





'''simple docstring'''







import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


snake_case_  :							Tuple            =	torch.cuda.is_available()
snake_case_  :							Any            =	[" Sam ate lunch today.", "Sams lunch ingredients."]
snake_case_  :							Tuple            =	["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
snake_case_  :							Optional[int]            =	{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Path,      SCREAMING_SNAKE_CASE__   :      list )			->						Tuple:
						UpperCAmelCase_      :   int      =	'''\n'''.join(SCREAMING_SNAKE_CASE__ )
						Path(SCREAMING_SNAKE_CASE__ ).open('''w''' ).writelines(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						Dict:
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__,      F"""{split}.source""" ),      SCREAMING_SNAKE_CASE__ )
												_dump_articles(os.path.join(SCREAMING_SNAKE_CASE__,      F"""{split}.target""" ),      SCREAMING_SNAKE_CASE__ )
						return tmp_dir


class        __a   (lowerCamelCase  ):

							@classmethod
							def      UpperCAmelCase__						(				cls				:  Union[str, Any] )       ->							Tuple:







													"""simple docstring"""
													return cls

							@require_torch_multi_gpu
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	{
													    '''no_teacher''': True,
													    '''freeze_encoder''': True,
													    '''gpus''': 2,
													    '''overwrite_output_dir''': True,
													    '''sortish_sampler''': True,
													}
													self._test_distiller_cli_fork(__magic_name__							,  check_contents=__magic_name__ )



							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  int=True )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	{
													    '''label_smoothing''': 0.0,
													    '''early_stopping_patience''': -1,
													    '''train_batch_size''': 1,
													    '''eval_batch_size''': 2,
													    '''max_epochs''': 2,
													    '''alpha_mlm''': 0.2,
													    '''alpha_ce''': 0.8,
													    '''do_predict''': True,
													    '''model_name_or_path''': '''sshleifer/tinier_bart''',
													    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
													    '''val_check_interval''': 0.5,
													}
													default_updates.update(__magic_name__ )
													UpperCAmelCase_      :   dict      =	CHEAP_ARGS.copy()
													UpperCAmelCase_      :   List[Any]      =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
													UpperCAmelCase_      :   List[str]      =	self.get_auto_remove_tmp_dir()
													args_d.update(data_dir=__magic_name__							,  output_dir=__magic_name__							,  **__magic_name__ )

													def convert(__magic_name__				:  str							,  __magic_name__				:  Dict ):
																			if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																									return ""
																			if v is False or v is None:
																									return ""
																			if v is True:  # or len(str(v))==0:
																									return F"""--{k}"""
																			return F"""--{k}={v}"""

													UpperCAmelCase_      :   Dict      =	[x for x in (convert(__magic_name__							,  __magic_name__ ) for k, v in args_d.items()) if len(__magic_name__ )]
													UpperCAmelCase_      :   List[Any]      =	[sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
													execute_subprocess_async(__magic_name__							,  env=self.get_env() )

													UpperCAmelCase_      :   int      =	os.listdir(__magic_name__ )
													UpperCAmelCase_      :   Optional[Any]      =	{os.path.basename(__magic_name__ ) for p in contents}
													UpperCAmelCase_      :   Dict      =	[p for p in contents if p.endswith('''ckpt''' )]
													assert len(__magic_name__ ) > 0

													self.assertIn('''test_generations.txt'''							,  __magic_name__ )
													self.assertIn('''test_results.txt'''							,  __magic_name__ )

													# get the following from the module, (we don't have access to `model` here)
													UpperCAmelCase_      :   str      =	os.path.join(__magic_name__							,  '''metrics.json''' )
													UpperCAmelCase_      :   List[str]      =	'''rouge2'''

													UpperCAmelCase_      :   int      =	load_json(__magic_name__ )
													# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
													print(__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	metrics['''val'''][-1]
													self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']							,  0.0_1 )
													self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]							,  __magic_name__ )
													self.assertEqual(len(metrics['''test'''] )							,  1 )
													UpperCAmelCase_      :   Union[str, Any]      =	int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1 )
													self.assertEqual(len(metrics['''val'''] )							,  __magic_name__ )









"""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


a_											=     10_00
class snake_case   :







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





						'''simple docstring'''







						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   :							List[Any]     , a__   :							Union[str, Any]     , a__   :							Tuple=None		)							->    Tuple:





						'''simple docstring'''







						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   :							Union[str, Any]     , a__   :							Dict     , a__   :							List[Any]=None     , a__   :							int=None     , a__   :							str=None     , a__   :							Optional[int]=None		)							->    List[str]:





						'''simple docstring'''







						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   :							List[str]     , a__   :							Any     , a__   :							List[str]     , a__   :							Tuple		)							->    int:





						'''simple docstring'''







						_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 - 0.0_2) / 0.0_8 + 1     , 1.2     , 2		)
						font_size *= 0.7_5 * self.font_size

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







		def 			a_			(       self   :							str     , a__   :							Optional[int]     , a__   :							Optional[int]     , a__   :							Any="g"     , a__   :							Optional[int]="left"     , )							->    Dict:





						'''simple docstring'''







						_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   :							List[str]     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						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   :							Union[str, Any]     , a__   :							Optional[Any]     , a__   :							List[Any]     , a__   :							Tuple     , a__   :							List[str]		)							->    Optional[int]:





						'''simple docstring'''







						_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   :							Any     , a__   :							Dict     , a__   :							Tuple		)							->    Dict:





						'''simple docstring'''







						_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(a__     , s * 1_00		) for li, s in zip(a__     , a__		)]
						return labels







		def 			a_			(       self   :							List[Any]     , a__   :							Optional[Any]=2_55		)							->    str:





						'''simple docstring'''







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







		def 			a_			(       self   :							int		)							->    Dict:





						'''simple docstring'''







						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"		) / 2_5_5.0
										_A       	=      img * (1 - alpha) + rgb * alpha

						return visualized_image.astype("uint8"		)







		def 			a_			(       self   :							Optional[int]     , a__   :							str     , a__   :							Union[str, Any]		)							->    str:





						'''simple docstring'''







						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





a_											=     (
    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,
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    )
    .astype(np.floataa)
    .reshape(-1, 3)
)