


'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

     for i, tensor in enumerate(lowerCAmelCase__						):
          if padding_side == "right":
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]

     return out_tensor.tolist()






def 		_A			(lowerCAmelCase__					:Any						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		4_2
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"""pt"""
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      import torch

      _a      =							'label' if 'label' in features[0].keys() else 'labels'
      _a      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =							self.tokenizer.pad(
          __magic_name__ ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors='pt' if labels is None else None ,			)

      if labels is None:
           return batch

      _a      =							torch.tensor(batch['entity_ids']  ).shape[1]
      _a      =							self.tokenizer.padding_side
      if padding_side == "right":
           _a      =							[
               list(__magic_name__  ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) for label in labels
           ]
      else:
           _a      =							[
               [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) + list(__magic_name__  ) for label in labels
           ]

      _a      =							[feature['ner_tags'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			-1 ,			__magic_name__ ,			__magic_name__  )
      _a      =							[feature['original_entity_spans'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			(-1, -1) ,			__magic_name__ ,			__magic_name__  )
      _a      =							{k: torch.tensor(__magic_name__ ,			dtype=torch.intaa  ) for k, v in batch.items()}

      return batch









'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore






def 		_A			(lowerCAmelCase__					:float			,  lowerCAmelCase__					:float			,  lowerCAmelCase__					:int						)				->					float:




     '''simple docstring'''



     _a      =							x
     _a      =							y
     for step in range(lowerCAmelCase__						):  # noqa: B007
          _a      =							a * a - b * b + x
          _a      =							2 * a * b + y
          _a      =							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)






def 		_A			(lowerCAmelCase__					:float						)				->					tuple:




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_55, 2_55, 2_55)






def 		_A			(lowerCAmelCase__					:float						)				->					tuple:




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_55						) for i in colorsys.hsv_to_rgb(lowerCAmelCase__			,  1			,  1						)						)






def 		_A			(lowerCAmelCase__					:int = 8_00			,  lowerCAmelCase__					:int = 6_00			,  lowerCAmelCase__					:float = -0.6			,  lowerCAmelCase__					:float = 0			,  lowerCAmelCase__					:float = 3.2			,  lowerCAmelCase__					:int = 50			,  lowerCAmelCase__					:bool = True			,  )				->					Image.Image:




     '''simple docstring'''



     _a      =							Image.new('RGB'			,  (image_width, image_height)						)
     _a      =							img.load()

     # loop through the image-coordinates
     for image_x in range(lowerCAmelCase__						):
          for image_y in range(lowerCAmelCase__						):
               # determine the figure-coordinates based on the image-coordinates
               _a      =							figure_width / image_width * image_height
               _a      =							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _a      =							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _a      =							get_distance(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _a      =							get_color_coded_rgb(lowerCAmelCase__						)
               else:
                    _a      =							get_black_and_white_rgb(lowerCAmelCase__						)

     return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a_   :      Optional[Any]				    =      get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

