





'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowercase__       : Optional[Any]         =       logging.get_logger(__name__)


lowercase__       : Dict         =       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase__       : Any         =       {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowercase__       : List[str]         =       {
    '''allenai/led-base-16384''': 1_63_84,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 			_lowerCAmelCase     (    )	->	Optional[Any]:
     __A		:					Union[str, Any]						=			(
         list(range(ord('!'     )					,				ord('~'     ) + 1     )     ) + list(range(ord('¡'     )					,				ord('¬'     ) + 1     )     ) + list(range(ord('®'     )					,				ord('ÿ'     ) + 1     )     )
     )
     __A		:					Tuple						=			bs[:]
     __A		:					Tuple						=			0
     for b in range(2**8     ):
          if b not in bs:
               bs.append(__snake_case     )
               cs.append(2**8 + n     )
               n += 1
     __A		:					Any						=			[chr(__snake_case     ) for n in cs]
     return dict(zip(__snake_case					,				__snake_case     )     )






def 			_lowerCAmelCase     (    __snake_case    :					Tuple     )	->	str:
     __A		:					List[str]						=			set()
     __A		:					int						=			word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)     )
          __A		:					Dict						=			char
     return pairs







class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase="replace"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="<unk>"		,      _UpperCAmelCase="<pad>"		,      _UpperCAmelCase="<mask>"		,      _UpperCAmelCase=False		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Optional[Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else bos_token
          __A		:					List[Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else eos_token
          __A		:					List[Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else sep_token
          __A		:					List[Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else cls_token
          __A		:					Optional[Any]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else unk_token
          __A		:					Optional[int]						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          __A		:					Tuple						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

          super().__init__(
              errors=_UpperCAmelCase		,      bos_token=_UpperCAmelCase		,      eos_token=_UpperCAmelCase		,      unk_token=_UpperCAmelCase		,      sep_token=_UpperCAmelCase		,      cls_token=_UpperCAmelCase		,      pad_token=_UpperCAmelCase		,      mask_token=_UpperCAmelCase		,      add_prefix_space=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          with open(_UpperCAmelCase		,      encoding='utf-8') as vocab_handle:
               __A		:					List[Any]						=			json.load(_UpperCAmelCase)
          __A		:					Optional[Any]						=			{v: k for k, v in self.encoder.items()}
          __A		:					List[str]						=			errors  # how to handle errors in decoding
          __A		:					Dict						=			bytes_to_unicode()
          __A		:					List[Any]						=			{v: k for k, v in self.byte_encoder.items()}
          with open(_UpperCAmelCase		,      encoding='utf-8') as merges_handle:
               __A		:					Any						=			merges_handle.read().split('\n')[1:-1]
          __A		:					List[str]						=			[tuple(merge.split()) for merge in bpe_merges]
          __A		:					Optional[int]						=			dict(zip(_UpperCAmelCase		,      range(len(_UpperCAmelCase))))
          __A		:					Optional[Any]						=			{}
          __A		:					Optional[Any]						=			add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          __A		:					Optional[int]						=			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')


     @property
     # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return len(self.encoder)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return dict(self.encoder		,      **self.added_tokens_encoder)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if token in self.cache:
               return self.cache[token]
          __A		:					Union[str, Any]						=			tuple(_UpperCAmelCase)
          __A		:					int						=			get_pairs(_UpperCAmelCase)

          if not pairs:
               return token

          while True:
               __A		:					int						=			min(_UpperCAmelCase		,      key=lambda _UpperCAmelCase: self.bpe_ranks.get(_UpperCAmelCase		,      float('inf')))
               if bigram not in self.bpe_ranks:
                    break
               __A					,__A		:					Union[str, Any]						=			bigram
               __A		:					Tuple						=			[]
               __A		:					List[str]						=			0
               while i < len(_UpperCAmelCase):
                    try:
                         __A		:					Any						=			word.index(_UpperCAmelCase		,      _UpperCAmelCase)
                    except ValueError:
                         new_word.extend(word[i:])
                         break
                    else:
                         new_word.extend(word[i:j])
                         __A		:					int						=			j

                    if word[i] == first and i < len(_UpperCAmelCase) - 1 and word[i + 1] == second:
                         new_word.append(first + second)
                         i += 2
                    else:
                         new_word.append(word[i])
                         i += 1
               __A		:					Optional[Any]						=			tuple(_UpperCAmelCase)
               __A		:					Dict						=			new_word
               if len(_UpperCAmelCase) == 1:
                    break
               else:
                    __A		:					Union[str, Any]						=			get_pairs(_UpperCAmelCase)
          __A		:					List[str]						=			' '.join(_UpperCAmelCase)
          __A		:					Tuple						=			word
          return word


