



"""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


__SCREAMING_SNAKE_CASE     :  List[Any]										=					1_0_0_0


class 							lowerCamelCase_:





					'''simple docstring'''



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

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

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

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

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

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_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 < 4_0 * 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(lowerCamelCase__ ,				brightness_factor=0.7      )
									_lowerCamelCase					     = 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=lowerCamelCase__ ,				position=lowerCamelCase__ ,				color=lowerCamelCase__ ,				)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__="g" ,				lowerCamelCase__="left" ,				):
									_lowerCamelCase					     = 0
									_lowerCamelCase					     = self.font_size
									_lowerCamelCase					     = np.maximum(list(mplc.to_rgb(lowerCamelCase__      )      ) ,				0.2      )
									_lowerCamelCase					     = max(0.8 ,				np.max(lowerCamelCase__      )      )
									_lowerCamelCase					     = {
									    '''facecolor''': '''black''',
									    '''alpha''': self.alpha,
									    '''pad''': self.pad,
									    '''edgecolor''': '''none''',
									}
									_lowerCamelCase     ,   _lowerCamelCase					     = position
									self.ax.text(
									    lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				size=font_size * self.scale ,				family='''sans-serif''' ,				bbox=lowerCamelCase__ ,				verticalalignment='''top''' ,				horizontalalignment=lowerCamelCase__ ,				color=lowerCamelCase__ ,				zorder=1_0 ,				rotation=lowerCamelCase__ ,				)

					def 	snake_case__					(			self ,				lowerCamelCase__=None      ):
									if saveas is None:
													_lowerCamelCase					     = self.saveas
									if saveas.lower().endswith('''.jpg'''      ) or saveas.lower().endswith('''.png'''      ):
													cva.imwrite(
													    lowerCamelCase__ ,				self._get_buffer()[:, :, ::-1] ,				)
									else:
													self.fig.savefig(lowerCamelCase__      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_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(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )
									]
									return labels

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = [self.idaobj[i] for i in classes]
									if scores is not None:
													if labels is None:
																	_lowerCamelCase					     = ['''{:.0f}%'''.format(s * 1_0_0      ) for s in scores]
													else:
																	_lowerCamelCase					     = ['''{} {:.0f}%'''.format(lowerCamelCase__ ,				s * 1_0_0      ) for li, s in zip(lowerCamelCase__ ,				lowerCamelCase__      )]
									return labels

					def 	snake_case__					(			self ,				lowerCamelCase__=2_5_5      ):
									_lowerCamelCase					     = np.random.randint(0 ,				len(_COLORS      )      )
									_lowerCamelCase					     = _COLORS[idx] * maximum
									if not self.rgb:
													_lowerCamelCase					     = ret[::-1]
									return ret

					def 	snake_case__					(			self      ):
									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(lowerCamelCase__      )
													_lowerCamelCase     ,   _lowerCamelCase					     = self.width, self.height
													_lowerCamelCase					     = buf.getvalue()
													_lowerCamelCase					     = self.img

									_lowerCamelCase					     = np.frombuffer(lowerCamelCase__ ,				dtype='''uint8'''      )
									_lowerCamelCase					     = buffer.reshape(lowerCamelCase__ ,				lowerCamelCase__ ,				4      )
									_lowerCamelCase     ,   _lowerCamelCase					     = np.split(lowerCamelCase__ ,				[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'''      ) / 2_5_5.0
													_lowerCamelCase					     = img * (1 - alpha) + rgb * alpha

									return visualized_image.astype('''uint8'''      )





					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									assert brightness_factor >= -1.0 and brightness_factor <= 1.0
									_lowerCamelCase					     = mplc.to_rgb(lowerCamelCase__      )
									_lowerCamelCase					     = colorsys.rgb_to_hls(*mplc.to_rgb(lowerCamelCase__      )      )
									_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] ,				lowerCamelCase__ ,				polygon_color[2]      )
									return modified_color
__SCREAMING_SNAKE_CASE     :  int										=					(
    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 argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__SCREAMING_SNAKE_CASE     :  Dict										=					logging.getLogger(__name__)



