
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:					Optional[Any]     =1_000





class 						__lowercase:


       """simple docstring"""







       def __init__(     self					:			List[str]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			str=1.2  ,      _lowerCAmelCase					:			Optional[Any]="g"  ,      _lowerCAmelCase					:			Optional[int]=0.5  ,      _lowerCAmelCase					:			Tuple="-"  ,      _lowerCAmelCase					:			List[str]="test_out.jpg"  ,      _lowerCAmelCase					:			Dict=True  ,      _lowerCAmelCase					:			int=False  ,      _lowerCAmelCase					:			Tuple=None  ,      _lowerCAmelCase					:			Any=None  ,      _lowerCAmelCase					:			Union[str, Any]=0.7  ,      )					->   Optional[Any]:
         if isinstance(UpperCAmelCase__  ,      torch.Tensor       ):
           _lowerCAmelCase						=		img.numpy().astype('np.uint8'       )
         if isinstance(UpperCAmelCase__  ,      UpperCAmelCase__       ):
           _lowerCAmelCase						=		img_tensorize(UpperCAmelCase__       )
         assert isinstance(UpperCAmelCase__  ,      np.ndarray       )

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

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



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			Tuple=None       )					->   Optional[Any]:
         if color is None:
           _lowerCAmelCase						=		self.edgecolor
         ((_lowerCAmelCase)	,       (_lowerCAmelCase)	,       (_lowerCAmelCase)	,       (_lowerCAmelCase))						=		box
         _lowerCAmelCase						=		xa - xa
         _lowerCAmelCase						=		ya - ya
         self.ax.add_patch(
             mpl.patches.Rectangle(
                 (xa, ya)  ,      UpperCAmelCase__  ,      UpperCAmelCase__  ,      fill=UpperCAmelCase__  ,      edgecolor=UpperCAmelCase__  ,      linewidth=self.font_size // 3  ,      alpha=self.alpha  ,      linestyle=self.linestyle  ,      )       )



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

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Any       )					->   Tuple:
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		box
         _lowerCAmelCase						=		(xa, ya)
         _lowerCAmelCase						=		(ya - ya) * (xa - xa)
         _lowerCAmelCase						=		_SMALL_OBJ * self.scale
         if instance_area < small or ya - ya < 40 * self.scale:
           if ya >= self.height - 5:
             _lowerCAmelCase						=		(xa, ya)
           else:
             _lowerCAmelCase						=		(xa, ya)

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

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			Any="g"  ,      _lowerCAmelCase					:			Optional[int]="left"  ,      )					->   Optional[int]:
         _lowerCAmelCase						=		0
         _lowerCAmelCase						=		self.font_size
         _lowerCAmelCase						=		np.maximum(list(mplc.to_rgb(UpperCAmelCase__       )       )  ,      0.2       )
         _lowerCAmelCase						=		max(0.8  ,      np.max(UpperCAmelCase__       )       )
         _lowerCAmelCase						=		{
             'facecolor': 'black',
             'alpha': self.alpha,
             'pad': self.pad,
             'edgecolor': 'none',
         }
         _lowerCAmelCase	,       _lowerCAmelCase						=		position
         self.ax.text(
             UpperCAmelCase__  ,      UpperCAmelCase__  ,      UpperCAmelCase__  ,      size=font_size * self.scale  ,      family='sans-serif'  ,      bbox=UpperCAmelCase__  ,      verticalalignment='top'  ,      horizontalalignment=UpperCAmelCase__  ,      color=UpperCAmelCase__  ,      zorder=10  ,      rotation=UpperCAmelCase__  ,      )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			str=None       )					->   Optional[Any]:
         if saveas is None:
           _lowerCAmelCase						=		self.saveas
         if saveas.lower().endswith('.jpg'       ) or saveas.lower().endswith('.png'       ):
           cva.imwrite(
               UpperCAmelCase__  ,      self._get_buffer()[:, :, ::-1]  ,      )
         else:
           self.fig.savefig(UpperCAmelCase__       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Any       )					->   List[Any]:
         _lowerCAmelCase						=		[self.idaobj[i] for i in classes]
         _lowerCAmelCase						=		[self.idaattr[i] for i in attr_classes]
         _lowerCAmelCase						=		[
             F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
             for label, score, attr, attr_score in zip(UpperCAmelCase__  ,      UpperCAmelCase__  ,      UpperCAmelCase__  ,      UpperCAmelCase__       )
         ]
         return labels



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			List[Any]  ,      _lowerCAmelCase					:			int       )					->   Tuple:
         _lowerCAmelCase						=		[self.idaobj[i] for i in classes]
         if scores is not None:
           if labels is None:
             _lowerCAmelCase						=		['{:.0f}%'.format(s * 100       ) for s in scores]
           else:
             _lowerCAmelCase						=		['{} {:.0f}%'.format(UpperCAmelCase__  ,      s * 100       ) for li, s in zip(UpperCAmelCase__  ,      UpperCAmelCase__       )]
         return labels



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			Dict=255       )					->   Optional[Any]:
         _lowerCAmelCase						=		np.random.randint(0  ,      len(_COLORS       )       )
         _lowerCAmelCase						=		_COLORS[idx] * maximum
         if not self.rgb:
           _lowerCAmelCase						=		ret[::-1]
         return ret



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         if not self.pynb:
           _lowerCAmelCase	,       _lowerCAmelCase						=		self.canvas.print_to_buffer()
           if (width, height) != (self.width, self.height):
             _lowerCAmelCase						=		cva.resize(self.img  ,      (width, height)       )
           else:
             _lowerCAmelCase						=		self.img
         else:
           _lowerCAmelCase						=		io.BytesIO()  # works for cairo backend
           self.canvas.print_rgba(UpperCAmelCase__       )
           _lowerCAmelCase	,       _lowerCAmelCase						=		self.width, self.height
           _lowerCAmelCase						=		buf.getvalue()
           _lowerCAmelCase						=		self.img

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

         try:
           import numexpr as ne  # fuse them with numexpr

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

         return visualized_image.astype('uint8'       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Any  ,      _lowerCAmelCase					:			Dict       )					->   str:
         assert brightness_factor >= -1.0 and brightness_factor <= 1.0
         _lowerCAmelCase						=		mplc.to_rgb(UpperCAmelCase__       )
         _lowerCAmelCase						=		colorsys.rgb_to_hls(*mplc.to_rgb(UpperCAmelCase__       )       )
         _lowerCAmelCase						=		polygon_color[1] + (brightness_factor * polygon_color[1])
         _lowerCAmelCase						=		0.0 if modified_lightness < 0.0 else modified_lightness
         _lowerCAmelCase						=		1.0 if modified_lightness > 1.0 else modified_lightness
         _lowerCAmelCase						=		colorsys.hls_to_rgb(polygon_color[0]  ,      UpperCAmelCase__  ,      polygon_color[2]       )
         return modified_color



_UpperCamelCase:					List[str]     =(
    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)
)




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase  =	False


@nightly
@require_torch_gpu
class       lowerCamelCase_		(      unittest.TestCase ):





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase__  )

								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					generator.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"






				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					"cyberpunk 2077"
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt=UpperCAmelCase__							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=50							,		output_type="numpy"							,		).images

								SCREAMING_SNAKE_CASE__  				=					image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
