



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)
)







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"},
          ]							,     )