




'''simple docstring'''


import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa







class     SCREAMING_SNAKE_CASE						(	snake_case_							):
      __magic_name__     :					Dict             =				['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']






      def __init__(   self      :     List[Any]	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           assert Path(hparams.data_dir					).exists()
           a_    :       Any    	=     Path(hparams.output_dir					)
           self.output_dir.mkdir(exist_ok=lowercase__					)

           a_    :       Dict    	=     self.output_dir.joinpath("""student"""					)

           a_    :       List[Any]    	=     str(lowercase__					)  # Tell lightning we are training the student
           a_    :       Optional[int]    	=     AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher					).eval()
           use_task_specific_params(lowercase__	,	hparams.task					)  # We copy good generation parameters to student by default
           if hparams.student is not None:
                a_    :       List[str]    	=     AutoModelForSeqaSeqLM.from_pretrained(hparams.student					)
                use_task_specific_params(lowercase__	,	hparams.task					)
                a_	,    a_    :       List[str]    	=     None, None
           else:
                a_	,    a_	,    a_    :       str    	=     create_student_by_copying_alternating_layers(
                    lowercase__	,	e=hparams.student_encoder_layers	,	d=hparams.student_decoder_layers	,	save_path=lowercase__					)

           if hparams.length_penalty != -1:
                a_    :       Optional[int]    	=     hparams.length_penalty
           a_    :       str    	=     hparams.teacher  # Use teacher's tokenizer
           super().__init__(lowercase__	,	model=lowercase__	,	config=student.config					)
           assert student.config.model_type == teacher.config.model_type, (
               F"teacher, student model types should be the same, got {student.config.model_type} !="
               F" {teacher.config.model_type}"
           )

           if student.config.model_type == "t5":
                a_    :       Optional[int]    	=     len(student.get_encoder().block					)
                a_    :       Any    	=     len(student.get_decoder().block					)
                a_    :       Dict    	=     len(teacher.get_encoder().block					)
                a_    :       List[Any]    	=     len(teacher.get_decoder().block					)
           else:
                a_    :       List[str]    	=     student.config.encoder_layers
                a_    :       Union[str, Any]    	=     student.config.decoder_layers
                a_    :       Optional[int]    	=     teacher.config.encoder_layers
                a_    :       Optional[int]    	=     teacher.config.decoder_layers

           a_    :       Optional[Any]    	=     not (hparams.student is None or hparams.teacher == hparams.student)
           a_    :       Any    	=     (not self.different_base_models) and hparams.alpha_hid > 0
           a_    :       Optional[int]    	=     self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
           # self.different_encoder determines whether we need to run the teacher encoder
           a_    :       str    	=     teacher
           freeze_params(self.teacher					)

           if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
                try:
                     del self.teacher.model.encoder
                except AttributeError:  # T5
                     del self.teacher.encoder

           if e_layer_ids is None:
                a_    :       Tuple    	=     list(range(lowercase__					)					)
           if d_layer_ids is None:
                a_    :       List[Any]    	=     list(range(lowercase__					)					)

           a_	,    a_    :       int    	=     e_layer_ids, d_layer_ids  # type: List[int], List[int]

           if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
                if hparams.supervise_forward:
                     a_    :       str    	=     get_layers_to_supervise(
                         n_student=len(self.e_layer_ids					)	,	n_teacher=lowercase__					)
                     a_    :       int    	=     get_layers_to_supervise(
                         n_student=len(self.d_layer_ids					)	,	n_teacher=lowercase__					)
                else:  # student layer should emulate hidden states of the teacher layer it was copied from
                     a_    :       int    	=     self.e_layer_ids
                     a_    :       Dict    	=     self.d_layer_ids
           else:
                a_    :       List[Any]    	=     None
                a_    :       str    	=     None

           a_    :       List[str]    	=     nn.KLDivLoss(reduction="""batchmean"""					)
           a_    :       Tuple    	=     2.0
           a_    :       Any    	=     hparams.alpha_mlm
           a_    :       str    	=     hparams.alpha_ce
           a_    :       Dict    	=     hparams.alpha_hid
           gc.collect()
           torch.cuda.empty_cache()






