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


SCREAMING_SNAKE_CASE							:List[str]											=       1000





class 							UpperCAmelCase   :




	'''simple docstring'''





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

					__A         =				img.shape[1], img.shape[0]
					__A         =				mplfigure.Figure(frameon=_snake_case				)
					__A         =				fig.get_dpi()
					__A         =				(width * scale + 1E-2) / dpi
					__A         =				(height * scale + 1E-2) / dpi
					fig.set_size_inches(_snake_case							,_snake_case				)
					__A         =				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				)

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






	def        UpperCamelCase_						( self      :				Dict							,A      :				Union[str, Any]							,A      :				int=None				):
					if color is None:
									__A         =				self.edgecolor
					(__A)         =				box
					__A         =				xa - xa
					__A         =				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        UpperCamelCase_						( self      :				Any							,A      :				Optional[Any]							,A      :				List[str]=None							,A      :				Union[str, Any]=None							,A      :				Optional[int]=None							,A      :				List[str]=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(_snake_case							,torch.Tensor				):
									__A         =				boxes.numpy()
					if isinstance(_snake_case							,_snake_case				):
									__A         =				np.array(_snake_case				)
					assert isinstance(_snake_case							,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(_snake_case				)				)]
					__A         =				[assigned_colors[idx] for idx in sorted_idxs]
					if obj_ids is not None:
									__A         =				self._create_text_labels_attr(_snake_case							,_snake_case							,_snake_case							,_snake_case				)
									for i in range(len(_snake_case				)				):
													__A         =				assigned_colors[i]
													self.add_box(boxes[i]							,_snake_case				)
													self.draw_labels(labels[i]							,boxes[i]							,_snake_case				)






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				int							,A      :				int							,A      :				str				):
					__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(_snake_case							,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=_snake_case							,position=_snake_case							,color=_snake_case							,)






	def        UpperCamelCase_						( self      :				Any							,A      :				Union[str, Any]							,A      :				Any							,A      :				Optional[Any]="g"							,A      :				Dict="left"							,):
					__A         =				0
					__A         =				self.font_size
					__A         =				np.maximum(list(mplc.to_rgb(_snake_case				)				)							,0.2				)
					__A         =				max(0.8							,np.max(_snake_case				)				)
					__A         =				{
					    '''facecolor''': '''black''',
					    '''alpha''': self.alpha,
					    '''pad''': self.pad,
					    '''edgecolor''': '''none''',
					}
					__A         =				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        UpperCamelCase_						( self      :				str							,A      :				Optional[int]=None				):
					if saveas is None:
									__A         =				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        UpperCamelCase_						( self      :				Tuple							,A      :				Dict							,A      :				List[str]							,A      :				Dict							,A      :				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(_snake_case							,_snake_case							,_snake_case							,_snake_case				)
					]
					return labels






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Tuple							,A      :				List[Any]				):
					__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(_snake_case							,s * 1_00				) for li, s in zip(_snake_case							,_snake_case				)]
					return labels






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






	def        UpperCamelCase_						( self      :				List[str]				):
					if not self.pynb:
									__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(_snake_case				)
									__A         =				self.width, self.height
									__A         =				buf.getvalue()
									__A         =				self.img

					__A         =				np.frombuffer(_snake_case							,dtype="uint8"				)
					__A         =				buffer.reshape(_snake_case							,_snake_case							,4				)
					__A         =				np.split(_snake_case							,[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"				) / 2_55.0
									__A         =				img * (1 - alpha) + rgb * alpha

					return visualized_image.astype("uint8"				)







	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				str							,A      :				Optional[Any]				):
					assert brightness_factor >= -1.0 and brightness_factor <= 1.0
					__A         =				mplc.to_rgb(_snake_case				)
					__A         =				colorsys.rgb_to_hls(*mplc.to_rgb(_snake_case				)				)
					__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]							,_snake_case							,polygon_color[2]				)
					return modified_color





SCREAMING_SNAKE_CASE							: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 argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
