





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


lowerCAmelCase							:       Dict					 =	logging.get_logger(__name__)

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

lowerCAmelCase							:       List[Any]					 =	{
    """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"""
        ),
    },
}

lowerCAmelCase							:       Any					 =	{
    """squeezebert/squeezebert-uncased""": 512,
    """squeezebert/squeezebert-mnli""": 512,
    """squeezebert/squeezebert-mnli-headless""": 512,
}


lowerCAmelCase							:       Union[str, Any]					 =	{
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__     :   Any		      =     PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE__     :   Any		      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__     :   Dict		      =     SqueezeBertTokenizer






				def __init__(  self			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, _lowerCAmelCase=True			, _lowerCAmelCase="[UNK]"			, _lowerCAmelCase="[SEP]"			, _lowerCAmelCase="[PAD]"			, _lowerCAmelCase="[CLS]"			, _lowerCAmelCase="[MASK]"			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, **_lowerCAmelCase			, ):

										"""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			, )

										__SCREAMING_SNAKE_CASE:				int           =   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
										):
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   getattr(_lowerCAmelCase			, normalizer_state.pop('''type'''					)					)
																__SCREAMING_SNAKE_CASE:				Dict           =   do_lower_case
																__SCREAMING_SNAKE_CASE:				Any           =   strip_accents
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   tokenize_chinese_chars
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   normalizer_class(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   do_lower_case






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   [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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   [self.sep_token_id]
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   self._tokenizer.model.save(_lowerCAmelCase			, name=_lowerCAmelCase					)
										return tuple(_lowerCAmelCase					)










def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   int					)      ->    list:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   len(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   [[0] * n for i in range(UpperCamelCase__					)]
						for i in range(UpperCamelCase__					):
												__SCREAMING_SNAKE_CASE:				str           =   y_points[i]

						for i in range(2       ,			UpperCamelCase__					):
												for j in range(UpperCamelCase__       ,			UpperCamelCase__					):
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
																		    (xa - x_points[j - i + 1]) * q[j][i - 1]
																		    - (xa - x_points[j]) * q[j - 1][i - 1]
																		) / (x_points[j] - x_points[j - i + 1])

						return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()



