

'''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

UpperCAmelCase				: Tuple         =  logging.get_logger(__name__)

UpperCAmelCase				: Tuple         =  {'vocab_file': 'tokenizer.model'}

UpperCAmelCase				: str         =  {
    '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',
    },
}
UpperCAmelCase				: Tuple         =  {
    'hf-internal-testing/llama-tokenizer': 2_0_4_8,
}
UpperCAmelCase				: str         =  '▁'

UpperCAmelCase     ,  UpperCAmelCase				: List[Any]         =  '[INST]', '[/INST]'
UpperCAmelCase     ,  UpperCAmelCase				: Optional[Any]         =  '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
UpperCAmelCase				: List[Any]         =  '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 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   VOCAB_FILES_NAMES
 lowerCAmelCase__						      =   PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__						      =   ["input_ids", "attention_mask"]
 def __init__(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : int="<unk>"      ,							__SCREAMING_SNAKE_CASE      : Dict="<s>"      ,							__SCREAMING_SNAKE_CASE      : List[Any]="</s>"      ,							__SCREAMING_SNAKE_CASE      : Dict=None      ,							__SCREAMING_SNAKE_CASE      : Optional[Dict[str, Any]] = None      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Any=False      ,							__SCREAMING_SNAKE_CASE      : List[str]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=True      ,							**__SCREAMING_SNAKE_CASE      : List[Any]      ,							)						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {} if sp_model_kwargs is None else sp_model_kwargs
      __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else bos_token
      __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else eos_token
      __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else unk_token
      __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else pad_token
      super().__init__(
          bos_token=__SCREAMING_SNAKE_CASE      ,							eos_token=__SCREAMING_SNAKE_CASE      ,							unk_token=__SCREAMING_SNAKE_CASE      ,							pad_token=__SCREAMING_SNAKE_CASE      ,							add_bos_token=__SCREAMING_SNAKE_CASE      ,							add_eos_token=__SCREAMING_SNAKE_CASE      ,							sp_model_kwargs=self.sp_model_kwargs      ,							clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE      ,							legacy=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
      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"""    )
      __SCREAMING_SNAKE_CASE						     =   legacy
      __SCREAMING_SNAKE_CASE						     =   vocab_file
      __SCREAMING_SNAKE_CASE						     =   add_bos_token
      __SCREAMING_SNAKE_CASE						     =   add_eos_token
      __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.Load(__SCREAMING_SNAKE_CASE    )
 def __getstate__(	self      : Any    )						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.__dict__.copy()
      __SCREAMING_SNAKE_CASE						     =   None
      __SCREAMING_SNAKE_CASE						     =   self.sp_model.serialized_model_proto()
      return state
 def __setstate__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   d
      __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )
 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				Dict:


      """simple docstring"""




      return self.sp_model.get_piece_size()
 def    UpperCAmelCase__	(	self      : Any    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
      vocab.update(self.added_tokens_encoder    )
      return vocab
 def    UpperCAmelCase__	(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				List[str]:


      """simple docstring"""




      if not self.legacy:
           __SCREAMING_SNAKE_CASE						     =   SPIECE_UNDERLINE + text.replace(__SCREAMING_SNAKE_CASE      ,							""" """    )
      return super().tokenize(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				int:


      """simple docstring"""




      if not self.legacy:
           __SCREAMING_SNAKE_CASE						     =   text.startswith(__SCREAMING_SNAKE_CASE    )
           if is_first:
                __SCREAMING_SNAKE_CASE						     =   text[1:]

      __SCREAMING_SNAKE_CASE						     =   self.sp_model.encode(__SCREAMING_SNAKE_CASE      ,							out_type=__SCREAMING_SNAKE_CASE    )

      if not self.legacy and not is_first and not text.startswith(""" """    ) and tokens[0].startswith(__SCREAMING_SNAKE_CASE    ):
           __SCREAMING_SNAKE_CASE						     =   ([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
      return tokens
 def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : int    )						->				str:


      """simple docstring"""




      return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str    )						->				Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE    )
      return token
 def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Tuple    )						->				Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   []
      __SCREAMING_SNAKE_CASE						     =   """"""
      __SCREAMING_SNAKE_CASE						     =   False
      for i, token in enumerate(__SCREAMING_SNAKE_CASE    ):
           # 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(__SCREAMING_SNAKE_CASE    ) + token
                __SCREAMING_SNAKE_CASE						     =   True
                __SCREAMING_SNAKE_CASE						     =   []
           else:
                current_sub_tokens.append(__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   False
      out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    )
      return out_string
 def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : Optional[str] = None    )						->				Tuple[str]:


      """simple docstring"""




      if not os.path.isdir(__SCREAMING_SNAKE_CASE    ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
           return
      __SCREAMING_SNAKE_CASE						     =   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    ) and os.path.isfile(self.vocab_file    ):
           copyfile(self.vocab_file      ,							__SCREAMING_SNAKE_CASE    )
      elif not os.path.isfile(self.vocab_file    ):
           with open(__SCREAMING_SNAKE_CASE      ,							"""wb"""    ) as fi:
                __SCREAMING_SNAKE_CASE						     =   self.sp_model.serialized_model_proto()
                fi.write(__SCREAMING_SNAKE_CASE    )

      return (out_vocab_file,)
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Any=None    )						->				Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [self.bos_token_id] if self.add_bos_token else []
      __SCREAMING_SNAKE_CASE						     =   [self.eos_token_id] if self.add_eos_token else []

      __SCREAMING_SNAKE_CASE						     =   bos_token_id + token_ids_a + eos_token_id

      if token_ids_a is not None:
           __SCREAMING_SNAKE_CASE						     =   output + bos_token_id + token_ids_a + eos_token_id

      return output
 def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False    )						->				List[int]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE						     =   [1] if self.add_bos_token else []
      __SCREAMING_SNAKE_CASE						     =   [1] if self.add_eos_token else []

      if token_ids_a is None:
           return bos_token_id + ([0] * len(__SCREAMING_SNAKE_CASE    )) + eos_token_id
      return (
          bos_token_id
          + ([0] * len(__SCREAMING_SNAKE_CASE    ))
          + eos_token_id
          + bos_token_id
          + ([0] * len(__SCREAMING_SNAKE_CASE    ))
          + eos_token_id
      )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None    )						->				List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [self.bos_token_id] if self.add_bos_token else []
      __SCREAMING_SNAKE_CASE						     =   [self.eos_token_id] if self.add_eos_token else []

      __SCREAMING_SNAKE_CASE						     =   [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    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : "Conversation"    )						->				List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   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...)"""    )

      __SCREAMING_SNAKE_CASE						     =   []
      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]:
                __SCREAMING_SNAKE_CASE						     =   (
                    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]:
           __SCREAMING_SNAKE_CASE						     =   (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'''



from __future__ import annotations

from collections.abc import Callable







def      a__ (						a__   ,					a__   ,					a__   ,					a__ = 1_00   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   x_start
     __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
     __SCREAMING_SNAKE_CASE						     =   0.0

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

          # Increment step
          __SCREAMING_SNAKE_CASE						     =   xa
          __SCREAMING_SNAKE_CASE						     =   fxa
     return area


if __name__ == "__main__":







  def      a__ (						a__		):




       """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:')
  UpperCAmelCase				: Optional[Any]         =  1_0
  while i <= 1_0_0_0_0_0:
    UpperCAmelCase				: Union[str, Any]         =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 1_0






