




'''simple docstring'''


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


_lowerCAmelCase  		=      logging.get_logger(__name__)

_lowerCAmelCase  		=      {'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase  		=      {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}
class   __A (							snake_case_  ):



   """simple docstring"""







   A_												=				VOCAB_FILES_NAMES
   A_												=				PRETRAINED_VOCAB_FILES_MAP
   A_												=				['''input_ids''', '''attention_mask''']
   A_												=				None

   def __init__(      self				, _lowerCamelCase=None				, _lowerCamelCase=None				, _lowerCamelCase=None				, _lowerCamelCase="<unk>"				, _lowerCamelCase="<s>"				, _lowerCamelCase="</s>"				, _lowerCamelCase="<pad>"				, _lowerCamelCase=False				, _lowerCamelCase=False				, **_lowerCamelCase				, )->       int:
      super().__init__(
          __lowercase				, __lowercase				, tokenizer_file=__lowercase				, unk_token=__lowercase				, bos_token=__lowercase				, eos_token=__lowercase				, pad_token=__lowercase				, add_prefix_space=__lowercase				, clean_up_tokenization_spaces=__lowercase				, **__lowercase				, )
      lowercase__       							=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
      if pre_tok_state.get('''add_prefix_space'''				, __lowercase						) != add_prefix_space:
         lowercase__       							=							getattr(__lowercase				, pre_tok_state.pop('''type'''						)						)
         lowercase__       							=							add_prefix_space
         lowercase__       							=							pre_tok_class(**__lowercase						)

      lowercase__       							=							add_prefix_space

   def snake_case_(      self				, *_lowerCamelCase				, **_lowerCamelCase						)->       Union[str, Any]:
      lowercase__       							=							kwargs.get('''is_split_into_words'''				, __lowercase						)
      if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
             ''' pretokenized inputs.'''						)

      return super()._batch_encode_plus(*__lowercase				, **__lowercase						)

   def snake_case_(      self				, *_lowerCamelCase				, **_lowerCamelCase						)->       Any:
      lowercase__       							=							kwargs.get('''is_split_into_words'''				, __lowercase						)

      if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
             ''' pretokenized inputs.'''						)

      return super()._encode_plus(*__lowercase				, **__lowercase						)

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase = None						)->       Any:
      lowercase__       							=							self._tokenizer.model.save(__lowercase				, name=__lowercase						)
      return tuple(__lowercase						)

   def snake_case_(      self				, _lowerCamelCase						)->       Union[str, Any]:
      lowercase__       							=							[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(__lowercase				, add_special_tokens=__lowercase						) + [self.eos_token_id]						)

      if len(__lowercase						) > self.model_max_length:
         lowercase__       							=							input_ids[-self.model_max_length :]
      return input_ids








'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







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

  __A	:		Dict	  =      img
  __A	:		Dict	  =      img.shape[1]
  __A	:		Tuple	  =      img.shape[0]
  __A	:		List[str]	  =      dst_width
  __A	:		Tuple	  =      dst_height

  __A	:		Optional[Any]	  =      self.src_w / self.dst_w
  __A	:		List[str]	  =      self.src_h / self.dst_h

  __A	:		Dict	  =      (
      np.ones((self.dst_h, self.dst_w, 3)   ,   np.uinta    ) * 255
  )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for i in range(self.dst_h    ):
   for j in range(self.dst_w    ):
    __A	:		List[str]	  =      self.img[self.get_y(__lowercase    )][self.get_x(__lowercase    )]


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_x * x    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_y * y    )


if __name__ == "__main__":
 UpperCAmelCase_				,      UpperCAmelCase_			:  str										=   8_0_0, 6_0_0
 UpperCAmelCase_			:  str										=   imread('image_data/lena.jpg', 1)
 UpperCAmelCase_			:  List[str]										=   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()