      def        lowercase_	(   self      :     Any	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Optional[int]	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       str    	=     mask[:, :, None].expand_as(lowercase__					)
           a_    :       List[Any]    	=     s_logits.size(-1					)
           a_    :       List[Any]    	=     torch.masked_select(lowercase__	,	lowercase__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
           a_    :       List[Any]    	=     torch.masked_select(lowercase__	,	lowercase__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
           a_    :       int    	=     s_logits_slct.view(-1	,	lowercase__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
           a_    :       List[str]    	=     t_logits_slct.view(-1	,	lowercase__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
           assert t_logits_slct.size() == s_logits_slct.size()
           a_    :       Optional[Any]    	=     (
               self.ce_loss_fct(
                   nn.functional.log_softmax(s_logits_slct / self.temperature	,	dim=-1					)	,	nn.functional.softmax(t_logits_slct / self.temperature	,	dim=-1					)	,	)
               * (self.temperature) ** 2
           )
           return loss_ce






      @staticmethod
      def        lowercase_	(   lowercase__      :     Dict	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           SummarizationModule.add_model_specific_args(lowercase__	,	lowercase__					)
           add_distill_args(lowercase__					)
           return parser






      def        lowercase_	(   self      :     Dict	,	lowercase__      :     dict					):


           '''simple docstring'''

           a_    :       int    	=     self.tokenizer.pad_token_id
           a_	,    a_	,    a_    :       Any    	=     batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
           if isinstance(self.model	,	lowercase__					):
                a_    :       List[str]    	=     self.model._shift_right(lowercase__					)
           else:
                a_    :       Any    	=     shift_tokens_right(lowercase__	,	lowercase__					)

           # noinspection PyCallingNonCallable
           a_    :       str    	=     self(
               lowercase__	,	attention_mask=lowercase__	,	decoder_input_ids=lowercase__	,	output_hidden_states=self.do_calc_hidden_loss	,	output_attentions=lowercase__	,	use_cache=lowercase__	,	)
           a_    :       List[str]    	=     student_outputs["""logits"""]

           # Same cross entropy vs. label smoothing logic as finetune.py
           assert lm_logits.shape[-1] == self.model.config.vocab_size
           if self.hparams.label_smoothing == 0:
                # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                a_    :       List[Any]    	=     nn.CrossEntropyLoss(ignore_index=lowercase__					)
                a_    :       Dict    	=     loss_fct(lm_logits.view(-1	,	lm_logits.shape[-1]					)	,	labels.view(-1					)					)
           else:
                a_    :       int    	=     nn.functional.log_softmax(lowercase__	,	dim=-1					)
                a_	,    a_    :       List[str]    	=     label_smoothed_nll_loss(
                    lowercase__	,	lowercase__	,	self.hparams.label_smoothing	,	ignore_index=lowercase__					)

           def zero_tensor():
                return torch.tensor(0.0					).type_as(lowercase__					)

           a_    :       Dict    	=     student_outputs[
               """encoder_last_hidden_state"""
           ]  # use this unless self.different_base_models
           a_	,    a_    :       List[str]    	=     zero_tensor(), zero_tensor()
           if self.different_encoder:  # compute encoder hidden state loss
                a_    :       str    	=     self.teacher.get_encoder()(
                    lowercase__	,	attention_mask=lowercase__	,	output_hidden_states=self.do_calc_hidden_loss	,	)
                if self.different_base_models:
                     a_    :       List[str]    	=     all_teacher_encoder_outputs["""last_hidden_state"""]
                elif self.do_calc_hidden_loss:
                     a_    :       Dict    	=     self.calc_hidden_loss(
                         lowercase__	,	student_outputs["""encoder_hidden_states"""]	,	all_teacher_encoder_outputs["""hidden_states"""]	,	self.e_matches	,	normalize_hidden=self.hparams.normalize_hidden	,	)

