
'''simple docstring'''
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a_     =  logging.get_logger(__name__)

a_     =  {"vocab_file": "tokenizer.model"}

a_     =  {
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
a_     =  {
    "hf-internal-testing/llama-tokenizer": 2048,
}
a_     =  "▁"

a_   ,a_     =  "[INST]", "[/INST]"
a_   ,a_     =  "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
a_     =  "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 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  VOCAB_FILES_NAMES
 UpperCAmelCase_						      =  PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_						      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_						      =  ["""input_ids""", """attention_mask"""]





 def __init__(      self	,    lowercase_	,    lowercase_="<unk>"	,    lowercase_="<s>"	,    lowercase_="</s>"	,    lowercase_=None	,    lowercase_ = None	,    lowercase_=True	,    lowercase_=False	,    lowercase_=False	,    lowercase_=True	,    **lowercase_	,    ):
     snake_case_	:      Optional[Any]            =  {} if sp_model_kwargs is None else sp_model_kwargs
     snake_case_	:      Optional[int]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else bos_token
     snake_case_	:      str            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else eos_token
     snake_case_	:      Any            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else unk_token
     snake_case_	:      List[str]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else pad_token
     super().__init__(
         bos_token=lowercase_	,    eos_token=lowercase_	,    unk_token=lowercase_	,    pad_token=lowercase_	,    add_bos_token=lowercase_	,    add_eos_token=lowercase_	,    sp_model_kwargs=self.sp_model_kwargs	,    clean_up_tokenization_spaces=lowercase_	,    legacy=lowercase_	,    **lowercase_	,    )
     if legacy:
         logger.warning_once(
             F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
             " read the related pull request available at https://github.com/huggingface/transformers/pull/24565")
     snake_case_	:      Tuple            =  legacy
     snake_case_	:      Optional[int]            =  vocab_file
     snake_case_	:      int            =  add_bos_token
     snake_case_	:      Union[str, Any]            =  add_eos_token
     snake_case_	:      str            =  spm.SentencePieceProcessor(**self.sp_model_kwargs)
     self.sp_model.Load(lowercase_)





 def __getstate__(      self):
     snake_case_	:      Any            =  self.__dict__.copy()
     snake_case_	:      List[str]            =  None
     snake_case_	:      List[str]            =  self.sp_model.serialized_model_proto()
     return state





 def __setstate__(      self	,    lowercase_):
     snake_case_	:      Tuple            =  d
     snake_case_	:      List[Any]            =  spm.SentencePieceProcessor(**self.sp_model_kwargs)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto)





 @property
 def     snake_case__						(      self):
     return self.sp_model.get_piece_size()





 def     snake_case__						(      self):
     snake_case_	:      Tuple            =  {self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
     vocab.update(self.added_tokens_encoder)
     return vocab





 def     snake_case__						(      self	,    lowercase_	,    **lowercase_):
     # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
     # the beginning of the text
     if not self.legacy:
         snake_case_	:      Dict            =  SPIECE_UNDERLINE + text.replace(lowercase_	,    " ")
     return super().tokenize(lowercase_	,    **lowercase_)





 def     snake_case__						(      self	,    lowercase_):
     if not self.legacy:
         snake_case_	:      Union[str, Any]            =  text.startswith(lowercase_)
         if is_first:
             snake_case_	:      List[str]            =  text[1:]

     snake_case_	:      List[str]            =  self.sp_model.encode(lowercase_	,    out_type=lowercase_)

     if not self.legacy and not is_first and not text.startswith(" ") and tokens[0].startswith(lowercase_):
         snake_case_	:      int            =  ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
     return tokens





 def     snake_case__						(      self	,    lowercase_):
     return self.sp_model.piece_to_id(lowercase_)





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      Dict            =  self.sp_model.IdToPiece(lowercase_)
     return token





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      Any            =  []
     snake_case_	:      Any            =  ""
     snake_case_	:      Union[str, Any]            =  False
     for i, token in enumerate(lowercase_):
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             if not prev_is_special and i != 0:
                 out_string += " "
             out_string += self.sp_model.decode(lowercase_) + token
             snake_case_	:      int            =  True
             snake_case_	:      Optional[int]            =  []
         else:
             current_sub_tokens.append(lowercase_)
             snake_case_	:      Optional[int]            =  False
     out_string += self.sp_model.decode(lowercase_)
     return out_string





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     if not os.path.isdir(lowercase_):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
     snake_case_	:      int            =  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_) and os.path.isfile(self.vocab_file):
         copyfile(self.vocab_file	,    lowercase_)
     elif not os.path.isfile(self.vocab_file):
         with open(lowercase_	,    "wb") as fi:
             snake_case_	:      Union[str, Any]            =  self.sp_model.serialized_model_proto()
             fi.write(lowercase_)

     return (out_vocab_file,)





 def     snake_case__						(      self	,    lowercase_	,    lowercase_=None):
     snake_case_	:      Optional[int]            =  [self.bos_token_id] if self.add_bos_token else []
     snake_case_	:      int            =  [self.eos_token_id] if self.add_eos_token else []

     snake_case_	:      Union[str, Any]            =  bos_token_id + token_ids_a + eos_token_id

     if token_ids_a is not None:
         snake_case_	:      Dict            =  output + bos_token_id + token_ids_a + eos_token_id

     return output





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None	,    lowercase_ = False):
     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_	,    token_ids_a=lowercase_	,    already_has_special_tokens=lowercase_)

     snake_case_	:      Dict            =  [1] if self.add_bos_token else []
     snake_case_	:      Dict            =  [1] if self.add_eos_token else []

     if token_ids_a is None:
         return bos_token_id + ([0] * len(lowercase_)) + eos_token_id
     return (
         bos_token_id
         + ([0] * len(lowercase_))
         + eos_token_id
         + bos_token_id
         + ([0] * len(lowercase_))
         + eos_token_id
     )





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     snake_case_	:      Dict            =  [self.bos_token_id] if self.add_bos_token else []
     snake_case_	:      List[str]            =  [self.eos_token_id] if self.add_eos_token else []

     snake_case_	:      Dict            =  [0] * len(bos_token_id + token_ids_a + eos_token_id)

     if token_ids_a is not None:
         output += [1] * len(bos_token_id + token_ids_a + eos_token_id)

     return output





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      Optional[int]            =  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...)")

     snake_case_	:      List[int]            =  []
     if len(conversation.past_user_inputs) > 0:
         if not conversation.past_user_inputs[0].startswith(lowercase_) or E_SYS not in conversation.past_user_inputs[0]:
             snake_case_	:      List[str]            =  (
                 B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
             )
     elif not dialogue[0][1].startswith(lowercase_) or E_SYS not in dialogue[0][1]:
         snake_case_	:      Optional[Any]            =  (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=lowercase_)
             + [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=lowercase_)
     return dialog_tokens






'''simple docstring'''
from math import exp

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






    return 8 * x - 2 * exp(-x    )

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






    snake_case_	:      Optional[Any]            =  lower_bound
    snake_case_	:      int            =  upper_bound
    for _ in range(0,						__SCREAMING_SNAKE_CASE    ):
        snake_case_     ,	snake_case_	:      List[str]            =  xa, xa - (f(__SCREAMING_SNAKE_CASE    ) * (xa - xa)) / (f(__SCREAMING_SNAKE_CASE    ) - f(__SCREAMING_SNAKE_CASE    ))
    return xa


if __name__ == "__main__":
  print(f"""Example: {secant_method(1, 3, 2)}""")