def 				lowerCAmelCase_(      )		->				Optional[int]:
				_lowerCamelCase					     = argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
				parser.add_argument('''--model_name'''  ,      type=lowercase_  ,      default='''transfo-xl-wt103'''  ,      help='''pretrained model name'''							)
				parser.add_argument(
				    '''--split'''  ,      type=lowercase_  ,      default='''test'''  ,      choices=['''all''', '''valid''', '''test''']  ,      help='''which split to evaluate'''							)
				parser.add_argument('''--batch_size'''  ,      type=lowercase_  ,      default=10  ,      help='''batch size'''							)
				parser.add_argument('''--tgt_len'''  ,      type=lowercase_  ,      default=1_28  ,      help='''number of tokens to predict'''							)
				parser.add_argument('''--ext_len'''  ,      type=lowercase_  ,      default=0  ,      help='''length of the extended context'''							)
				parser.add_argument('''--mem_len'''  ,      type=lowercase_  ,      default=16_00  ,      help='''length of the retained previous heads'''							)
				parser.add_argument('''--clamp_len'''  ,      type=lowercase_  ,      default=10_00  ,      help='''max positional embedding index'''							)
				parser.add_argument('''--no_cuda'''  ,      action='''store_true'''  ,      help='''Do not use CUDA even though CUA is available'''							)
				parser.add_argument('''--work_dir'''  ,      type=lowercase_  ,      required=lowercase_  ,      help='''path to the work_dir'''							)
				parser.add_argument('''--no_log'''  ,      action='''store_true'''  ,      help='''do not log the eval result'''							)
				parser.add_argument('''--same_length'''  ,      action='''store_true'''  ,      help='''set same length attention with masking'''							)
				parser.add_argument('''--server_ip'''  ,      type=lowercase_  ,      default=''''''  ,      help='''Can be used for distant debugging.'''							)
				parser.add_argument('''--server_port'''  ,      type=lowercase_  ,      default=''''''  ,      help='''Can be used for distant debugging.'''							)
				_lowerCamelCase					     = parser.parse_args()
				assert args.ext_len >= 0, "extended context length must be non-negative"

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('''Waiting for debugger attach'''							)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port)  ,      redirect_output=lowercase_							)
								ptvsd.wait_for_attach()

				_lowerCamelCase					     = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
				logger.info('''device: {}'''.format(lowercase_							)							)

				# Load a pre-processed dataset
				# You can also build the corpus yourself using TransfoXLCorpus methods
				# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
				# and tokenizing the dataset
				# The pre-processed corpus is a convertion (using the conversion script )
				_lowerCamelCase					     = TransfoXLCorpus.from_pretrained(args.model_name							)

				_lowerCamelCase					     = corpus.get_iterator('''valid'''  ,      args.batch_size  ,      args.tgt_len  ,      device=lowercase_  ,      ext_len=args.ext_len							)
				_lowerCamelCase					     = corpus.get_iterator('''test'''  ,      args.batch_size  ,      args.tgt_len  ,      device=lowercase_  ,      ext_len=args.ext_len							)

				# Load a pre-trained model
				_lowerCamelCase					     = TransfoXLLMHeadModel.from_pretrained(args.model_name							)
				model.to(lowercase_							)

				logger.info(
				    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
				        args.batch_size  ,      args.tgt_len  ,      args.ext_len  ,      args.mem_len  ,      args.clamp_len							)							)

				model.reset_memory_length(args.mem_len							)
				if args.clamp_len > 0:
								_lowerCamelCase					     = args.clamp_len
				if args.same_length:
								_lowerCamelCase					     = True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(lowercase_   :							Any							):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								_lowerCamelCase     ,   _lowerCamelCase					     = 0, 0.0
								_lowerCamelCase					     = time.time()
								with torch.no_grad():
												_lowerCamelCase					     = None
												for idx, (data, target, seq_len) in enumerate(lowercase_							):
																_lowerCamelCase					     = model(lowercase_  ,      lm_labels=lowercase_  ,      mems=lowercase_							)
																_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = ret
																_lowerCamelCase					     = loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												_lowerCamelCase					     = time.time() - start_time
								logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(lowercase_  ,      10_00 * total_time / (idx + 1)							)							)
								return total_loss / total_len

				# Run on test data.
				if args.split == "all":
								_lowerCamelCase					     = evaluate(lowercase_							)
								_lowerCamelCase					     = evaluate(lowercase_							)
				elif args.split == "valid":
								_lowerCamelCase					     = evaluate(lowercase_							)
								_lowerCamelCase					     = None
				elif args.split == "test":
								_lowerCamelCase					     = evaluate(lowercase_							)
								_lowerCamelCase					     = None

				def format_log(lowercase_   :							Optional[Any]  ,      lowercase_   :							Optional[Any]							):
								_lowerCamelCase					     = '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(lowercase_  ,      lowercase_  ,      math.exp(lowercase_							)							)
								return log_str

				_lowerCamelCase					     = ''''''
				if valid_loss is not None:
								log_str += format_log(lowercase_  ,      '''valid'''							)
				if test_loss is not None:
								log_str += format_log(lowercase_  ,      '''test'''							)

				logger.info('''=''' * 1_00							)
				logger.info(lowercase_							)
				logger.info('''=''' * 1_00							)


if __name__ == "__main__":
			main()