           a_    :       List[str]    	=     self.teacher(
               lowercase__	,	attention_mask=lowercase__	,	encoder_outputs=(teacher_enc_outputs,)	,	decoder_input_ids=lowercase__	,	output_hidden_states=self.do_calc_hidden_loss	,	use_cache=lowercase__	,	)
           a_    :       Dict    	=     decoder_input_ids.ne(lowercase__					)
           a_    :       Optional[int]    	=     self.calc_ce_loss(lowercase__	,	lowercase__	,	teacher_outputs["""logits"""]					)
           if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
                a_    :       Any    	=     self.calc_hidden_loss(
                    lowercase__	,	student_outputs["""decoder_hidden_states"""]	,	teacher_outputs["""decoder_hidden_states"""]	,	self.d_matches	,	normalize_hidden=self.hparams.normalize_hidden	,	)

           a_    :       List[Any]    	=     (
               self.alpha_ce * loss_ce
               + self.alpha_mlm * student_lm_loss
               + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
           )
           return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec






      @staticmethod
      def        lowercase_	(   lowercase__      :     Union[str, Any]	,	lowercase__      :     Any	,	lowercase__      :     Tuple	,	lowercase__      :     Any	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       int    	=     """expected list or tuple for hidden_states, got tensor of shape: """
           assert not isinstance(lowercase__	,	torch.Tensor					), F"{msg}{hidden_states.shape}"
           assert not isinstance(lowercase__	,	torch.Tensor					), F"{msg}{hidden_states_T.shape}"
           a_    :       Dict    	=     attention_mask.to(hidden_states[0]					)
           a_    :       Optional[int]    	=     mask.sum() * hidden_states[0].size(-1					)
           a_    :       Tuple    	=     torch.stack([hidden_states[i] for i in range(len(lowercase__					)					)]					)
           a_    :       Dict    	=     torch.stack([hidden_states_T[j] for j in matches]					)
           assert student_states.shape == teacher_states.shape, F"{student_states.shape} != {teacher_states.shape}"
           if normalize_hidden:
                a_    :       Optional[Any]    	=     nn.functional.layer_norm(lowercase__	,	student_states.shape[1:]					)
                a_    :       Tuple    	=     nn.functional.layer_norm(lowercase__	,	teacher_states.shape[1:]					)
           a_    :       Tuple    	=     nn.functional.mse_loss(lowercase__	,	lowercase__	,	reduction="""none"""					)
           a_    :       Tuple    	=     (mse * mask.unsqueeze(0					).unsqueeze(-1					)).sum() / valid_count
           return masked_mse



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict							):




     """simple docstring"""


     parser.add_argument("""--teacher"""   ,						type=UpperCamelCase__							)
     parser.add_argument("""--alpha_ce"""   ,						default=0.8   ,						type=UpperCamelCase__							)
     parser.add_argument("""--alpha_mlm"""   ,						default=0.2   ,						type=UpperCamelCase__							)
     parser.add_argument("""--alpha_hid"""   ,						default=0.0   ,						type=UpperCamelCase__   ,						required=UpperCamelCase__							)
     parser.add_argument("""--student"""   ,						type=UpperCamelCase__   ,						required=UpperCamelCase__							)
     parser.add_argument("""--student_decoder_layers"""   ,						default=12   ,						type=UpperCamelCase__   ,						required=UpperCamelCase__							)
     parser.add_argument("""--student_encoder_layers"""   ,						default=12   ,						type=UpperCamelCase__   ,						required=UpperCamelCase__							)
     parser.add_argument("""--no_teacher"""   ,						action="""store_true"""   ,						default=UpperCamelCase__							)
     parser.add_argument("""--length_penalty"""   ,						type=UpperCamelCase__   ,						default=-1							)
     parser.add_argument("""--supervise_forward"""   ,						action="""store_true"""   ,						default=UpperCamelCase__							)
     parser.add_argument("""--normalize_hidden"""   ,						action="""store_true"""   ,						default=UpperCamelCase__							)







class     SCREAMING_SNAKE_CASE						(	snake_case_							):
      __magic_name__     :					str             =				'''translation'''
      __magic_name__     :					Union[str, Any]             =				['''bleu''']
      __magic_name__     :					str             =				'''bleu'''






      def __init__(   self      :     List[str]	,	lowercase__      :     Tuple	,	**lowercase__      :     List[str]					):


