

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

from tokenizers import processors

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


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
       from .tokenization_llama import LlamaTokenizer
else:
       lowerCamelCase 	=   None

lowerCamelCase 	=   logging.get_logger(__name__)
lowerCamelCase 	=   {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase 	=   '[INST]', '[/INST]'
lowerCamelCase 	=   '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
lowerCamelCase 	=   'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'







class 		__magic_name__     (				_UpperCamelCase				):

   '''simple docstring'''




   lowerCamelCase__					:      Dict        =  VOCAB_FILES_NAMES
   lowerCamelCase__					:      Optional[int]        =  LlamaTokenizer
   lowerCamelCase__					:      int        =  'left'
   lowerCamelCase__					:      List[str]        =  ['input_ids', 'attention_mask']



   def __init__(							self,       lowercase_=None,       lowercase_=None,       lowercase_=False,       lowercase_="<unk>",       lowercase_="<s>",       lowercase_="</s>",       lowercase_=True,       lowercase_=False,       **lowercase_,       )				->       Tuple:






     """simple docstring"""






     super().__init__(
         vocab_file=_SCREAMING_SNAKE_CASE,       tokenizer_file=_SCREAMING_SNAKE_CASE,       clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE,       unk_token=_SCREAMING_SNAKE_CASE,       bos_token=_SCREAMING_SNAKE_CASE,       eos_token=_SCREAMING_SNAKE_CASE,       **_SCREAMING_SNAKE_CASE,       )
     a__    =add_bos_token
     a__    =add_eos_token
     self.update_post_processor()

     a__    =vocab_file
     a__    =False if not self.vocab_file else True



   def 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     a__    =self.bos_token
     a__    =self.bos_token_id

     a__    =self.eos_token
     a__    =self.eos_token_id

     a__    =F"""{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}"""
     a__    =F"""{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}"""

     a__    =[]
     if self.add_bos_token:
       special_tokens.append((bos, bos_token_id)			)
     if self.add_eos_token:
       special_tokens.append((eos, eos_token_id)			)
     a__    =processors.TemplateProcessing(
         single=_SCREAMING_SNAKE_CASE,       pair=_SCREAMING_SNAKE_CASE,       special_tokens=_SCREAMING_SNAKE_CASE			)



   @property
   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     return self._add_eos_token



   @property
   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     return self._add_bos_token



   @add_eos_token.setter
   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






     a__    =value
     self.update_post_processor()



   @add_bos_token.setter
   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     a__    =value
     self.update_post_processor()



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None			)				->       Tuple[str]:






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

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

     return (out_vocab_file,)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






     a__    =list(conversation.iter_texts()			)
     if not all([is_user for is_user, msg in dialogue[::2]]			) or not all(
         [not is_user for is_user, msg in dialogue[1::2]]			):
       raise ValueError(
           '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''			)

     a__    =[]
     if len(conversation.past_user_inputs			) > 0:
       if not conversation.past_user_inputs[0].startswith(_SCREAMING_SNAKE_CASE			) or E_SYS not in conversation.past_user_inputs[0]:
         a__    =(
             B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
         )
     elif not dialogue[0][1].startswith(_SCREAMING_SNAKE_CASE			) or E_SYS not in dialogue[0][1]:
       a__    =(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

     dialog_tokens += sum(
         [
             [self.bos_token_id]
             + self.encode(
                 F"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """,       add_special_tokens=_SCREAMING_SNAKE_CASE			)
             + [self.eos_token_id]
             for prompt, answer in zip(dialogue[::2],       dialogue[1::2]			)
         ],       [],       )
     if not (dialogue[-1][0]):
       raise ValueError(F"""Last message must be from user, got {dialogue[-1]["role"]}"""			)
     dialog_tokens += [self.bos_token_id] + self.encode(
         F"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}""",       add_special_tokens=_SCREAMING_SNAKE_CASE			)
     return dialog_tokens

'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> list[list[int]]:
    return [[int(row == column  ) for column in range(_UpperCamelCase  )] for row in range(_UpperCamelCase  )]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  int(''''''.join(c for c in ordinal if c in '''0123456789'''  )  )  # 1000th --> 1000
        print(
            F'''{ordinal} fibonacci number using matrix exponentiation is '''
            F'''{nth_fibonacci_matrix(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\n'''  )
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






