





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    :							Union[str, Any]      =       1_0_0_0







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  ,				)->					Dict:
     if isinstance(_lowercase  ,				torch.Tensor	):
        __A      					=   img.numpy().astype('''np.uint8'''	)
     if isinstance(_lowercase  ,				_lowercase	):
        __A      					=   img_tensorize(_lowercase	)
     assert isinstance(_lowercase  ,				np.ndarray	)

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

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

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase=None	)->					List[str]:
     if color is None:
        __A      					=   self.edgecolor
     ((__A)					,						(__A)					,						(__A)					,						(__A))      					=   box
     __A      					=   xa - xa
     __A      					=   ya - ya
     self.ax.add_patch(
         mpl.patches.Rectangle(
             (xa, ya)  ,				_lowercase  ,				_lowercase  ,				fill=_lowercase  ,				edgecolor=_lowercase  ,				linewidth=self.font_size // 3  ,				alpha=self.alpha  ,				linestyle=self.linestyle  ,				)	)

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

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

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase	)->					Union[str, Any]:
     __A					,						__A					,						__A					,						__A      					=   box
     __A      					=   (xa, ya)
     __A      					=   (ya - ya) * (xa - xa)
     __A      					=   _SMALL_OBJ * self.scale
     if instance_area < small or ya - ya < 40 * self.scale:
        if ya >= self.height - 5:
           __A      					=   (xa, ya)
        else:
           __A      					=   (xa, ya)

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

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

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

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

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase	)->					Tuple:
     __A      					=   [self.idaobj[i] for i in classes]
     __A      					=   [self.idaattr[i] for i in attr_classes]
     __A      					=   [
         f"{label} {score:.2f} {attr} {attr_score:.2f}"
         for label, score, attr, attr_score in zip(_lowercase  ,				_lowercase  ,				_lowercase  ,				_lowercase	)
     ]
     return labels

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase	)->					int:
     __A      					=   [self.idaobj[i] for i in classes]
     if scores is not None:
        if labels is None:
           __A      					=   ['''{:.0f}%'''.format(s * 1_00	) for s in scores]
        else:
           __A      					=   ['''{} {:.0f}%'''.format(_lowercase  ,				s * 1_00	) for li, s in zip(_lowercase  ,				_lowercase	)]
     return labels

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase=2_55	)->					Optional[Any]:
     __A      					=   np.random.randint(0  ,				len(_COLORS	)	)
     __A      					=   _COLORS[idx] * maximum
     if not self.rgb:
        __A      					=   ret[::-1]
     return ret

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					int:
     if not self.pynb:
        __A					,						__A      					=   self.canvas.print_to_buffer()
        if (width, height) != (self.width, self.height):
           __A      					=   cva.resize(self.img  ,				(width, height)	)
        else:
           __A      					=   self.img
     else:
        __A      					=   io.BytesIO()  # works for cairo backend
        self.canvas.print_rgba(_lowercase	)
        __A					,						__A      					=   self.width, self.height
        __A      					=   buf.getvalue()
        __A      					=   self.img

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

     try:
        import numexpr as ne  # fuse them with numexpr

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

     return visualized_image.astype('''uint8'''	)





  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase	)->					List[str]:
     assert brightness_factor >= -1.0 and brightness_factor <= 1.0
     __A      					=   mplc.to_rgb(_lowercase	)
     __A      					=   colorsys.rgb_to_hls(*mplc.to_rgb(_lowercase	)	)
     __A      					=   polygon_color[1] + (brightness_factor * polygon_color[1])
     __A      					=   0.0 if modified_lightness < 0.0 else modified_lightness
     __A      					=   1.0 if modified_lightness > 1.0 else modified_lightness
     __A      					=   colorsys.hls_to_rgb(polygon_color[0]  ,				_lowercase  ,				polygon_color[2]	)
     return modified_color
_UpperCamelCase    :							str      =       (
    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)
)




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 		__magic_name__ :






      @staticmethod
      def      UpperCAmelCase_							(							*_lowercase						,		**_lowercase     )->			Tuple:
          pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__magic_name__ (				unittest.TestCase							):
     UpperCamelCase_     :Union[str, Any]									=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     UpperCamelCase_     :Any									=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		ImageClassificationPipeline(model=_lowercase						,		image_processor=_lowercase						,		top_k=2     )
         UpperCamelCase_          =		[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"     )

         self.assertEqual(
             _lowercase						,		[
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
             ]						,		)

         import datasets

         UpperCamelCase_          =		datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,		"image"						,		split="test"     )

         # Accepts URL + PIL.Image + lists
         UpperCamelCase_          =		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(
             _lowercase						,		[
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
             ]						,		)






     @require_torch
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     @require_tf
     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase						,		framework="tf"     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         UpperCamelCase_          =		pipeline(
             "image-classification"						,		model="hf-internal-testing/tiny-random-vit"						,		tokenizer=_lowercase     )

         self.assertIs(image_classifier.tokenizer						,		_lowercase     )






     @slow
     @require_torch
     def      UpperCAmelCase_							(							self     )->			int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-conv"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.4_385, "label": "tabby, tabby cat"},
                 {"score": 0.321, "label": "tiger cat"},
                 {"score": 0.0_502, "label": "Egyptian cat"},
                 {"score": 0.0_137, "label": "crib, cot"},
                 {"score": 0.007, "label": "radiator"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-fourier"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.5_658, "label": "tabby, tabby cat"},
                 {"score": 0.1_309, "label": "tiger cat"},
                 {"score": 0.0_722, "label": "Egyptian cat"},
                 {"score": 0.0_707, "label": "remote control, remote"},
                 {"score": 0.0_082, "label": "computer keyboard, keypad"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-learned"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.3_022, "label": "tabby, tabby cat"},
                 {"score": 0.2_362, "label": "Egyptian cat"},
                 {"score": 0.1_856, "label": "tiger cat"},
                 {"score": 0.0_324, "label": "remote control, remote"},
                 {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
             ]						,		)
