'''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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    if isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
        __lowerCamelCase          =  np.full((len(UpperCamelCase__    ), sequence_length, 2)    ,  UpperCamelCase__    )
    else:
        __lowerCamelCase          =  np.full((len(UpperCamelCase__    ), sequence_length)    ,  UpperCamelCase__    )

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

    return out_tensor.tolist()






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Dict:
    __lowerCamelCase          =  ord(UpperCamelCase__    )
    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
    __lowerCamelCase          =  unicodedata.category(UpperCamelCase__    )
    if cat.startswith('''P'''    ):
        return True
    return False




@dataclass
class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					PreTrainedTokenizerBase
     lowerCamelCase  :					Union[bool, str, PaddingStrategy]							=True
     lowerCamelCase  :					Optional[int]							=None
     lowerCamelCase  :					Optional[int]							=None
     lowerCamelCase  :					int							=-1_0_0
     lowerCamelCase  :					str							="pt"






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		Tuple   ):





         """simple docstring"""



         import torch

         __lowerCamelCase          =  '''label''' if '''label''' in features[0].keys() else '''labels'''
         __lowerCamelCase          =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
         __lowerCamelCase          =  self.tokenizer.pad(
             a  ,    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

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

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

         return batch

'''simple docstring'''




import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class    a__				:






     def __init__(			self		:		List[Any]  ,    a		:		Tuple  ,    a		:		int  ,    a		:		int   ):





         """simple docstring"""



         if dst_width < 0 or dst_height < 0:
             raise ValueError('''Destination width/height should be > 0'''   )

         __lowerCamelCase          =  img
         __lowerCamelCase          =  img.shape[1]
         __lowerCamelCase          =  img.shape[0]
         __lowerCamelCase          =  dst_width
         __lowerCamelCase          =  dst_height

         __lowerCamelCase          =  self.src_w / self.dst_w
         __lowerCamelCase          =  self.src_h / self.dst_h

         __lowerCamelCase          =  __lowerCamelCase          =  (
             np.ones((self.dst_h, self.dst_w, 3)  ,    np.uinta   ) * 2_55
         )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for i in range(self.dst_h   ):
             for j in range(self.dst_w   ):
                 __lowerCamelCase          =  self.img[self.get_y(a   )][self.get_x(a   )]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int   ):





         """simple docstring"""



         return int(self.ratio_x * x   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		int   ):





         """simple docstring"""



         return int(self.ratio_y * y   )


if __name__ == "__main__":
  __UpperCAmelCase							,	__UpperCAmelCase												=8_0_0, 6_0_0
  __UpperCAmelCase												=imread("image_data/lena.jpg", 1)
  __UpperCAmelCase												=NearestNeighbour(im, dst_w, dst_h)
  n.process()

  imshow(
      f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
  )
  waitKey(0)
  destroyAllWindows()