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			[]
          for token in re.findall(self.pat		,      _UpperCAmelCase):
               __A		:					Dict						=			''.join(
                   self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase).split(' '))
          return bpe_tokens


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return self.encoder.get(_UpperCAmelCase		,      self.encoder.get(self.unk_token))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return self.decoder.get(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[Any]						=			''.join(_UpperCAmelCase)
          __A		:					Any						=			bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'		,      errors=self.errors)
          return text


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if not os.path.isdir(_UpperCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
          __A		:					str						=			os.path.join(
              _UpperCAmelCase		,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
          __A		:					Any						=			os.path.join(
              _UpperCAmelCase		,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

          with open(_UpperCAmelCase		,      'w'		,      encoding='utf-8') as f:
               f.write(json.dumps(self.encoder		,      indent=2		,      sort_keys=_UpperCAmelCase		,      ensure_ascii=_UpperCAmelCase) + '\n')

          __A		:					List[str]						=			0
          with open(_UpperCAmelCase		,      'w'		,      encoding='utf-8') as writer:
               writer.write('#version: 0.2\n')
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,      key=lambda _UpperCAmelCase: kv[1]):
                    if index != token_index:
                         logger.warning(
                             F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                             ' Please check that the tokenizer is not corrupted!')
                         __A		:					Tuple						=			token_index
                    writer.write(' '.join(_UpperCAmelCase) + '\n')
                    index += 1

          return vocab_file, merge_file


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = False):

          '''simple docstring'''






          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_UpperCAmelCase		,      token_ids_a=_UpperCAmelCase		,      already_has_special_tokens=_UpperCAmelCase)

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					int						=			[self.sep_token_id]
          __A		:					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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=False		,      **_UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			kwargs.pop('add_prefix_space'		,      self.add_prefix_space)
          if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase) > 0 and not text[0].isspace()):
               __A		:					Any						=			' ' + text
          return (text, kwargs)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = PaddingStrategy.DO_NOT_PAD		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      ):

          '''simple docstring'''






          __A		:					Dict						=			super()._pad(
              encoded_inputs=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding_strategy=_UpperCAmelCase		,      pad_to_multiple_of=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      )

          # Load from model defaults
          if return_attention_mask is None:
               __A		:					str						=			'attention_mask' in self.model_input_names

          if return_attention_mask and "global_attention_mask" in encoded_inputs:
               __A		:					Dict						=			encoded_inputs[self.model_input_names[0]]
               # `global_attention_mask` need to have the same length as other (sequential) inputs.
               __A		:					Dict						=			len(encoded_inputs['global_attention_mask']) != len(_UpperCAmelCase)

               if needs_to_be_padded:
                    __A		:					Optional[Any]						=			len(_UpperCAmelCase) - len(encoded_inputs['global_attention_mask'])

                    if self.padding_side == "right":
                         # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                         __A		:					str						=			(
                             encoded_inputs['global_attention_mask'] + [-1] * difference
                         )
                    elif self.padding_side == "left":
                         __A		:					Any						=			[-1] * difference + encoded_inputs[
                             'global_attention_mask'
                         ]
                    else:
                         raise ValueError('Invalid padding strategy:' + str(self.padding_side))

          return encoded_inputs



from __future__ import annotations

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

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




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =initial_vectors
       for _ in range(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						List[str]            =iteration_step(SCREAMING_SNAKE_CASE__	)
       return vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =[]
       for i, start_vector in enumerate(vectors[:-1]	):
              _lowerCamelCase			:						Tuple            =vectors[i + 1]
              new_vectors.append(SCREAMING_SNAKE_CASE__	)
              _lowerCamelCase			:						Tuple            =end_vector - start_vector
              new_vectors.append(start_vector + difference_vector / 3	)
              new_vectors.append(
                  start_vector + difference_vector / 3 + rotate(difference_vector / 3						,     60	)	)
              new_vectors.append(start_vector + difference_vector * 2 / 3	)
       new_vectors.append(vectors[-1]	)
       return new_vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : numpy.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =numpy.radians(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =numpy.cos(SCREAMING_SNAKE_CASE__	), numpy.sin(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =numpy.array(((c, -s), (s, c))	)
       return numpy.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[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()
       _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =zip(*SCREAMING_SNAKE_CASE__	)
       plt.plot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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


