


"""simple docstring"""


import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_											=     logging.get_logger(__name__)

a_											=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

a_											=     {
    "vocab_file": {
        "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
    },
    "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}

a_											=     {
    "microsoft/biogpt": 10_24,
}






def   a__			(					__lowercase )						->						Union[str, Any]:
				_A       	=      set()
				_A       	=      word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								_A       	=      char
				return pairs




class snake_case   (	_UpperCamelCase):
		__UpperCamelCase							     =      VOCAB_FILES_NAMES
		__UpperCamelCase							     =      PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							     =      ['input_ids', 'attention_mask']







		def __init__(       self   :							Dict     , a__   :							Any     , a__   :							Optional[int]     , a__   :							int="<unk>"     , a__   :							Dict="<s>"     , a__   :							Dict="</s>"     , a__   :							List[Any]="</s>"     , a__   :							List[str]="<pad>"     , **a__   :							Optional[Any]     , )							->    int:





						'''simple docstring'''







						super().__init__(
						    bos_token=a__     , eos_token=a__     , sep_token=a__     , unk_token=a__     , pad_token=a__     , **a__     , )

						try:
										import sacremoses
						except ImportError:
										raise ImportError(
										    "You need to install sacremoses to use BioGptTokenizer. "
										    "See https://pypi.org/project/sacremoses/ for installation."		)

						_A       	=      "en"
						_A       	=      sacremoses
						# cache of sm.MosesTokenizer instance
						_A       	=      {}
						_A       	=      {}
						with open(a__     , encoding="utf-8"		) as vocab_handle:
										_A       	=      json.load(a__		)
						_A       	=      {v: k for k, v in self.encoder.items()}
						with open(a__     , encoding="utf-8"		) as merges_handle:
										_A       	=      merges_handle.read().split("\n"		)[:-1]
						_A       	=      [tuple(merge.split()[:2]		) for merge in merges]
						_A       	=      dict(zip(a__     , range(len(a__		)		)		)		)
						_A       	=      {}







		@property
		def 			a_			(       self   :							Optional[Any]		)							->    Optional[int]:





						'''simple docstring'''







						return len(self.encoder		)







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





						'''simple docstring'''







						return dict(self.encoder     , **self.added_tokens_encoder		)







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





						'''simple docstring'''







						if lang not in self.cache_moses_tokenizer:
										_A       	=      self.sm.MosesTokenizer(lang=a__		)
										_A       	=      moses_tokenizer
						return self.cache_moses_tokenizer[lang].tokenize(
						    a__     , aggressive_dash_splits=a__     , return_str=a__     , escape=a__		)







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





						'''simple docstring'''







						if lang not in self.cache_moses_detokenizer:
										_A       	=      self.sm.MosesDetokenizer(lang=a__		)
										_A       	=      moses_detokenizer
						return self.cache_moses_detokenizer[lang].detokenize(a__		)







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





						'''simple docstring'''







						_A       	=      tuple(token[:-1]		) + (token[-1] + "</w>",)
						if token in self.cache:
										return self.cache[token]
						_A       	=      get_pairs(a__		)

						if not pairs:
										return token + "</w>"

						while True:
										_A       	=      min(a__     , key=lambda a__		: self.bpe_ranks.get(a__     , float("inf"		)		)		)
										if bigram not in self.bpe_ranks:
														break
										_A	,		_A       	=      bigram
										_A       	=      []
										_A       	=      0
										while i < len(a__		):
														try:
																		_A       	=      word.index(a__     , a__		)
														except ValueError:
																		new_word.extend(word[i:]		)
																		break
														else:
																		new_word.extend(word[i:j]		)
																		_A       	=      j

														if word[i] == first and i < len(a__		) - 1 and word[i + 1] == second:
																		new_word.append(first + second		)
																		i += 2
														else:
																		new_word.append(word[i]		)
																		i += 1
										_A       	=      tuple(a__		)
										_A       	=      new_word
										if len(a__		) == 1:
														break
										else:
														_A       	=      get_pairs(a__		)
						_A       	=      " ".join(a__		)
						if word == "\n  </w>":
										_A       	=      "\n</w>"
						_A       	=      word
						return word







		def 			a_			(       self   :							Optional[Any]     , a__   :							Optional[Any]     , a__   :							Dict=False		)							->    str:





