
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


lowercase_											=	1_000





class   __UpperCamelCase				:






     """simple docstring"""







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







       """simple docstring"""




       if isinstance(_A      ,		torch.Tensor				):
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						img.numpy().astype('''np.uint8'''				)
       if isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Tuple			=						img_tensorize(_A				)
       assert isinstance(_A      ,		np.ndarray				)

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

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



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    List[Any]      ,		_A    :    Any=None				):







       """simple docstring"""




       if color is None:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.edgecolor
       ((__SCREAMING_SNAKE_CASE),						(__SCREAMING_SNAKE_CASE),						(__SCREAMING_SNAKE_CASE),						(__SCREAMING_SNAKE_CASE))     :					Tuple			=						box
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						xa - xa
       __SCREAMING_SNAKE_CASE     :					int			=						ya - ya
       self.ax.add_patch(
           mpl.patches.Rectangle(
               (xa, ya)      ,		_A      ,		_A      ,		fill=_A      ,		edgecolor=_A      ,		linewidth=self.font_size // 3      ,		alpha=self.alpha      ,		linestyle=self.linestyle      ,		)				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    Union[str, Any]      ,		_A    :    List[Any]=None      ,		_A    :    List[str]=None      ,		_A    :    Optional[int]=None      ,		_A    :    Optional[int]=None				):







       """simple docstring"""




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

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



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    int      ,		_A    :    Tuple      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						box
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(xa, ya)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(ya - ya) * (xa - xa)
       __SCREAMING_SNAKE_CASE     :					str			=						_SMALL_OBJ * self.scale
       if instance_area < small or ya - ya < 40 * self.scale:
         if ya >= self.height - 5:
           __SCREAMING_SNAKE_CASE     :					int			=						(xa, ya)
         else:
           __SCREAMING_SNAKE_CASE     :					Dict			=						(xa, ya)

       __SCREAMING_SNAKE_CASE     :					Dict			=						(ya - ya) / np.sqrt(self.height * self.width				)
       __SCREAMING_SNAKE_CASE     :					int			=						self._change_color_brightness(_A      ,		brightness_factor=0.7				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						np.clip((height_ratio - 0.02) / 0.08 + 1      ,		1.2      ,		2				)
       font_size *= 0.75 * self.font_size

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



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    Union[str, Any]      ,		_A    :    Any      ,		_A    :    List[str]="g"      ,		_A    :    Dict="left"      ,		):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Dict=None				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    Any      ,		_A    :    List[Any]      ,		_A    :    List[Any]      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.idaobj[i] for i in classes]
       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.idaattr[i] for i in attr_classes]
       __SCREAMING_SNAKE_CASE     :					int			=						[
           F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
           for label, score, attr, attr_score in zip(_A      ,		_A      ,		_A      ,		_A				)
       ]
       return labels



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    str      ,		_A    :    List[str]				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    Tuple=255				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       if not self.pynb:
         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						self.canvas.print_to_buffer()
         if (width, height) != (self.width, self.height):
           __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						cva.resize(self.img      ,		(width, height)				)
         else:
           __SCREAMING_SNAKE_CASE     :					Tuple			=						self.img
       else:
         __SCREAMING_SNAKE_CASE     :					List[str]			=						io.BytesIO()  # works for cairo backend
         self.canvas.print_rgba(_A				)
         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.width, self.height
         __SCREAMING_SNAKE_CASE     :					List[str]			=						buf.getvalue()
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.img

       __SCREAMING_SNAKE_CASE     :					List[str]			=						np.frombuffer(_A      ,		dtype='''uint8'''				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						buffer.reshape(_A      ,		_A      ,		4				)
       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						np.split(_A      ,		[3]      ,		axis=2				)

       try:
         import numexpr as ne  # fuse them with numexpr

         __SCREAMING_SNAKE_CASE     :					str			=						ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''				)
       except ImportError:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						alpha.astype('''float32'''				) / 2_55.0
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						img * (1 - alpha) + rgb * alpha

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




     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    Tuple      ,		_A    :    int				):







       """simple docstring"""




       assert brightness_factor >= -1.0 and brightness_factor <= 1.0
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						mplc.to_rgb(_A				)
       __SCREAMING_SNAKE_CASE     :					str			=						colorsys.rgb_to_hls(*mplc.to_rgb(_A				)				)
       __SCREAMING_SNAKE_CASE     :					Any			=						polygon_color[1] + (brightness_factor * polygon_color[1])
       __SCREAMING_SNAKE_CASE     :					Any			=						0.0 if modified_lightness < 0.0 else modified_lightness
       __SCREAMING_SNAKE_CASE     :					Dict			=						1.0 if modified_lightness > 1.0 else modified_lightness
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						colorsys.hls_to_rgb(polygon_color[0]      ,		_A      ,		polygon_color[2]				)
       return modified_color






lowercase_											=	(
    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 load_tool

from .test_tools_common import ToolTesterMixin


lowercase_											=	"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						load_tool('''summarization'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					Any			=						load_tool('''summarization'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.remote_tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)




     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.remote_tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)
