'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__UpperCAmelCase												=logging.get_logger(__name__)


__UpperCAmelCase												={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
__UpperCAmelCase												={
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase												={
    "RUCAIBox/mvp": 1_0_2_4,
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					int							=VOCAB_FILES_NAMES
     lowerCamelCase  :					List[str]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Any							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					Union[str, Any]							=["input_ids", "attention_mask"]
     lowerCamelCase  :					List[Any]							=MvpTokenizer






     def __init__(			self		:		List[Any]  ,    a		:		List[str]=None  ,    a		:		List[Any]=None  ,    a		:		Union[str, Any]=None  ,    a		:		int="replace"  ,    a		:		Dict="<s>"  ,    a		:		str="</s>"  ,    a		:		Optional[int]="</s>"  ,    a		:		Dict="<s>"  ,    a		:		Tuple="<unk>"  ,    a		:		Tuple="<pad>"  ,    a		:		Tuple="<mask>"  ,    a		:		Union[str, Any]=False  ,    a		:		str=True  ,    **a		:		Any  ,    ):





         """simple docstring"""



         super().__init__(
             a  ,    a  ,    tokenizer_file=a  ,    errors=a  ,    bos_token=a  ,    eos_token=a  ,    sep_token=a  ,    cls_token=a  ,    unk_token=a  ,    pad_token=a  ,    mask_token=a  ,    add_prefix_space=a  ,    trim_offsets=a  ,    **a  ,    )

         __lowerCamelCase          =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
         if pre_tok_state.get('''add_prefix_space'''  ,    a   ) != add_prefix_space:
             __lowerCamelCase          =  getattr(a  ,    pre_tok_state.pop('''type'''   )   )
             __lowerCamelCase          =  add_prefix_space
             __lowerCamelCase          =  pre_tok_class(**a   )

         __lowerCamelCase          =  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         __lowerCamelCase          =  '''post_processor'''
         __lowerCamelCase          =  getattr(self.backend_tokenizer  ,    a  ,    a   )
         if tokenizer_component_instance:
             __lowerCamelCase          =  json.loads(tokenizer_component_instance.__getstate__()   )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __lowerCamelCase          =  tuple(state['''sep''']   )
             if "cls" in state:
                 __lowerCamelCase          =  tuple(state['''cls''']   )

             __lowerCamelCase          =  False

             if state.get('''add_prefix_space'''  ,    a   ) != add_prefix_space:
                 __lowerCamelCase          =  add_prefix_space
                 __lowerCamelCase          =  True

             if state.get('''trim_offsets'''  ,    a   ) != trim_offsets:
                 __lowerCamelCase          =  trim_offsets
                 __lowerCamelCase          =  True

             if changes_to_apply:
                 __lowerCamelCase          =  getattr(a  ,    state.pop('''type'''   )   )
                 __lowerCamelCase          =  component_class(**a   )
                 setattr(self.backend_tokenizer  ,    a  ,    a   )






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         if self._mask_token is None:
             if self.verbose:
                 logger.error('''Using mask_token, but it is not set yet.'''   )
             return None
         return str(self._mask_token   )






     @mask_token.setter
     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  AddedToken(a  ,    lstrip=a  ,    rstrip=a   ) if isinstance(a  ,    a   ) else value
         __lowerCamelCase          =  value






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    *a		:		List[str]  ,    **a		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  kwargs.get('''is_split_into_words'''  ,    a   )

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

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






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





         """simple docstring"""



         __lowerCamelCase          =  kwargs.get('''is_split_into_words'''  ,    a   )

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

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		str  ,    a		:		Optional[str] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  self._tokenizer.model.save(a  ,    name=a   )
         return tuple(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		Union[str, Any]  ,    a		:		Optional[Any]=None   ):





         """simple docstring"""



         __lowerCamelCase          =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [self.sep_token_id]
         __lowerCamelCase          =  [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep   ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]

'''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()