						'''simple docstring'''







						if bypass_tokenizer:
										_A       	=      text.split()
						else:
										_A       	=      self.moses_tokenize(a__     , self.lang		)

						_A       	=      []
						for token in text:
										if token:
														split_tokens.extend(list(self.bpe(a__		).split(" "		)		)		)

						return split_tokens







		def 			a_			(       self   :							Any     , a__   :							Tuple		)							->    Optional[Any]:





						'''simple docstring'''







						return self.encoder.get(a__     , self.encoder.get(self.unk_token		)		)







		def 			a_			(       self   :							Tuple     , a__   :							int		)							->    Tuple:





						'''simple docstring'''







						return self.decoder.get(a__     , self.unk_token		)







		def 			a_			(       self   :							Tuple     , a__   :							int		)							->    int:





						'''simple docstring'''







						_A       	=      [t.replace(" "     , ""		).replace("</w>"     , " "		) for t in tokens]
						_A       	=      "".join(a__		).split()
						# detokenize
						_A       	=      self.moses_detokenize(a__     , self.lang		)
						return text







		def 			a_			(       self   :							Optional[Any]     , a__   :							List[int]     , a__   :							Optional[List[int]] = None		)							->    List[int]:





						'''simple docstring'''







						if token_ids_a is None:
										return [self.sep_token_id] + token_ids_a
						_A       	=      [self.sep_token_id]
						return sep + token_ids_a + sep + token_ids_a







		def 			a_			(       self   :							Dict     , a__   :							List[int]     , a__   :							Optional[List[int]] = None     , a__   :							bool = False		)							->    List[int]:





						'''simple docstring'''







						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=a__     , token_ids_a=a__     , already_has_special_tokens=a__		)
						# no bos used in fairseq
						if token_ids_a is not None:
										return [1] + ([0] * len(a__		)) + [1] + ([0] * len(a__		))
						return [1] + ([0] * len(a__		))







		def 			a_			(       self   :							Dict     , a__   :							List[int]     , a__   :							Optional[List[int]] = None		)							->    List[int]:





						'''simple docstring'''







						_A       	=      [self.sep_token_id]

						# no bos used in fairseq
						if token_ids_a is None:
										return len(token_ids_a + sep		) * [0]
						return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







		def 			a_			(       self   :							int     , a__   :							str     , a__   :							Optional[str] = None		)							->    Tuple[str]:





						'''simple docstring'''







						if not os.path.isdir(a__		):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
										return
						_A       	=      os.path.join(
						    a__     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
						_A       	=      os.path.join(
						    a__     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

						with open(a__     , "w"     , encoding="utf-8"		) as f:
										f.write(json.dumps(self.encoder     , indent=2     , sort_keys=a__     , ensure_ascii=a__		) + "\n"		)

						_A       	=      0
						with open(a__     , "w"     , encoding="utf-8"		) as writer:
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     , key=lambda a__		: kv[1]		):
														if index != token_index:
																		logger.warning(
																		    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																		    " Please check that the tokenizer is not corrupted!"		)
																		_A       	=      token_index
														writer.write(" ".join(a__		) + "\n"		)
														index += 1

						return vocab_file, merge_file







		def __getstate__(       self   :							Optional[int]		)							->    List[Any]:





						'''simple docstring'''







						_A       	=      self.__dict__.copy()
						_A       	=      None
						return state







		def __setstate__(       self   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						_A       	=      d

						try:
										import sacremoses
						except ImportError:
										raise ImportError(
										    "You need to install sacremoses to use XLMTokenizer. "
										    "See https://pypi.org/project/sacremoses/ for installation."		)

						_A       	=      sacremoses



"""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,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)