





'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


__lowerCAmelCase							:	Dict			=logging.get_logger(__name__)

__lowerCAmelCase							:	Dict			={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase							:	int			={
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

__lowerCAmelCase							:	Dict			={
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				VOCAB_FILES_NAMES
 __lowercase   		=				PRETRAINED_VOCAB_FILES_MAP
 __lowercase   		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase   		=				["""input_ids""", """attention_mask"""]
 __lowercase   		=				GPTaTokenizer
 def __init__(				self      :Optional[Any]						,       lowercase_      :str=None						,       lowercase_      :Optional[Any]=None						,       lowercase_      :str=None						,       lowercase_      :Optional[int]="<|endoftext|>"						,       lowercase_      :Union[str, Any]="<|endoftext|>"						,       lowercase_      :Optional[Any]="<|endoftext|>"						,       lowercase_      :Union[str, Any]=False						,       **lowercase_      :Union[str, Any]						,       )->							List[Any]:
     super().__init__(
         lowercase_						,       lowercase_						,       tokenizer_file=lowercase_						,       unk_token=lowercase_						,       bos_token=lowercase_						,       eos_token=lowercase_						,       add_prefix_space=lowercase_						,       **lowercase_						,       )

     A__				=			kwargs.pop("add_bos_token"						,       lowercase_							)

     A__				=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
     if pre_tok_state.get("add_prefix_space"						,       lowercase_							) != add_prefix_space:
         A__				=			getattr(lowercase_						,       pre_tok_state.pop("type"							)							)
         A__				=			add_prefix_space
         A__				=			pre_tok_class(**lowercase_							)

     A__				=			add_prefix_space
 def      UpperCAmelCase_							(				self      :Dict						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Any							)->							BatchEncoding:
     A__				=			kwargs.get("is_split_into_words"						,       lowercase_							)
     assert self.add_prefix_space or not is_split_into_words, (
         F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[int]						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Optional[int]							)->							BatchEncoding:
     A__				=			kwargs.get("is_split_into_words"						,       lowercase_							)

     assert self.add_prefix_space or not is_split_into_words, (
         F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :Optional[str] = None							)->							Tuple[str]:
     A__				=			self._tokenizer.model.save(lowercase_						,       name=lowercase_							)
     return tuple(lowercase_							)



 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :"Conversation"							)->							List[int]:
     A__				=			[]
     for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowercase_						,       add_special_tokens=lowercase_							) + [self.eos_token_id]							)

     if len(lowercase_							) > self.model_max_length:
         A__				=			input_ids[-self.model_max_length :]
     return input_ids










'''simple docstring'''

from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase							:	Optional[Any]			=[8, 5, 9, 7]
__lowerCAmelCase							:	Optional[int]			=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase							:	List[Any]			=[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :list[int]						,       lowercase_      :list[list[int]]						,       lowercase_      :list[list[int]]						,       )->							None:
     A__				=			claim_vector
     A__				=			allocated_resources_table
     A__				=			maximum_claim_table
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							list[int]:
     return [
         sum(p_item[i] for p_item in self.__allocated_resources_table							)
         for i in range(len(self.__allocated_resources_table[0]							)							)
     ]
 def      UpperCAmelCase_							(				self      :Tuple							)->							list[int]:
     return np.array(self.__claim_vector							) - np.array(
         self.__processes_resource_summation()							)
 def      UpperCAmelCase_							(				self      :Dict							)->							list[list[int]]:
     return [
         list(np.array(self.__maximum_claim_table[i]							) - np.array(lowercase_							)							)
         for i, allocated_resource in enumerate(self.__allocated_resources_table							)
     ]
 def      UpperCAmelCase_							(				self      :Any							)->							dict[int, list[int]]:
     return {self.__need().index(lowercase_							): i for i in self.__need()}
 def      UpperCAmelCase_							(				self      :Tuple						,       **lowercase_      :List[str]							)->							None:
     A__				=			self.__need()
     A__				=			self.__allocated_resources_table
     A__				=			self.__available_resources()
     A__				=			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:
         A__				=			False
         for each_need in need_list:
             A__				=			True
             for index, need in enumerate(lowercase_							):
                 if need > available_resources[index]:
                     A__				=			False
                     break
             if execution:
                 A__				=			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:
                         A__				=			original_need_index
                 print(F"Process {process_number + 1} is executing."							)
                 # remove the process run from stack
                 need_list.remove(lowercase_							)
                 # update available/freed resources stack
                 A__				=			np.array(lowercase_							) + np.array(
                     alloc_resources_table[process_number]							)
                 print(
                     "Updated available resource stack for processes: "
                     + " ".join([str(lowercase_							) 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      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     print(" " * 9 + "Allocated Resource Table"							)
     for item in self.__allocated_resources_table:
         print(
             F"P{self.__allocated_resources_table.index(lowercase_							) + 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(lowercase_							) + 1}"
             + " ".join(F"{it:>8}" for it in item							)
             + "\n"							)
     print(
         "Current Usage by Active Processes: "
         + " ".join(str(lowercase_							) for x in self.__claim_vector							)							)
     print(
         "Initial Available Resources:       "
         + " ".join(str(lowercase_							) for x in self.__available_resources()							)							)
     time.sleep(1							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



