





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_bart import BartTokenizer


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_SCREAMING_SNAKE_CASE 		=						{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

_SCREAMING_SNAKE_CASE 		=						{
    """facebook/bart-base""": 10_24,
    """facebook/bart-large""": 10_24,
    """facebook/bart-large-mnli""": 10_24,
    """facebook/bart-large-cnn""": 10_24,
    """facebook/bart-large-xsum""": 10_24,
    """yjernite/bart_eli5""": 10_24,
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     List[str]	 =    VOCAB_FILES_NAMES
						__magic_name__:     Union[str, Any]	 =    PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:     Union[str, Any]	 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:     Union[str, Any]	 =    ["input_ids", "attention_mask"]
						__magic_name__:     Any	 =    BartTokenizer






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







							"""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						,				)

							snake_case_						:     Dict     =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
							if pre_tok_state.get('add_prefix_space'						,				_A		) != add_prefix_space:
								snake_case_						:     Optional[Any]     =		getattr(_A						,				pre_tok_state.pop('type'		)		)
								snake_case_						:     int     =		add_prefix_space
								snake_case_						:     Optional[int]     =		pre_tok_class(**_A		)

							snake_case_						:     Any     =		add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							snake_case_						:     List[str]     =		'post_processor'
							snake_case_						:     Union[str, Any]     =		getattr(self.backend_tokenizer						,				_A						,				_A		)
							if tokenizer_component_instance:
								snake_case_						:     List[Any]     =		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:
									snake_case_						:     Union[str, Any]     =		tuple(state['sep']		)
								if "cls" in state:
									snake_case_						:     int     =		tuple(state['cls']		)

								snake_case_						:     Optional[int]     =		False

								if state.get('add_prefix_space'						,				_A		) != add_prefix_space:
									snake_case_						:     Any     =		add_prefix_space
									snake_case_						:     Optional[Any]     =		True

								if state.get('trim_offsets'						,				_A		) != trim_offsets:
									snake_case_						:     Optional[int]     =		trim_offsets
									snake_case_						:     str     =		True

								if changes_to_apply:
									snake_case_						:     List[Any]     =		getattr(_A						,				state.pop('type'		)		)
									snake_case_						:     Optional[int]     =		component_class(**_A		)
									setattr(self.backend_tokenizer						,				_A						,				_A		)






						@property
						def UpperCAmelCase_			( self : List[str]		)							->      str:







							"""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 UpperCAmelCase_			( self : Optional[int]						,				_A : Tuple		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Tuple     =		AddedToken(_A						,				lstrip=_A						,				rstrip=_A		) if isinstance(_A						,				_A		) else value
							snake_case_						:     List[str]     =		value






						def UpperCAmelCase_			( self : Union[str, Any]						,				*_A : List[Any]						,				**_A : Union[str, Any]		)							->      BatchEncoding:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		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 UpperCAmelCase_			( self : Union[str, Any]						,				*_A : Optional[Any]						,				**_A : str		)							->      BatchEncoding:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		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 UpperCAmelCase_			( self : Optional[Any]						,				_A : str						,				_A : Optional[str] = None		)							->      Tuple[str]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		self._tokenizer.model.save(_A						,				name=_A		)
							return tuple(_A		)






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







							"""simple docstring"""


							snake_case_						:     int     =		[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 UpperCAmelCase_			( self : Any						,				_A : List[int]						,				_A : Optional[List[int]] = None		)							->      List[int]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		[self.sep_token_id]
							snake_case_						:     Tuple     =		[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]








from __future__ import annotations

import math


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     Optional[int]     =		u
	for i in range(1				,	__a				):
		snake_case_						:     Optional[Any]     =		temp * (u - i)
	return temp


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Dict     =		int(input('enter the numbers of values: '				)				)
	snake_case_						:     list[list[float]]     =		[]
	for _ in range(__a				):
		y.append([]				)
	for i in range(__a				):
		for j in range(__a				):
			y[i].append(__a				)
			snake_case_						:     str     =		0

	print('enter the values of parameters in a list: '				)
	snake_case_						:     int     =		list(map(__a				,	input().split()				)				)

	print('enter the values of corresponding parameters: '				)
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		float(input()				)

	snake_case_						:     int     =		int(input('enter the value to interpolate: '				)				)
	snake_case_						:     List[Any]     =		(value - x[0]) / (x[1] - x[0])

	# for calculating forward difference table

	for i in range(1				,	__a				):
		for j in range(n - i				):
			snake_case_						:     int     =		y[j + 1][i - 1] - y[j][i - 1]

	snake_case_						:     str     =		y[0][0]
	for i in range(1				,	__a				):
		summ += (ucal(__a				,	__a				) * y[0][i]) / math.factorial(__a				)

	print(f"""the value at {value} is {summ}"""				)


if __name__ == "__main__":
						main()

