


"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase						=      logging.get_logger(__name__)

__UpperCamelCase						=      {'''vocab_file''': '''sentencepiece.bpe.model'''}

__UpperCamelCase						=      {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

__UpperCamelCase						=      {
    '''camembert-base''': 512,
}

__UpperCamelCase						=      '''▁'''
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_						:     Dict								=     PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_						:     List[str]								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     ["""input_ids""", """attention_mask"""]







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

								SCREAMING_SNAKE_CASE													=					{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=lowerCAmelCase__						,       eos_token=lowerCAmelCase__						,       unk_token=lowerCAmelCase__						,       sep_token=lowerCAmelCase__						,       cls_token=lowerCAmelCase__						,       pad_token=lowerCAmelCase__						,       mask_token=lowerCAmelCase__						,       additional_special_tokens=lowerCAmelCase__						,       sp_model_kwargs=self.sp_model_kwargs						,       **lowerCAmelCase__						,       )
								SCREAMING_SNAKE_CASE													=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(str(lowerCAmelCase__				)				)
								SCREAMING_SNAKE_CASE													=					vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								SCREAMING_SNAKE_CASE													=					{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
								SCREAMING_SNAKE_CASE													=					len(self.fairseq_tokens_to_ids				)
								SCREAMING_SNAKE_CASE													=					len(self.sp_model				) + len(self.fairseq_tokens_to_ids				)
								SCREAMING_SNAKE_CASE													=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[int]:

								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								SCREAMING_SNAKE_CASE													=					[self.cls_token_id]
								SCREAMING_SNAKE_CASE													=					[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = False				)     ->							List[int]:
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowerCAmelCase__						,       token_ids_a=lowerCAmelCase__						,       already_has_special_tokens=lowerCAmelCase__				)

								if token_ids_a is None:
														return [1] + ([0] * len(lowerCAmelCase__				)) + [1]
								return [1] + ([0] * len(lowerCAmelCase__				)) + [1, 1] + ([0] * len(lowerCAmelCase__				)) + [1]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[int]:
								SCREAMING_SNAKE_CASE													=					[self.sep_token_id]
								SCREAMING_SNAKE_CASE													=					[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]







		@property
		def 							__A						(	self				)     ->							Tuple:
								return len(self.fairseq_tokens_to_ids				) + len(self.sp_model				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					{self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							List[str]:
								return self.sp_model.encode(lowerCAmelCase__						,       out_type=lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Dict:
								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(lowerCAmelCase__				) == 0:
														# Convert sentence piece unk token to fairseq unk token index
														return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Optional[int]:
								if index in self.fairseq_ids_to_tokens:
														return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							str:
								SCREAMING_SNAKE_CASE													=					[]
								SCREAMING_SNAKE_CASE													=					''
								SCREAMING_SNAKE_CASE													=					False
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				if not prev_is_special:
																										out_string += " "
																				out_string += self.sp_model.decode(lowerCAmelCase__				) + token
																				SCREAMING_SNAKE_CASE													=					True
																				SCREAMING_SNAKE_CASE													=					[]
														else:
																				current_sub_tokens.append(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					False
								out_string += self.sp_model.decode(lowerCAmelCase__				)
								return out_string.strip()







		def __getstate__(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					self.__dict__.copy()
								SCREAMING_SNAKE_CASE													=					None
								return state







		def __setstate__(	self						,       lowerCAmelCase__				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					d

								# for backward compatibility
								if not hasattr(self						,       'sp_model_kwargs'				):
														SCREAMING_SNAKE_CASE													=					{}

								SCREAMING_SNAKE_CASE													=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(self.vocab_file				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							Tuple[str]:
								if not os.path.isdir(lowerCAmelCase__				):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
														return
								SCREAMING_SNAKE_CASE													=					os.path.join(
								    lowerCAmelCase__						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

								if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				) and os.path.isfile(self.vocab_file				):
														copyfile(self.vocab_file						,       lowerCAmelCase__				)
								elif not os.path.isfile(self.vocab_file				):
														with open(lowerCAmelCase__						,       'wb'				) as fi:
																				SCREAMING_SNAKE_CASE													=					self.sp_model.serialized_model_proto()
																				fi.write(lowerCAmelCase__				)

								return (out_vocab_file,)






"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Callable[[int | float], int | float] ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int = 1_00 ,			)   ->						float:
						SCREAMING_SNAKE_CASE													=					x_start
						SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0.0
						for _ in range(SCREAMING_SNAKE_CASE_  ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												SCREAMING_SNAKE_CASE													=					(x_end - x_start) / steps + xa
												SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
												area += abs(fxa + fxa  ) * (xa - xa) / 2
												# Increment step
												SCREAMING_SNAKE_CASE													=					xa
												SCREAMING_SNAKE_CASE													=					fxa
						return area


if __name__ == "__main__":


	def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Dict:
							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