           '''simple docstring'''

           super().__init__(lowercase__	,	**lowercase__					)
           assert hparams.src_lang is not None
           assert hparams.tgt_lang is not None
           a_    :       List[str]    	=     hparams.src_lang
           a_    :       Dict    	=     hparams.tgt_lang
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,	lowercase__					):
                a_    :       int    	=     self.tokenizer.lang_code_to_id[hparams.tgt_lang]






      def        lowercase_	(   self      :     Union[str, Any]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     int					):


           '''simple docstring'''

           return calculate_bleu(lowercase__	,	lowercase__					)






      @staticmethod
      def        lowercase_	(   lowercase__      :     List[Any]	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           TranslationModule.add_model_specific_args(lowercase__	,	lowercase__					)
           add_distill_args(lowercase__					)
           return parser



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict							):




     """simple docstring"""


     if args.no_teacher:
          a_    :       Optional[int]    	=     TranslationModule if """translation""" in args.task else SummarizationModule
     else:  # DISTILL WITH TEACHER
          a_    :       List[str]    	=     TranslationDistiller if """translation""" in args.task else SummarizationDistiller
     a_    :       str    	=     module_cls.__name__
     print(F"using module {args.setup_cls}"							)
     a_    :       str    	=     module_cls(UpperCamelCase__							)
     return model



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     Path(args.output_dir							).mkdir(exist_ok=UpperCamelCase__							)
     check_output_dir(UpperCamelCase__   ,						expected_items=3							)

     a_    :       str    	=     create_module(UpperCamelCase__							)
     return ft_main(UpperCamelCase__   ,						model=UpperCamelCase__							)


if __name__ == "__main__":
 lowerCAmelCase_			:     List[Any]							    =     argparse.ArgumentParser()
 lowerCAmelCase_			:     int							    =     pl.Trainer.add_argparse_args(parser)
 lowerCAmelCase_			:     Dict							    =     SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
 lowerCAmelCase_			:     List[Any]							    =     parser.parse_args()

 distill_main(args)






'''simple docstring'''


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


lowerCAmelCase_			:     Tuple							    =     1000







class     SCREAMING_SNAKE_CASE						:






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


           '''simple docstring'''

           if isinstance(lowercase__	,	torch.Tensor					):
                a_    :       Union[str, Any]    	=     img.numpy().astype("""np.uint8"""					)
           if isinstance(lowercase__	,	lowercase__					):
                a_    :       List[Any]    	=     img_tensorize(lowercase__					)
           assert isinstance(lowercase__	,	np.ndarray					)

           a_	,    a_    :       int    	=     img.shape[1], img.shape[0]
           a_    :       str    	=     mplfigure.Figure(frameon=lowercase__					)
           a_    :       Optional[int]    	=     fig.get_dpi()
           a_    :       List[Any]    	=     (width * scale + 1e-2) / dpi
           a_    :       Union[str, Any]    	=     (height * scale + 1e-2) / dpi
           fig.set_size_inches(lowercase__	,	lowercase__					)
           a_    :       Optional[Any]    	=     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_    :       Union[str, Any]    	=     saveas
           a_    :       List[Any]    	=     rgb
           a_    :       Optional[int]    	=     pynb
           a_    :       Any    	=     img
           a_    :       List[Any]    	=     edgecolor
           a_    :       str    	=     0.5
           a_    :       Optional[int]    	=     linestyle
           a_    :       int    	=     int(np.sqrt(min(lowercase__	,	lowercase__					)					) * scale // 3					)
           a_    :       Optional[int]    	=     width
           a_    :       Union[str, Any]    	=     height
           a_    :       List[Any]    	=     scale
           a_    :       Any    	=     fig
           a_    :       List[Any]    	=     ax
           a_    :       Union[str, Any]    	=     pad
           a_    :       List[str]    	=     idaobj
           a_    :       List[Any]    	=     idaattr
           a_    :       Union[str, Any]    	=     FigureCanvasAgg(lowercase__					)






      def        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     str=None					):


