



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							=       1_000

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=1.2	,	a_="g"	,	a_=0.5	,	a_="-"	,	a_="test_out.jpg"	,	a_=True	,	a_=False	,	a_=None	,	a_=None	,	a_=0.7	,	):
											if isinstance(a_	,	torch.Tensor		):
															a__					   =		img.numpy().astype("""np.uint8"""		)
											if isinstance(a_	,	a_		):
															a__					   =		img_tensorize(a_		)
											assert isinstance(a_	,	np.ndarray		)

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

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


							def _a   (				self	,	a_	,	a_=None		):
											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)	,	a_	,	a_	,	fill=a_	,	edgecolor=a_	,	linewidth=self.font_size // 3	,	alpha=self.alpha	,	linestyle=self.linestyle	,	)		)


							def _a   (				self	,	a_	,	a_=None	,	a_=None	,	a_=None	,	a_=None		):
											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(a_	,	torch.Tensor		):
															a__					   =		boxes.numpy()
											if isinstance(a_	,	a_		):
															a__					   =		np.array(a_		)
											assert isinstance(a_	,	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(a_		)		)]
											a__					   =		[assigned_colors[idx] for idx in sorted_idxs]
											if obj_ids is not None:
															a__					   =		self._create_text_labels_attr(a_	,	a_	,	a_	,	a_		)
															for i in range(len(a_		)		):
																			a__					   =		assigned_colors[i]
																			self.add_box(boxes[i]	,	a_		)
																			self.draw_labels(labels[i]	,	boxes[i]	,	a_		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											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(a_	,	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=a_	,	position=a_	,	color=a_	,	)


							def _a   (				self	,	a_	,	a_	,	a_="g"	,	a_="left"	,	):
											a__					   =		0
											a__					   =		self.font_size
											a__					   =		np.maximum(list(mplc.to_rgb(a_		)		)	,	0.2		)
											a__					   =		max(0.8	,	np.max(a_		)		)
											a__					   =		{
											    """facecolor""": """black""",
											    """alpha""": self.alpha,
											    """pad""": self.pad,
											    """edgecolor""": """none""",
											}
											a__   ,      a__					   =		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 _a   (				self	,	a_=None		):
											if saveas is None:
															a__					   =		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 _a   (				self	,	a_	,	a_	,	a_	,	a_		):
											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(a_	,	a_	,	a_	,	a_		)
											]
											return labels


							def _a   (				self	,	a_	,	a_		):
											a__					   =		[self.idaobj[i] for i in classes]
											if scores is not None:
															if labels is None:
																			a__					   =		["""{:.0f}%""".format(s * 100		) for s in scores]
															else:
																			a__					   =		["""{} {:.0f}%""".format(a_	,	s * 100		) for li, s in zip(a_	,	a_		)]
											return labels


							def _a   (				self	,	a_=255		):
											a__					   =		np.random.randint(0	,	len(_COLORS		)		)
											a__					   =		_COLORS[idx] * maximum
											if not self.rgb:
															a__					   =		ret[::-1]
											return ret


							def _a   (				self		):
											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(a_		)
															a__   ,      a__					   =		self.width, self.height
															a__					   =		buf.getvalue()
															a__					   =		self.img

											a__					   =		np.frombuffer(a_	,	dtype="""uint8"""		)
											a__					   =		buffer.reshape(a_	,	a_	,	4		)
											a__   ,      a__					   =		np.split(a_	,	[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 _a   (				self	,	a_	,	a_		):
											assert brightness_factor >= -1.0 and brightness_factor <= 1.0
											a__					   =		mplc.to_rgb(a_		)
											a__					   =		colorsys.rgb_to_hls(*mplc.to_rgb(a_		)		)
											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]	,	a_	,	polygon_color[2]		)
											return modified_color




UpperCAmelCase							=       (
    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)
)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

