'''simple docstring'''






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_	   =			{'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE_	   =			{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

SCREAMING_SNAKE_CASE_	   =			{
    '''AI-Sweden/gpt-sw3-126m''': 20_48,
    '''AI-Sweden/gpt-sw3-350m''': 20_48,
    '''AI-Sweden/gpt-sw3-1.6b''': 20_48,
    '''AI-Sweden/gpt-sw3-6.7b''': 20_48,
    '''AI-Sweden/gpt-sw3-20b''': 20_48,
}




class    a (       snake_case__				):



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







  def __init__(							self   ,    snake_case_   ,    snake_case_=False   ,    snake_case_=False   ,    snake_case_=False   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= {} if sp_model_kwargs is None else sp_model_kwargs

    __UpperCAmelCase:					str		= kwargs.get("""name_or_path"""					)
    if name_or_path is None:
      logger.warning(
          """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
          """ you are testing the model, this can safely be ignored"""					)
      __UpperCAmelCase:					Any		= 'None'

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    __UpperCAmelCase:					int		= '<|endoftext|>' if eos_token is None else eos_token
    __UpperCAmelCase:					Any		= '<unk>' if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
      __UpperCAmelCase:					Optional[int]		= unk_token if pad_token is None else pad_token
      __UpperCAmelCase:					List[Any]		= eos_token if bos_token is None else bos_token
    else:
      __UpperCAmelCase:					List[str]		= '<pad>' if pad_token is None else pad_token
      __UpperCAmelCase:					Optional[int]		= '<s>' if bos_token is None else bos_token

    super().__init__(
        do_lower_case=_A   ,    remove_space=_A   ,    keep_accents=_A   ,    bos_token=_A   ,    eos_token=_A   ,    unk_token=_A   ,    pad_token=_A   ,    sp_model_kwargs=self.sp_model_kwargs   ,    **_A   ,    )

    __UpperCAmelCase:					Union[str, Any]		= do_lower_case
    __UpperCAmelCase:					int		= remove_space
    __UpperCAmelCase:					Any		= keep_accents
    __UpperCAmelCase:					str		= vocab_file

    __UpperCAmelCase:					Optional[int]		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(_A					)

    # Used for whitespace normalization in input texts
    # fmt : off
    __UpperCAmelCase:					str		= {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    __UpperCAmelCase:					Union[str, Any]		= re.compile(
        F'''[{''.join(map(_A   ,    list(range(0   ,    9					)					) + list(range(11   ,    32					)					) + list(range(127   ,    160					)					) + [160, 173, 8203]					)					)}]'''					)







  def __getstate__(							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= self.__dict__.copy()
    __UpperCAmelCase:					Tuple		= None
    return state







  def __setstate__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= d

    # for backward compatibility
    if not hasattr(self   ,    """sp_model_kwargs"""					):
      __UpperCAmelCase:					List[Any]		= {}

    __UpperCAmelCase:					List[Any]		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(self.vocab_file					)







  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def    lowercase_  (							self					):

    '''simple docstring'''







    return len(self.sp_model					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.non_printing_characters_re.sub(""""""   ,    _A					)

    # Normalize whitespaces
    __UpperCAmelCase:					str		= ''.join([char if char not in self.whitespaces else """ """ for char in text]					)

    # NFC Unicode normalization
    __UpperCAmelCase:					Optional[Any]		= unicodedata.normalize("""NFC"""   ,    _A					)
    return text







  def    lowercase_  (							self   ,    snake_case_   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= self.preprocess_text(_A					)
    return self.sp_model.encode(_A   ,    out_type=_A					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.PieceToId(_A					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.IdToPiece(_A					)







  @staticmethod
  def    lowercase_  (							snake_case_					):

    '''simple docstring'''







    return out_string







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= []
    __UpperCAmelCase:					Optional[int]		= ''
    __UpperCAmelCase:					List[str]		= False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
        if not prev_is_special:
          out_string += " "

        out_string += self.sp_model.decode(_A					) + token
        __UpperCAmelCase:					Tuple		= True
        __UpperCAmelCase:					int		= []
      else:
        current_sub_tokens.append(_A					)
        __UpperCAmelCase:					List[str]		= False
    out_string += self.sp_model.decode(_A					)

    return out_string







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= {self.convert_ids_to_tokens(_A					): i for i in range(self.vocab_size					)}
    vocab.update(self.added_tokens_encoder					)
    return vocab







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

    '''simple docstring'''







