






import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__UpperCamelCase   :						Any							=  1000


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

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

											snake_case__													=       saveas
											snake_case__													=       rgb
											snake_case__													=       pynb
											snake_case__													=       img
											snake_case__													=       edgecolor
											snake_case__													=       0.5
											snake_case__													=       linestyle
											snake_case__													=       int(np.sqrt(min(UpperCamelCase    ,		UpperCamelCase  )  ) * scale // 3  )
											snake_case__													=       width
											snake_case__													=       height
											snake_case__													=       scale
											snake_case__													=       fig
											snake_case__													=       ax
											snake_case__													=       pad
											snake_case__													=       idaobj
											snake_case__													=       idaattr
											snake_case__													=       FigureCanvasAgg(UpperCamelCase  )
							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: int=None  )						->						Dict:
											if color is None:
															snake_case__													=       self.edgecolor
											((snake_case__)							,  (snake_case__)							,  (snake_case__)							,  (snake_case__))													=       box
											snake_case__													=       xa - xa
											snake_case__													=       ya - ya
											self.ax.add_patch(
											    mpl.patches.Rectangle(
											        (xa, ya)    ,		UpperCamelCase    ,		UpperCamelCase    ,		fill=UpperCamelCase    ,		edgecolor=UpperCamelCase    ,		linewidth=self.font_size // 3    ,		alpha=self.alpha    ,		linestyle=self.linestyle    ,		)  )
							def lowerCAmelCase_	(       self: List[Any]    ,		UpperCamelCase: str    ,		UpperCamelCase: List[Any]=None    ,		UpperCamelCase: List[str]=None    ,		UpperCamelCase: Union[str, Any]=None    ,		UpperCamelCase: Union[str, Any]=None  )						->						Tuple:
											if len(boxes.shape  ) > 2:
															snake_case__													=       boxes[0]
											if len(obj_ids.shape  ) > 1:
															snake_case__													=       obj_ids[0]
											if len(obj_scores.shape  ) > 1:
															snake_case__													=       obj_scores[0]
											if len(attr_ids.shape  ) > 1:
															snake_case__													=       attr_ids[0]
											if len(attr_scores.shape  ) > 1:
															snake_case__													=       attr_scores[0]
											if isinstance(UpperCamelCase    ,		torch.Tensor  ):
															snake_case__													=       boxes.numpy()
											if isinstance(UpperCamelCase    ,		UpperCamelCase  ):
															snake_case__													=       np.array(UpperCamelCase  )
											assert isinstance(UpperCamelCase    ,		np.ndarray  )
											snake_case__													=       np.prod(boxes[:, 2:] - boxes[:, :2]    ,		axis=1  )
											snake_case__													=       np.argsort(-areas  ).tolist()
											snake_case__													=       boxes[sorted_idxs] if boxes is not None else None
											snake_case__													=       obj_ids[sorted_idxs] if obj_ids is not None else None
											snake_case__													=       obj_scores[sorted_idxs] if obj_scores is not None else None
											snake_case__													=       attr_ids[sorted_idxs] if attr_ids is not None else None
											snake_case__													=       attr_scores[sorted_idxs] if attr_scores is not None else None

											snake_case__													=       [self._random_color(maximum=1  ) for _ in range(len(UpperCamelCase  )  )]
											snake_case__													=       [assigned_colors[idx] for idx in sorted_idxs]
											if obj_ids is not None:
															snake_case__													=       self._create_text_labels_attr(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase  )
															for i in range(len(UpperCamelCase  )  ):
																			snake_case__													=       assigned_colors[i]
																			self.add_box(boxes[i]    ,		UpperCamelCase  )
																			self.draw_labels(labels[i]    ,		boxes[i]    ,		UpperCamelCase  )
							def lowerCAmelCase_	(       self: int    ,		UpperCamelCase: Optional[int]    ,		UpperCamelCase: str    ,		UpperCamelCase: int  )						->						int:
											snake_case__							,  snake_case__							,  snake_case__							,  snake_case__													=       box
											snake_case__													=       (xa, ya)
											snake_case__													=       (ya - ya) * (xa - xa)
											snake_case__													=       _SMALL_OBJ * self.scale
											if instance_area < small or ya - ya < 40 * self.scale:
															if ya >= self.height - 5:
																			snake_case__													=       (xa, ya)
															else:
																			snake_case__													=       (xa, ya)

											snake_case__													=       (ya - ya) / np.sqrt(self.height * self.width  )
											snake_case__													=       self._change_color_brightness(UpperCamelCase    ,		brightness_factor=0.7  )
											snake_case__													=       np.clip((height_ratio - 0.02) / 0.08 + 1    ,		1.2    ,		2  )
											font_size *= 0.75 * self.font_size

