






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCamelCase__							=     logging.get_logger(__name__)

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

lowerCamelCase__							=     {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase__							=     {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				str 				= VOCAB_FILES_NAMES
       lowerCamelCase							:				Tuple 				= PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase							:				Tuple 				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase							:				Tuple 				= ["input_ids", "attention_mask"]
       lowerCamelCase							:				Any 				= RobertaTokenizer



       def __init__(							self			,						a__=None			,						a__=None			,						a__=None			,						a__="replace"			,						a__="<s>"			,						a__="</s>"			,						a__="</s>"			,						a__="<s>"			,						a__="<unk>"			,						a__="<pad>"			,						a__="<mask>"			,						a__=False			,						a__=True			,						**a__			,						) ->      Dict:






            '''simple docstring'''





            super().__init__(
                a__			,						a__			,						tokenizer_file=a__			,						errors=a__			,						bos_token=a__			,						eos_token=a__			,						sep_token=a__			,						cls_token=a__			,						unk_token=a__			,						pad_token=a__			,						mask_token=a__			,						add_prefix_space=a__			,						trim_offsets=a__			,						**a__			,						)

            __snake_case			:Optional[int]          =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
            if pre_tok_state.get("""add_prefix_space"""			,						a__    ) != add_prefix_space:
                 __snake_case			:str          =						getattr(a__			,						pre_tok_state.pop("""type"""    )    )
                 __snake_case			:Optional[Any]          =						add_prefix_space
                 __snake_case			:str          =						pre_tok_class(**a__    )

            __snake_case			:List[Any]          =						add_prefix_space

            __snake_case			:Optional[Any]          =						"""post_processor"""
            __snake_case			:Dict          =						getattr(self.backend_tokenizer			,						a__			,						a__    )
            if tokenizer_component_instance:
                 __snake_case			:Dict          =						json.loads(tokenizer_component_instance.__getstate__()    )

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                      __snake_case			:Tuple          =						tuple(state["""sep"""]    )
                 if "cls" in state:
                      __snake_case			:Optional[Any]          =						tuple(state["""cls"""]    )

                 __snake_case			:int          =						False

                 if state.get("""add_prefix_space"""			,						a__    ) != add_prefix_space:
                      __snake_case			:Tuple          =						add_prefix_space
                      __snake_case			:Dict          =						True

                 if state.get("""trim_offsets"""			,						a__    ) != trim_offsets:
                      __snake_case			:Optional[Any]          =						trim_offsets
                      __snake_case			:Optional[int]          =						True

                 if changes_to_apply:
                      __snake_case			:List[str]          =						getattr(a__			,						state.pop("""type"""    )    )
                      __snake_case			:Optional[Any]          =						component_class(**a__    )
                      setattr(self.backend_tokenizer			,						a__			,						a__    )



       @property
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            if self._mask_token is None:
                 if self.verbose:
                      logger.error("""Using mask_token, but it is not set yet."""    )
                 return None
            return str(self._mask_token    )



       @mask_token.setter
       def 					__lowercase						(							self			,						a__    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						AddedToken(a__			,						lstrip=a__			,						rstrip=a__    ) if isinstance(a__			,						a__    ) else value
            __snake_case			:Tuple          =						value



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      BatchEncoding:






            '''simple docstring'''





            __snake_case			:List[Any]          =						kwargs.get("""is_split_into_words"""			,						a__    )
            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(*a__			,						**a__    )



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      BatchEncoding:






            '''simple docstring'''





            __snake_case			:Any          =						kwargs.get("""is_split_into_words"""			,						a__    )

            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(*a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      Tuple[str]:






            '''simple docstring'''





            __snake_case			:List[str]          =						self._tokenizer.model.save(a__			,						name=a__    )
            return tuple(a__    )



       def 					__lowercase						(							self			,						a__			,						a__=None    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:str          =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                 return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



       def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      List[int]:






            '''simple docstring'''





            __snake_case			:str          =						[self.sep_token_id]
            __snake_case			:Dict          =						[self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep    ) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]










from __future__ import annotations

import time

import numpy as np

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




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__			,						) ->      None:






            '''simple docstring'''





            __snake_case			:Dict          =						claim_vector
            __snake_case			:Optional[int]          =						allocated_resources_table
            __snake_case			:Optional[int]          =						maximum_claim_table



       def 					__lowercase						(							self    ) ->      list[int]:






            '''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    ) ->      list[int]:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      list[list[int]]:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      dict[int, list[int]]:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						**a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.__need()
            __snake_case			:List[Any]          =						self.__allocated_resources_table
            __snake_case			:str          =						self.__available_resources()
            __snake_case			: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:
                 __snake_case			:Dict          =						False
                 for each_need in need_list:
                      __snake_case			:Dict          =						True
                      for index, need in enumerate(a__    ):
                           if need > available_resources[index]:
                                __snake_case			:Dict          =						False
                                break
                      if execution:
                           __snake_case			:Any          =						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:
                                     __snake_case			:List[str]          =						original_need_index
                           print(F'''Process {process_number + 1} is executing.'''    )
                           # remove the process run from stack
                           need_list.remove(a__    )
                           # update available/freed resources stack
                           __snake_case			:Tuple          =						np.array(a__    ) + np.array(
                               alloc_resources_table[process_number]    )
                           print(
                               """Updated available resource stack for processes: """
                               + """ """.join([str(a__    ) 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    ) ->      Dict:






            '''simple docstring'''





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


if __name__ == "__main__":
     import doctest

     doctest.testmod()


