperCamelCase__    :     Optional[int]        =						np.array(__snake_case		)
        assert isinstance(__snake_case							,     np.ndarray		)
        UpperCamelCase__    :     int        =						np.prod(boxes[:, 2:] - boxes[:, :2]							,     axis=1		)
        UpperCamelCase__    :     Optional[Any]        =						np.argsort(-areas		).tolist()
        UpperCamelCase__    :     Optional[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__    :     List[str]        =						obj_scores[sorted_idxs] if obj_scores is not None else None
        UpperCamelCase__    :     str        =						attr_ids[sorted_idxs] if attr_ids is not None else None
        UpperCamelCase__    :     Any        =						attr_scores[sorted_idxs] if attr_scores is not None else None
  
        UpperCamelCase__    :     List[str]        =						[self._random_color(maximum=1		) for _ in range(len(__snake_case		)		)]
        UpperCamelCase__    :     int        =						[assigned_colors[idx] for idx in sorted_idxs]
        if obj_ids is not None:
             UpperCamelCase__    :     List[Any]        =						self._create_text_labels_attr(__snake_case							,     __snake_case							,     __snake_case							,     __snake_case		)
             for i in range(len(__snake_case		)		):
                  UpperCamelCase__    :     List[str]        =						assigned_colors[i]
                  self.add_box(boxes[i]							,     __snake_case		)
                  self.draw_labels(labels[i]							,     boxes[i]							,     __snake_case		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Any							,     SCREAMING_SNAKE_CASE      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__    :     Optional[Any]        =						box
        UpperCamelCase__    :     Any        =						(xa, ya)
        UpperCamelCase__    :     Optional[int]        =						(ya - ya) * (xa - xa)
        UpperCamelCase__    :     Dict        =						_SMALL_OBJ * self.scale
        if instance_area < small or ya - ya < 40 * self.scale:
             if ya >= self.height - 5:
                  UpperCamelCase__    :     Any        =						(xa, ya)
             else:
                  UpperCamelCase__    :     str        =						(xa, ya)
  
        UpperCamelCase__    :     Union[str, Any]        =						(ya - ya) / np.sqrt(self.height * self.width		)
        UpperCamelCase__    :     str        =						self._change_color_brightness(__snake_case							,     brightness_factor=0.7		)
        UpperCamelCase__    :     Tuple        =						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=__snake_case							,     position=__snake_case							,     color=__snake_case							,     )
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Any="g"							,     SCREAMING_SNAKE_CASE      :     str="left"							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						0
        UpperCamelCase__    :     List[str]        =						self.font_size
        UpperCamelCase__    :     Tuple        =						np.maximum(list(mplc.to_rgb(__snake_case		)		)							,     0.2		)
        UpperCamelCase__    :     Optional[int]        =						max(0.8							,     np.max(__snake_case		)		)
        UpperCamelCase__    :     Optional[Any]        =						{
            "facecolor": "black",
            "alpha": self.alpha,
            "pad": self.pad,
            "edgecolor": "none",
        }
        UpperCamelCase__       ,  UpperCamelCase__    :     List[str]        =						position
        self.ax.text(
            __snake_case							,     __snake_case							,     __snake_case							,     size=font_size * self.scale							,     family="sans-serif"							,     bbox=__snake_case							,     verticalalignment="top"							,     horizontalalignment=__snake_case							,     color=__snake_case							,     zorder=10							,     rotation=__snake_case							,     )
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     List[str]=None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if saveas is None:
             UpperCamelCase__    :     List[str]        =						self.saveas
        if saveas.lower().endswith(".jpg"		) or saveas.lower().endswith(".png"		):
             cva.imwrite(
                 __snake_case							,     self._get_buffer()[:, :, ::-1]							,     )
        else:
             self.fig.savefig(__snake_case		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Any        =						[self.idaobj[i] for i in classes]
        UpperCamelCase__    :     Dict        =						[self.idaattr[i] for i in attr_classes]
        UpperCamelCase__    :     Optional[Any]        =						[
            F'{label} {score:.2f} {attr} {attr_score:.2f}'
            for label, score, attr, attr_score in zip(__snake_case							,     __snake_case							,     __snake_case							,     __snake_case		)
        ]
        return labels
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Dict							,     SCREAMING_SNAKE_CASE      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						[self.idaobj[i] for i in classes]
        if scores is not None:
             if labels is None:
                  UpperCamelCase__    :     Tuple        =						["{:.0f}%".format(s * 1_00		) for s in scores]
             else:
                  UpperCamelCase__    :     Optional[int]        =						["{} {:.0f}%".format(__snake_case							,     s * 1_00		) for li, s in zip(__snake_case							,     __snake_case		)]
        return labels
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     int=2_55		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						np.random.randint(0							,     len(_COLORS		)		)
        UpperCamelCase__    :     Any        =						_COLORS[idx] * maximum
        if not self.rgb:
             UpperCamelCase__    :     Union[str, Any]        =						ret[::-1]
        return ret
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if not self.pynb:
             UpperCamelCase__       ,  UpperCamelCase__    :     str        =						self.canvas.print_to_buffer()
             if (width, height) != (self.width, self.height):
                  UpperCamelCase__    :     List[Any]        =						cva.resize(self.img							,     (width, height)		)
             else:
                  UpperCamelCase__    :     Any        =						self.img
        else:
             UpperCamelCase__    :     int        =						io.BytesIO()  # works for cairo backend
             self.canvas.print_rgba(__snake_case		)
             UpperCamelCase__       ,  UpperCamelCase__    :     Union[str, Any]        =						self.width, self.height
             UpperCamelCase__    :     Optional[Any]        =						buf.getvalue()
             UpperCamelCase__    :     str        =						self.img
  