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

    if os.path.abspath(self.vocab_file					) != os.path.abspath(_A					) and os.path.isfile(self.vocab_file					):
      copyfile(self.vocab_file   ,    _A					)
    elif not os.path.isfile(self.vocab_file					):
      with open(_A   ,    """wb"""					) as fi:
        __UpperCAmelCase:					Union[str, Any]		= self.sp_model.serialized_model_proto()
        fi.write(_A					)

    return (out_vocab_file,)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = False					):

    '''simple docstring'''







    if isinstance(_A   ,    _A					):
      __UpperCAmelCase:					Union[str, Any]		= self.preprocess_text(_A					)
      __UpperCAmelCase:					Optional[Any]		= self.sp_model.encode(_A					)
    else:
      __UpperCAmelCase:					Any		= [self.preprocess_text(_A					) for t in text]
      __UpperCAmelCase:					str		= self.sp_model.encode(_A					)

    if return_tensors is True or return_tensors == "pt":
      __UpperCAmelCase:					Dict		= torch.tensor(_A					)

    return token_ids







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.decode(_A					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
    __UpperCAmelCase:					Any		= (
        F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(_A					) + F'''{self.bos_token}Bot:'''
    )
    return self.encode(text=_A					)






'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

SCREAMING_SNAKE_CASE_	   =			[8, 5, 9, 7]
SCREAMING_SNAKE_CASE_	   =			[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE_	   =			[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					str		= claim_vector
    __UpperCAmelCase:					Optional[Any]		= allocated_resources_table
    __UpperCAmelCase:					List[str]		= maximum_claim_table







  def    lowercase_  (							self					):

    '''simple docstring'''







    return [
        sum(p_item[i] for p_item in self.__allocated_resources_table					)
        for i in range(len(self.__allocated_resources_table[0]					)					)
    ]







  def    lowercase_  (							self					):

    '''simple docstring'''







    return np.array(self.__claim_vector					) - np.array(
        self.__processes_resource_summation()					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    return [
        list(np.array(self.__maximum_claim_table[i]					) - np.array(snake_case_					)					)
        for i, allocated_resource in enumerate(self.__allocated_resources_table					)
    ]







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {self.__need().index(snake_case_					): i for i in self.__need()}







  def    lowercase_  (							self   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.__need()
    __UpperCAmelCase:					Optional[Any]		= self.__allocated_resources_table
    __UpperCAmelCase:					int		= self.__available_resources()
    __UpperCAmelCase:					List[Any]		= self.__need_index_manager()
    for kw, val in kwargs.items():
      if kw and val is True:
        self.__pretty_data()
    print("""_""" * 50 + """\n"""					)
    while need_list:
      __UpperCAmelCase:					Any		= False
      for each_need in need_list:
        __UpperCAmelCase:					Any		= True
        for index, need in enumerate(snake_case_					):
          if need > available_resources[index]:
            __UpperCAmelCase:					Tuple		= False
            break
        if execution:
          __UpperCAmelCase:					Optional[int]		= True
          # get the original index of the process from ind_ctrl db
          for original_need_index, need_clone in need_index_manager.items():
            if each_need == need_clone:
              __UpperCAmelCase:					Tuple		= original_need_index
          print(F'''Process {process_number + 1} is executing.'''					)
          # remove the process run from stack
          need_list.remove(snake_case_					)
          # update available/freed resources stack
          __UpperCAmelCase:					Dict		= np.array(snake_case_					) + np.array(
              alloc_resources_table[process_number]					)
          print(
              """Updated available resource stack for processes: """
              + """ """.join([str(snake_case_					) for x in available_resources]					)					)
          break
      if safe:
        print("""The process is in a safe state.\n"""					)
      else:
        print("""System in unsafe state. Aborting...\n"""					)
        break







  def    lowercase_  (							self					):

    '''simple docstring'''







    print(""" """ * 9 + """Allocated Resource Table"""					)
    for item in self.__allocated_resources_table:
      print(
          F'''P{self.__allocated_resources_table.index(snake_case_					) + 1}'''
          + """ """.join(F'''{it:>8}''' for it in item					)
          + """\n"""					)
    print(""" """ * 9 + """System Resource Table"""					)
    for item in self.__maximum_claim_table:
      print(
          F'''P{self.__maximum_claim_table.index(snake_case_					) + 1}'''
          + """ """.join(F'''{it:>8}''' for it in item					)
          + """\n"""					)
    print(
        """Current Usage by Active Processes: """
        + """ """.join(str(snake_case_					) for x in self.__claim_vector					)					)
    print(
        """Initial Available Resources:       """
        + """ """.join(str(snake_case_					) for x in self.__available_resources()					)					)
    time.sleep(1					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()