           '''simple docstring'''

           if color is None:
                a_    :       int    	=     self.edgecolor
           ((a_)	,    (a_)	,    (a_)	,    (a_))    :       Union[str, Any]    	=     box
           a_    :       Union[str, Any]    	=     xa - xa
           a_    :       List[Any]    	=     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        lowercase_	(   self      :     int	,	lowercase__      :     List[Any]	,	lowercase__      :     List[str]=None	,	lowercase__      :     Optional[Any]=None	,	lowercase__      :     List[Any]=None	,	lowercase__      :     Union[str, Any]=None					):


           '''simple docstring'''

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

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






      def        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     Any	,	lowercase__      :     Optional[Any]	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

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

           a_    :       List[Any]    	=     (ya - ya) / np.sqrt(self.height * self.width					)
           a_    :       Optional[int]    	=     self._change_color_brightness(lowercase__	,	brightness_factor=0.7					)
           a_    :       List[str]    	=     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        lowercase_	(   self      :     Any	,	lowercase__      :     Optional[int]	,	lowercase__      :     Tuple	,	lowercase__      :     str="g"	,	lowercase__      :     Optional[Any]="left"	,	):


           '''simple docstring'''

           a_    :       List[Any]    	=     0
           a_    :       Tuple    	=     self.font_size
           a_    :       Tuple    	=     np.maximum(list(mplc.to_rgb(lowercase__					)					)	,	0.2					)
           a_    :       List[str]    	=     max(0.8	,	np.max(lowercase__					)					)
           a_    :       str    	=     {
               """facecolor""": """black""",
               """alpha""": self.alpha,
               """pad""": self.pad,
               """edgecolor""": """none""",
           }
           a_	,    a_    :       Any    	=     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        lowercase_	(   self      :     List[Any]	,	lowercase__      :     Optional[Any]=None					):


           '''simple docstring'''

           if saveas is None:
                a_    :       Dict    	=     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        lowercase_	(   self      :     List[Any]	,	lowercase__      :     int	,	lowercase__      :     Tuple	,	lowercase__      :     Optional[int]	,	lowercase__      :     Tuple					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     [self.idaobj[i] for i in classes]
           a_    :       Any    	=     [self.idaattr[i] for i in attr_classes]
           a_    :       List[str]    	=     [
               F"{label} {score:.2f} {attr} {attr_score:.2f}"
               for label, score, attr, attr_score in zip(lowercase__	,	lowercase__	,	lowercase__	,	lowercase__					)
           ]
           return labels






      def        lowercase_	(   self      :     str	,	lowercase__      :     Optional[Any]	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     Dict	,	lowercase__      :     Union[str, Any]=255					):


           '''simple docstring'''

           a_    :       int    	=     np.random.randint(0	,	len(_COLORS					)					)
           a_    :       Tuple    	=     _COLORS[idx] * maximum
           if not self.rgb:
                a_    :       str    	=     ret[::-1]
           return ret






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

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

           a_    :       Optional[Any]    	=     np.frombuffer(lowercase__	,	dtype="""uint8"""					)
           a_    :       Union[str, Any]    	=     buffer.reshape(lowercase__	,	lowercase__	,	4					)
           a_	,    a_    :       Optional[Any]    	=     np.split(lowercase__	,	[3]	,	axis=2					)

           try:
                import numexpr as ne  # fuse them with numexpr

                a_    :       Dict    	=     ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""					)
           except ImportError:
                a_    :       List[Any]    	=     alpha.astype("""float32"""					) / 255.0
                a_    :       Optional[int]    	=     img * (1 - alpha) + rgb * alpha

           return visualized_image.astype("""uint8"""					)






      def        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Any					):


           '''simple docstring'''

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

lowerCAmelCase_			:     List[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)
)
