
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 						__magic_name__			(   __a      :	str	,		__a      :	str	,		__a      :	Optional[int]	,		__a      :	str						):



     '''simple docstring'''




     if isinstance(__a	,		__a						):
          UpperCamelCase__				=    np.full((len(__a						), sequence_length, 2)	,		__a						)
     else:
          UpperCamelCase__				=    np.full((len(__a						), sequence_length)	,		__a						)

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

     return out_tensor.tolist()






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    ord(__a						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     UpperCamelCase__				=    unicodedata.category(__a						)
     if cat.startswith("""P"""						):
          return True
     return False




@dataclass
class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      42
  SCREAMING_SNAKE_CASE__         =      True
  SCREAMING_SNAKE_CASE__         =      None
  SCREAMING_SNAKE_CASE__         =      None
  SCREAMING_SNAKE_CASE__         =      -100
  SCREAMING_SNAKE_CASE__         =      """pt"""







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       import torch

       UpperCamelCase__				=    """label""" if """label""" in features[0].keys() else """labels"""
       UpperCamelCase__				=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
       UpperCamelCase__				=    self.tokenizer.pad(
           SCREAMING_SNAKE_CASE_	,      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

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

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

       return batch







from PIL import Image






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




     UpperCamelCase__		,	UpperCamelCase__				=    image.size
     UpperCamelCase__				=    0
     UpperCamelCase__				=    image.load()
     for i in range(__a						):
          for j in range(__a						):
               UpperCamelCase__				=    pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(__a						):
          for i in range(__a						):
               UpperCamelCase__				=    255 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
    lowerCamelCase_						     =					mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')