        UpperCamelCase__    :     str        =						np.frombuffer(__snake_case							,     dtype="uint8"		)
        UpperCamelCase__    :     Any        =						buffer.reshape(__snake_case							,     __snake_case							,     4		)
        UpperCamelCase__       ,  UpperCamelCase__    :     int        =						np.split(__snake_case							,     [3]							,     axis=2		)
  
        try:
             import numexpr as ne  # fuse them with numexpr
  
             UpperCamelCase__    :     List[str]        =						ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"		)
        except ImportError:
             UpperCamelCase__    :     Optional[int]        =						alpha.astype("float32"		) / 2_5_5.0
             UpperCamelCase__    :     List[Any]        =						img * (1 - alpha) + rgb * alpha
  
        return visualized_image.astype("uint8"		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
        UpperCamelCase__    :     List[Any]        =						mplc.to_rgb(__snake_case		)
        UpperCamelCase__    :     str        =						colorsys.rgb_to_hls(*mplc.to_rgb(__snake_case		)		)
        UpperCamelCase__    :     Optional[Any]        =						polygon_color[1] + (brightness_factor * polygon_color[1])
        UpperCamelCase__    :     int        =						0.0 if modified_lightness < 0.0 else modified_lightness
        UpperCamelCase__    :     str        =						1.0 if modified_lightness > 1.0 else modified_lightness
        UpperCamelCase__    :     str        =						colorsys.hls_to_rgb(polygon_color[0]							,     __snake_case							,     polygon_color[2]		)
        return modified_color
  
  
  
  
  
  
  
  lowerCamelCase			:							Union[str, Any]       =(
      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)
  )
style_context: 
  
  
  
  
  
  
  import unittest
  
  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      PreTrainedTokenizer,
      is_vision_available,
  )
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      nested_simplify,
      require_tf,
      require_torch,
      require_torch_or_tf,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_vision_available():
   from PIL import Image
  else:
  
  
  
  
  
   class     __a						:
  
  
  
  
  
  
  
    @staticmethod
    def       __lowercase      (	*SCREAMING_SNAKE_CASE      :     Optional[Any]							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]		):
  
  
         '''simple docstring'''
  
  
  
  
  
  
         pass
  
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class     __a						(					unittest.TestCase ):
   _lowerCAmelCase : Tuple 				=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   _lowerCAmelCase : str 				=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE							,     image_processor=SCREAMING_SNAKE_CASE							,     top_k=2		)
        UpperCamelCase__    :     Union[str, Any]        =						[
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
        ]
        return image_classifier, examples
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  
        self.assertEqual(
            SCREAMING_SNAKE_CASE							,     [
                {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
            ]							,     )
  
        import datasets
  
        UpperCamelCase__    :     int        =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,     "image"							,     split="test"		)
  
        # Accepts URL + PIL.Image + lists
        UpperCamelCase__    :     Tuple        =						image_classifier(
            [
                Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                # RGBA
                dataset[0]["file"],
                # LA
                dataset[1]["file"],
                # L
                dataset[2]["file"],
            ]		)
        self.assertEqual(
            SCREAMING_SNAKE_CASE							,     [
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
            ]							,     )
  
  
  
  
  
  
  
   @require_torch
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						"hf-internal-testing/tiny-random-vit"
        UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )
  
        UpperCamelCase__    :     Optional[Any]        =						image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]							,     top_k=2							,     )
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
            ]							,     )
  
  
  
  
  
  
  
   @require_tf
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						"hf-internal-testing/tiny-random-vit"
        UpperCamelCase__    :     int        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE							,     framework="tf"		)
  
        UpperCamelCase__    :     List[Any]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )
  
        UpperCamelCase__    :     int        =						image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]							,     top_k=2							,     )
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
            ]							,     )
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Any        =						PreTrainedTokenizer()
  
        # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
        UpperCamelCase__    :     str        =						pipeline(
            "image-classification"							,     model="hf-internal-testing/tiny-random-vit"							,     tokenizer=SCREAMING_SNAKE_CASE		)
  
        self.assertIs(image_classifier.tokenizer							,     SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   @slow
   @require_torch
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-conv"		)
        UpperCamelCase__    :     Any        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
                {"score": 0.3_2_1, "label": "tiger cat"},
                {"score": 0.0_5_0_2, "label": "Egyptian cat"},
                {"score": 0.0_1_3_7, "label": "crib, cot"},
                {"score": 0.0_0_7, "label": "radiator"},
            ]							,     )
  
        UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-fourier"		)
        UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
                {"score": 0.1_3_0_9, "label": "tiger cat"},
                {"score": 0.0_7_2_2, "label": "Egyptian cat"},
                {"score": 0.0_7_0_7, "label": "remote control, remote"},
                {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
            ]							,     )
  
        UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-learned"		)
        UpperCamelCase__    :     Optional[int]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
                {"score": 0.2_3_6_2, "label": "Egyptian cat"},
                {"score": 0.1_8_5_6, "label": "tiger cat"},
                {"score": 0.0_3_2_4, "label": "remote control, remote"},
                {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
            ]							,     )