





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


A       :   str					= logging.get_logger(__name__)

A       :   Optional[int]					= {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A       :   List[str]					= {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

A       :   int					= {
    """squeezebert/squeezebert-uncased""": 5_1_2,
    """squeezebert/squeezebert-mnli""": 5_1_2,
    """squeezebert/squeezebert-mnli-headless""": 5_1_2,
}


A       :   List[str]					= {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}


class     A     (_UpperCamelCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			List[str]							     =       VOCAB_FILES_NAMES
			__lowerCamelCase      :			Tuple							     =       PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase      :			List[str]							     =       PRETRAINED_INIT_CONFIGURATION
			__lowerCamelCase      :			str							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase      :			Dict							     =       SqueezeBertTokenizer






			def __init__(     self  :       List[Any]				,      __lowerCAmelCase  :       str=None				,      __lowerCAmelCase  :       Any=None				,      __lowerCAmelCase  :       Dict=True				,      __lowerCAmelCase  :       List[Any]="[UNK]"				,      __lowerCAmelCase  :       Dict="[SEP]"				,      __lowerCAmelCase  :       Dict="[PAD]"				,      __lowerCAmelCase  :       List[str]="[CLS]"				,      __lowerCAmelCase  :       List[str]="[MASK]"				,      __lowerCAmelCase  :       List[str]=True				,      __lowerCAmelCase  :       int=None				,      **__lowerCAmelCase  :       List[str]				,      )      ->      Union[str, Any]:







					"""simple docstring"""






					super().__init__(
					    lowerCamelCase_				,      tokenizer_file=lowerCamelCase_				,      do_lower_case=lowerCamelCase_				,      unk_token=lowerCamelCase_				,      sep_token=lowerCamelCase_				,      pad_token=lowerCamelCase_				,      cls_token=lowerCamelCase_				,      mask_token=lowerCamelCase_				,      tokenize_chinese_chars=lowerCamelCase_				,      strip_accents=lowerCamelCase_				,      **lowerCamelCase_				,      )

					A__     =  json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
					if (
					    normalizer_state.get("""lowercase"""				,      lowerCamelCase_  ) != do_lower_case
					    or normalizer_state.get("""strip_accents"""				,      lowerCamelCase_  ) != strip_accents
					    or normalizer_state.get("""handle_chinese_chars"""				,      lowerCamelCase_  ) != tokenize_chinese_chars
					):
							A__     =  getattr(lowerCamelCase_				,      normalizer_state.pop("""type"""  )  )
							A__     =  do_lower_case
							A__     =  strip_accents
							A__     =  tokenize_chinese_chars
							A__     =  normalizer_class(**lowerCamelCase_  )

					A__     =  do_lower_case






			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Tuple=None  )      ->      Tuple:







					"""simple docstring"""






					A__     =  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

					return output






			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       List[int]				,      __lowerCAmelCase  :       Optional[List[int]] = None  )      ->      str:







					"""simple docstring"""






					A__     =  [self.sep_token_id]
					A__     =  [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  ) * [0] + len(token_ids_a + sep  ) * [1]







			def   a_			(     self  :       Optional[Any]				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Optional[str] = None  )      ->      int:







					"""simple docstring"""






					A__     =  self._tokenizer.model.save(lowerCamelCase_				,      name=lowerCamelCase_  )
					return tuple(lowerCamelCase_  )










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


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						int			):
			if dst_width < 0 or dst_height < 0:
					raise ValueError('Destination width/height should be > 0'			)

			lowercase__					:      str      =		img
			lowercase__					:      Tuple      =		img.shape[1]
			lowercase__					:      str      =		img.shape[0]
			lowercase__					:      Tuple      =		dst_width
			lowercase__					:      Any      =		dst_height

			lowercase__					:      Tuple      =		self.src_w / self.dst_w
			lowercase__					:      Union[str, Any]      =		self.src_h / self.dst_h

			lowercase__					:      List[Any]      =		(
			    np.ones((self.dst_h, self.dst_w, 3),					np.uinta			) * 255
			)

	def    snake_case__(						self:						Dict			):
			for i in range(self.dst_h			):
					for j in range(self.dst_w			):
							lowercase__					:      Tuple      =		self.img[self.get_y(lowerCamelCase_			)][self.get_x(lowerCamelCase_			)]

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						int			):
			return int(self.ratio_x * x			)





	def    snake_case__(						self:						int,					lowerCamelCase_:						int			):
			return int(self.ratio_y * y			)


if __name__ == "__main__":
			__UpperCamelCase,	__UpperCamelCase:	List[Any] = 8_0_0, 6_0_0
			__UpperCamelCase:	Optional[Any] = imread("""image_data/lena.jpg""", 1)
			__UpperCamelCase:	Union[str, Any] = 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()


