
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     :				Optional[int]             =       10_00


class 						__magic_name__				:





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

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





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[Any]=None			):
							if color is None:
												lowerCAmelCase						:   List[str]							=       self.edgecolor
							((lowerCAmelCase)		,							(lowerCAmelCase)		,							(lowerCAmelCase)		,							(lowerCAmelCase))						:   Any							=       box
							lowerCAmelCase						:   Optional[int]							=       xa - xa
							lowerCAmelCase						:   Tuple							=       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  _A							(							self      :		str  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Union[str, Any]=None  ,					lowerCamelCase__      :		Any=None  ,					lowerCamelCase__      :		int=None  ,					lowerCamelCase__      :		str=None			):
							if len(boxes.shape			) > 2:
												lowerCAmelCase						:   str							=       boxes[0]
							if len(obj_ids.shape			) > 1:
												lowerCAmelCase						:   List[str]							=       obj_ids[0]
							if len(obj_scores.shape			) > 1:
												lowerCAmelCase						:   Union[str, Any]							=       obj_scores[0]
							if len(attr_ids.shape			) > 1:
												lowerCAmelCase						:   Optional[Any]							=       attr_ids[0]
							if len(attr_scores.shape			) > 1:
												lowerCAmelCase						:   int							=       attr_scores[0]
							if isinstance(lowerCamelCase__  ,					torch.Tensor			):
												lowerCAmelCase						:   Any							=       boxes.numpy()
							if isinstance(lowerCamelCase__  ,					lowerCamelCase__			):
												lowerCAmelCase						:   Optional[Any]							=       np.array(lowerCamelCase__			)
							assert isinstance(lowerCamelCase__  ,					np.ndarray			)
							lowerCAmelCase						:   int							=       np.prod(boxes[:, 2:] - boxes[:, :2]  ,					axis=1			)
							lowerCAmelCase						:   Optional[Any]							=       np.argsort(-areas			).tolist()
							lowerCAmelCase						:   Optional[Any]							=       boxes[sorted_idxs] if boxes is not None else None
							lowerCAmelCase						:   str							=       obj_ids[sorted_idxs] if obj_ids is not None else None
							lowerCAmelCase						:   List[Any]							=       obj_scores[sorted_idxs] if obj_scores is not None else None
							lowerCAmelCase						:   List[Any]							=       attr_ids[sorted_idxs] if attr_ids is not None else None
							lowerCAmelCase						:   Union[str, Any]							=       attr_scores[sorted_idxs] if attr_scores is not None else None

							lowerCAmelCase						:   str							=       [self._random_color(maximum=1			) for _ in range(len(lowerCamelCase__			)			)]
							lowerCAmelCase						:   Any							=       [assigned_colors[idx] for idx in sorted_idxs]
							if obj_ids is not None:
												lowerCAmelCase						:   str							=       self._create_text_labels_attr(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)
												for i in range(len(lowerCamelCase__			)			):
																	lowerCAmelCase						:   Any							=       assigned_colors[i]
																	self.add_box(boxes[i]  ,					lowerCamelCase__			)
																	self.draw_labels(labels[i]  ,					boxes[i]  ,					lowerCamelCase__			)





		def  _A							(							self      :		Optional[Any]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		str			):
							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   List[str]							=       box
							lowerCAmelCase						:   Optional[Any]							=       (xa, ya)
							lowerCAmelCase						:   Union[str, Any]							=       (ya - ya) * (xa - xa)
							lowerCAmelCase						:   Optional[int]							=       _SMALL_OBJ * self.scale
							if instance_area < small or ya - ya < 4_0 * self.scale:
												if ya >= self.height - 5:
																	lowerCAmelCase						:   Dict							=       (xa, ya)
												else:
																	lowerCAmelCase						:   Optional[Any]							=       (xa, ya)

							lowerCAmelCase						:   List[str]							=       (ya - ya) / np.sqrt(self.height * self.width			)
							lowerCAmelCase						:   Optional[int]							=       self._change_color_brightness(lowerCamelCase__  ,					brightness_factor=0.7			)
							lowerCAmelCase						:   str							=       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  _A							(							self      :		Dict  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		int="g"  ,					lowerCamelCase__      :		List[Any]="left"  ,					):
							lowerCAmelCase						:   int							=       0
							lowerCAmelCase						:   Optional[Any]							=       self.font_size
							lowerCAmelCase						:   Optional[Any]							=       np.maximum(list(mplc.to_rgb(lowerCamelCase__			)			)  ,					0.2			)
							lowerCAmelCase						:   List[str]							=       max(0.8  ,					np.max(lowerCamelCase__			)			)
							lowerCAmelCase						:   Optional[int]							=       {
							    '''facecolor''': '''black''',
							    '''alpha''': self.alpha,
							    '''pad''': self.pad,
							    '''edgecolor''': '''none''',
							}
							lowerCAmelCase		,							lowerCAmelCase						:   str							=       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  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		Dict=None			):
							if saveas is None:
												lowerCAmelCase						:   Optional[Any]							=       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  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Tuple			):
							lowerCAmelCase						:   Optional[Any]							=       [self.idaobj[i] for i in classes]
							lowerCAmelCase						:   List[Any]							=       [self.idaattr[i] for i in attr_classes]
							lowerCAmelCase						:   Optional[int]							=       [
							    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
							    for label, score, attr, attr_score in zip(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)
							]
							return labels





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





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Optional[int]=2_5_5			):
							lowerCAmelCase						:   Dict							=       np.random.randint(0  ,					len(_COLORS			)			)
							lowerCAmelCase						:   Dict							=       _COLORS[idx] * maximum
							if not self.rgb:
												lowerCAmelCase						:   str							=       ret[::-1]
							return ret





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

							lowerCAmelCase						:   Dict							=       np.frombuffer(lowerCamelCase__  ,					dtype='''uint8'''			)
							lowerCAmelCase						:   Optional[int]							=       buffer.reshape(lowerCamelCase__  ,					lowerCamelCase__  ,					4			)
							lowerCAmelCase		,							lowerCAmelCase						:   List[Any]							=       np.split(lowerCamelCase__  ,					[3]  ,					axis=2			)

							try:
												import numexpr as ne  # fuse them with numexpr

												lowerCAmelCase						:   Union[str, Any]							=       ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''			)
							except ImportError:
												lowerCAmelCase						:   Any							=       alpha.astype('''float32'''			) / 2_5_5.0
												lowerCAmelCase						:   List[Any]							=       img * (1 - alpha) + rgb * alpha

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





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



__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
