






from __future__ import annotations

from collections.abc import Callable




def 					lowercase__				(					__snake_case   :							Callable[[int | float], int | float]					,	__snake_case   :							int | float					,	__snake_case   :							int | float					,	__snake_case   :							int = 100					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						x_start
					UpperCAmelCase_   :				Optional[int]						=						fnc(lowerCamelCase_  )
					UpperCAmelCase_   :				Optional[int]						=						0.0
					for _ in range(lowerCamelCase_  ):
										# Approximates small segments of curve as linear and solve
										# for trapezoidal area
										UpperCAmelCase_   :				int						=						(x_end - x_start) / steps + xa
										UpperCAmelCase_   :				Union[str, Any]						=						fnc(lowerCamelCase_  )
										area += abs(fxa + fxa  ) * (xa - xa) / 2
										# Increment step
										UpperCAmelCase_   :				Any						=						xa
										UpperCAmelCase_   :				str						=						fxa
					return area


if __name__ == "__main__":




		def 					lowercase__				(					__snake_case   :							Any  ):


							'''simple docstring'''


							return x**3 + x**2

		print('f(x) = x^3 + x^2')
		print('The area between the curve, x = -5, x = 5 and the x axis is:')
		__UpperCAmelCase     							=						10
		while i <= 100000:
				print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
				i *= 10







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_barthez import BarthezTokenizer
else:
		__UpperCAmelCase     							=						None

__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     							=						{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCAmelCase     							=						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__UpperCAmelCase     							=						'▁'



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Union[str, Any]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Dict							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Dict							=  ['''input_ids''', '''attention_mask''']
		_snake_case   :   Union[str, Any]							=  BarthezTokenizer



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase="<mask>"    ,		**_UpperCamelCase    ,		)     ->							str:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_   :				List[Any]						=						AddedToken(_UpperCamelCase    ,		lstrip=_UpperCamelCase    ,		rstrip=_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else mask_token

							super().__init__(
							    _UpperCamelCase    ,		tokenizer_file=_UpperCamelCase    ,		bos_token=_UpperCamelCase    ,		eos_token=_UpperCamelCase    ,		unk_token=_UpperCamelCase    ,		sep_token=_UpperCamelCase    ,		cls_token=_UpperCamelCase    ,		pad_token=_UpperCamelCase    ,		mask_token=_UpperCamelCase    ,		**_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Tuple						=						vocab_file
							UpperCAmelCase_   :				Tuple						=						False if not self.vocab_file else True



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:

							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_   :				Union[str, Any]						=						[self.cls_token_id]
							UpperCAmelCase_   :				Optional[Any]						=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Any						=						[self.sep_token_id]
							UpperCAmelCase_   :				List[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 + sep + token_ids_a + sep       ) * [0]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							if not self.can_save_slow_tokenizer:
												raise ValueError(
												    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
												    'tokenizer.'       )

							if not os.path.isdir(_UpperCamelCase       ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
												return
							UpperCAmelCase_   :				List[Any]						=						os.path.join(
							    _UpperCamelCase    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCamelCase       ):
												copyfile(self.vocab_file    ,		_UpperCamelCase       )

							return (out_vocab_file,)