											self.draw_text(
											    text=UpperCamelCase    ,		position=UpperCamelCase    ,		color=UpperCamelCase    ,		)
							def lowerCAmelCase_	(       self: Any    ,		UpperCamelCase: Optional[int]    ,		UpperCamelCase: Dict    ,		UpperCamelCase: str="g"    ,		UpperCamelCase: int="left"    ,		)						->						Any:
											snake_case__													=       0
											snake_case__													=       self.font_size
											snake_case__													=       np.maximum(list(mplc.to_rgb(UpperCamelCase  )  )    ,		0.2  )
											snake_case__													=       max(0.8    ,		np.max(UpperCamelCase  )  )
											snake_case__													=       {
											    'facecolor': 'black',
											    'alpha': self.alpha,
											    'pad': self.pad,
											    'edgecolor': 'none',
											}
											snake_case__							,  snake_case__													=       position
											self.ax.text(
											    UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		size=font_size * self.scale    ,		family='sans-serif'    ,		bbox=UpperCamelCase    ,		verticalalignment='top'    ,		horizontalalignment=UpperCamelCase    ,		color=UpperCamelCase    ,		zorder=10    ,		rotation=UpperCamelCase    ,		)
							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: int=None  )						->						List[str]:
											if saveas is None:
															snake_case__													=       self.saveas
											if saveas.lower().endswith('.jpg'  ) or saveas.lower().endswith('.png'  ):
															cva.imwrite(
															    UpperCamelCase    ,		self._get_buffer()[:, :, ::-1]    ,		)
											else:
															self.fig.savefig(UpperCamelCase  )
							def lowerCAmelCase_	(       self: Any    ,		UpperCamelCase: List[Any]    ,		UpperCamelCase: Any    ,		UpperCamelCase: Dict    ,		UpperCamelCase: Union[str, Any]  )						->						int:
											snake_case__													=       [self.idaobj[i] for i in classes]
											snake_case__													=       [self.idaattr[i] for i in attr_classes]
											snake_case__													=       [
											    F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
											    for label, score, attr, attr_score in zip(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase  )
											]
											return labels
							def lowerCAmelCase_	(       self: Dict    ,		UpperCamelCase: str    ,		UpperCamelCase: Optional[int]  )						->						Dict:
											snake_case__													=       [self.idaobj[i] for i in classes]
											if scores is not None:
															if labels is None:
																			snake_case__													=       ['{:.0f}%'.format(s * 1_00  ) for s in scores]
															else:
																			snake_case__													=       ['{} {:.0f}%'.format(UpperCamelCase    ,		s * 1_00  ) for li, s in zip(UpperCamelCase    ,		UpperCamelCase  )]
											return labels
							def lowerCAmelCase_	(       self: Dict    ,		UpperCamelCase: str=2_55  )						->						List[str]:
											snake_case__													=       np.random.randint(0    ,		len(_COLORS  )  )
											snake_case__													=       _COLORS[idx] * maximum
											if not self.rgb:
															snake_case__													=       ret[::-1]
											return ret
							def lowerCAmelCase_	(       self: Optional[Any]  )						->						int:
											if not self.pynb:
															snake_case__							,  snake_case__													=       self.canvas.print_to_buffer()
															if (width, height) != (self.width, self.height):
																			snake_case__													=       cva.resize(self.img    ,		(width, height)  )
															else:
																			snake_case__													=       self.img
											else:
															snake_case__													=       io.BytesIO()  # works for cairo backend
															self.canvas.print_rgba(UpperCamelCase  )
															snake_case__							,  snake_case__													=       self.width, self.height
															snake_case__													=       buf.getvalue()
															snake_case__													=       self.img

											snake_case__													=       np.frombuffer(UpperCamelCase    ,		dtype='uint8'  )
											snake_case__													=       buffer.reshape(UpperCamelCase    ,		UpperCamelCase    ,		4  )
											snake_case__							,  snake_case__													=       np.split(UpperCamelCase    ,		[3]    ,		axis=2  )

											try:
															import numexpr as ne  # fuse them with numexpr

															snake_case__													=       ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'  )
											except ImportError:
															snake_case__													=       alpha.astype('float32'  ) / 255.0
															snake_case__													=       img * (1 - alpha) + rgb * alpha

											return visualized_image.astype('uint8'  )


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


__UpperCamelCase   :						Any							=  (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)












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
)
__UpperCamelCase   :						List[Any]							=  logging.getLogger(__name__)
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
				parser.add_argument('--model_name' ,				type=_A ,				default='transfo-xl-wt103' ,				help='pretrained model name'					)
				parser.add_argument(
				    '--split' ,				type=_A ,				default='test' ,				choices=['all', 'valid', 'test'] ,				help='which split to evaluate'					)
				parser.add_argument('--batch_size' ,				type=_A ,				default=10 ,				help='batch size'					)
				parser.add_argument('--tgt_len' ,				type=_A ,				default=128 ,				help='number of tokens to predict'					)
				parser.add_argument('--ext_len' ,				type=_A ,				default=0 ,				help='length of the extended context'					)
				parser.add_argument('--mem_len' ,				type=_A ,				default=1600 ,				help='length of the retained previous heads'					)
				parser.add_argument('--clamp_len' ,				type=_A ,				default=1000 ,				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=_A ,				required=_A ,				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=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				parser.add_argument('--server_port' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				snake_case__													=       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=_A					)
								ptvsd.wait_for_attach()

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

				# 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 )
				snake_case__													=       TransfoXLCorpus.from_pretrained(args.model_name					)

				snake_case__													=       corpus.get_iterator('valid' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)
				snake_case__													=       corpus.get_iterator('test' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)

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

				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:
								snake_case__													=       args.clamp_len
				if args.same_length:
								snake_case__													=       True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(_A					):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								snake_case__							,  snake_case__													=       0, 0.0
								snake_case__													=       time.time()
								with torch.no_grad():
												snake_case__													=       None
												for idx, (data, target, seq_len) in enumerate(_A					):
																snake_case__													=       model(_A ,				lm_labels=_A ,				mems=_A					)
																snake_case__							,  snake_case__							,  snake_case__													=       ret
																snake_case__													=       loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												snake_case__													=       time.time() - start_time
								logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_A ,				1000 * total_time / (idx + 1)					)					)
								return total_loss / total_len

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

				def format_log(_A ,				_A					):
								snake_case__													=       '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_A ,				_A ,				math.exp(_A					)					)
								return log_str

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

				logger.info('=' * 100					)
				logger.info(_A					)
				logger.info('=' * 100					)


if __name__ == "__main__":
							main()




