
'''simple docstring'''
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_xlm_roberta import XLMRobertaTokenizer
else:
  a_     =  None


a_     =  logging.get_logger(__name__)

a_     =  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a_     =  {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

a_     =  {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}






class 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  VOCAB_FILES_NAMES
 UpperCAmelCase_						      =  PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_						      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_						      =  ["""input_ids""", """attention_mask"""]
 UpperCAmelCase_						      =  XLMRobertaTokenizer





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

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

     snake_case_	:      str            =  vocab_file
     snake_case_	:      Any            =  False if not self.vocab_file else True





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):

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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     snake_case_	:      int            =  [self.sep_token_id]
     snake_case_	:      Dict            =  [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     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     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(lowercase_):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory.')
         return
     snake_case_	:      str            =  os.path.join(
         lowercase_	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

     return (out_vocab_file,)






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE = 1_0_0,						):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  x_start
    snake_case_	:      str            =  fnc(__SCREAMING_SNAKE_CASE    )
    snake_case_	:      List[str]            =  0.0

    for _ in range(__SCREAMING_SNAKE_CASE    ):
        # Approximates small segments of curve as linear and solve
        # for trapezoidal area
        snake_case_	:      List[str]            =  (x_end - x_start) / steps + xa
        snake_case_	:      Optional[int]            =  fnc(__SCREAMING_SNAKE_CASE    )
        area += abs(fxa + fxa    ) * (xa - xa) / 2

        # Increment step
        snake_case_	:      List[Any]            =  xa
        snake_case_	:      Optional[Any]            =  fxa
    return area


if __name__ == "__main__":

  def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
      """simple docstring"""






      return x**3

  print("f(x) = x^3")
  print("The area between the curve, x = -10, x = 10 and the x axis is:")
  a_     =  10
  while i <= 10_0000:
    a_     =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 10




