'''simple docstring'''






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

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


if is_sentencepiece_available():
    from .tokenization_pegasus import PegasusTokenizer
else:
    SCREAMING_SNAKE_CASE_	   =			None


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_	   =			'▁'

SCREAMING_SNAKE_CASE_	   =			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_	   =			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

SCREAMING_SNAKE_CASE_	   =			{
    'google/pegasus-xsum': 5_12,
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					VOCAB_FILES_NAMES
  __lowerCAmelCase				      =					PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase				      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase				      =					PegasusTokenizer
  __lowerCAmelCase				      =					["""input_ids""", """attention_mask"""]







  def __init__(							self   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_="<pad>"   ,    snake_case_="</s>"   ,    snake_case_="<unk>"   ,    snake_case_="<mask_2>"   ,    snake_case_="<mask_1>"   ,    snake_case_=None   ,    snake_case_=103   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= offset

    if additional_special_tokens is not None:
      if not isinstance(snake_case_   ,    snake_case_					):
        raise TypeError(
            F'''additional_special_tokens should be of type {type(snake_case_					)}, but is'''
            F''' {type(snake_case_					)}'''					)

      __UpperCAmelCase:					int		= (
          ([mask_token_sent] + additional_special_tokens)
          if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
          else additional_special_tokens
      )
      # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
      additional_special_tokens_extended += [
          F'''<unk_{i}>''' for i in range(len(snake_case_					)   ,    self.offset - 1					)
      ]

      if len(set(snake_case_					)					) != len(snake_case_					):
        raise ValueError(
            """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
            F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''					)
      __UpperCAmelCase:					int		= additional_special_tokens_extended
    else:
      __UpperCAmelCase:					List[Any]		= [mask_token_sent] if mask_token_sent is not None else []
      additional_special_tokens += [F'''<unk_{i}>''' for i in range(2   ,    self.offset					)]

    super().__init__(
        snake_case_   ,    tokenizer_file=snake_case_   ,    pad_token=snake_case_   ,    eos_token=snake_case_   ,    unk_token=snake_case_   ,    mask_token=snake_case_   ,    mask_token_sent=snake_case_   ,    offset=snake_case_   ,    additional_special_tokens=snake_case_   ,    **snake_case_   ,    )
    __UpperCAmelCase:					Tuple		= vocab_file
    __UpperCAmelCase:					str		= False if not self.vocab_file else True







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= set(self.all_special_ids					)  # call it once instead of inside list comp
    all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

    if all_special_ids != set(range(len(self.additional_special_tokens					) + 3					)					):
      raise ValueError(
          """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
          F''' {len(self.additional_special_tokens					)} additional_special_tokens, but got {all_special_ids}'''					)

    return [1 if x in all_special_ids else 0 for x in seq]







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = False					):

    '''simple docstring'''







    if already_has_special_tokens:
      return self._special_token_mask(snake_case_					)
    elif token_ids_a is None:
      return self._special_token_mask(snake_case_					) + [1]
    else:
      return self._special_token_mask(token_ids_a + token_ids_a					) + [1]







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

    '''simple docstring'''







    if token_ids_a is None:
      return token_ids_a + [self.eos_token_id]
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return token_ids_a + token_ids_a + [self.eos_token_id]







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

    '''simple docstring'''







    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(snake_case_					):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
      return
    __UpperCAmelCase:					List[Any]		= os.path.join(
        snake_case_   ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

    return (out_vocab_file,)


'''simple docstring'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE_	   =			numpy.array([0, 0])
SCREAMING_SNAKE_CASE_	   =			numpy.array([0.5, 0.866_0254])
SCREAMING_SNAKE_CASE_	   =			numpy.array([1, 0])
SCREAMING_SNAKE_CASE_	   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]   ,    _lowercase    :				int     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					int		= initial_vectors
  for _ in range(_lowercase     ):
    __UpperCAmelCase:					Any		= iteration_step(_lowercase     )
  return vectors






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					Optional[int]		= []
  for i, start_vector in enumerate(vectors[:-1]     ):
    __UpperCAmelCase:					Union[str, Any]		= vectors[i + 1]
    new_vectors.append(_lowercase     )
    __UpperCAmelCase:					Optional[int]		= end_vector - start_vector
    new_vectors.append(start_vector + difference_vector / 3     )
    new_vectors.append(
        start_vector + difference_vector / 3 + rotate(difference_vector / 3   ,    6_0     )     )
    new_vectors.append(start_vector + difference_vector * 2 / 3     )
  new_vectors.append(vectors[-1]     )
  return new_vectors






def  UpperCamelCase__					(					_lowercase    :				numpy.ndarray   ,    _lowercase    :				float     )							->	numpy.ndarray:
  __UpperCAmelCase:					Tuple		= numpy.radians(_lowercase     )
  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= numpy.cos(_lowercase     ), numpy.sin(_lowercase     )
  __UpperCAmelCase:					Tuple		= numpy.array(((c, -s), (s, c))     )
  return numpy.dot(_lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	None:
  __UpperCAmelCase:					Union[str, Any]		= plt.gca()
  axes.set_aspect("""equal"""     )

  # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
  # y-coordinates as inputs, which are constructed from the vector-list using
  # zip()
  __UpperCAmelCase, __UpperCAmelCase:					Dict		= zip(*_lowercase     )
  plt.plot(_lowercase   ,    _lowercase     )
  plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			iterate(INITIAL_VECTORS, 5)
    plot(processed_vectors)