"""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							=1000






class        __UpperCAmelCase						:

				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 		_a     (				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 		_a     (				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 		_a     (				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 < 40 * 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 		_a     (				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=10     ,			rotation=_lowerCamelCase     ,			)

				def 		_a     (				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 		_a     (				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 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =[self.idaobj[i] for i in classes]
										if scores is not None:
																if labels is None:
																						lowerCamelCase__ =["{:.0f}%".format(s * 100				) for s in scores]
																else:
																						lowerCamelCase__ =["{} {:.0f}%".format(_lowerCamelCase     ,			s * 100				) for li, s in zip(_lowerCamelCase     ,			_lowerCamelCase				)]
										return labels

				def 		_a     (				self     ,			_lowerCamelCase=255				):
										lowerCamelCase__ =np.random.randint(0     ,			len(_COLORS				)				)
										lowerCamelCase__ =_COLORS[idx] * maximum
										if not self.rgb:
																lowerCamelCase__ =ret[::-1]
										return ret

				def 		_a     (				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 		_a     (				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





a							=(
    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"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a							={
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

a							=datasets.load_dataset('xnli', split='test+validation')

a							=0
a							=0
a							=0
a							=0





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						if spm_diff == list(reversed(__lowerCAmelCase    )    ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) and fast.decode(__lowerCAmelCase    ) == fast.decode(__lowerCAmelCase    ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						lowerCamelCase__ =slow.encode(slow.decode(__lowerCAmelCase    )    )
						lowerCamelCase__ =fast.encode(fast.decode(__lowerCAmelCase    )    )
						if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
												# Type 3 error.
												# Snehagatha ->
												#       Sne, h, aga, th, a
												#       Sne, ha, gat, ha
												# Encoding the wrong with sp does not even recover what spm gave us
												# It fits tokenizer however...
												return True
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =fast.encode_plus(__lowerCAmelCase    )[0]
						lowerCamelCase__ =enc.offsets
						lowerCamelCase__ ,				lowerCamelCase__ =offsets[idx], offsets[idx - 1]
						if curr is not None and line[curr[0] : curr[1]] == "\u200f":
												return True
						if prev is not None and line[prev[0] : prev[1]] == "\u200f":
												return True





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Tuple:




						'''simple docstring'''



						for i, (spm_id, tok_id) in enumerate(zip(__lowerCAmelCase       ,   __lowerCAmelCase    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowerCAmelCase    )       ,   reversed(__lowerCAmelCase    )    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =len(__lowerCAmelCase    ) - i

						lowerCamelCase__ =spm_ids[first:last]
						lowerCamelCase__ =tok_ids[first:last]

						if check_diff(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if check_LTR_mark(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if last - first > 5:
												# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
												lowerCamelCase__ =Counter(spm_ids[first:last]    )
												lowerCamelCase__ =Counter(tok_ids[first:last]    )

												lowerCamelCase__ ={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0    ) == si}
												lowerCamelCase__ =3
												for i in range(last - first - min_width    ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowerCAmelCase    )    ):
																								lowerCamelCase__ =[
																								    k
																								    for k in range(last - first - min_width    )
																								    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																								]
																								for j in possible_matches:
																														if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ) and check_details(
																														    __lowerCAmelCase       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   ):
																																				return True

						print(F'''Spm: {[fast.decode([spm_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						try:
												print(F'''Tok: {[fast.decode([tok_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						except Exception:
												pass

						lowerCamelCase__ =fast.decode(spm_ids[:first]    )
						lowerCamelCase__ =fast.decode(spm_ids[last:]    )
						lowerCamelCase__ =fast.decode(spm_ids[first:last]    )
						print()
						print(__lowerCAmelCase    )
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						global perfect
						global imperfect
						global wrong
						global total

						lowerCamelCase__ =slow.encode(__lowerCAmelCase    )
						lowerCamelCase__ =fast.encode(__lowerCAmelCase    )

						lowerCamelCase__ =False
						total += 1

						if slow_ids != fast_ids:
												if check_details(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
																		lowerCamelCase__ =True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 1_0000 == 0:
												print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''    )

						if skip_assert:
												return

						assert (
						    slow_ids == fast_ids
						), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowerCAmelCase    )}\n{fast.tokenize(__lowerCAmelCase    )}'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						global batch_total
						for i in range(len(__lowerCAmelCase    )    ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )

												# hypothesis, all languages
												for text in dataset[i]["hypothesis"]["translation"]:
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		a							=list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a							=0
			a							=0
			a							=0
			a							=0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			a							=slow_class.from_pretrained(checkpoint, force_download=True)
			a							=fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')


