






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= ["image_processor", "tokenizer"]
       lowerCamelCase							:				List[Any] 				= "BlipImageProcessor"
       lowerCamelCase							:				Any 				= "AutoTokenizer"



       def __init__(							self			,						a__			,						a__			,						a__    ) ->      Any:






            '''simple docstring'''





            super().__init__(a__			,						a__    )

            # add QFormer tokenizer
            __snake_case			:int          =						qformer_tokenizer



       def __call__(							self			,						a__ = None			,						a__ = None			,						a__ = True			,						a__ = False			,						a__ = None			,						a__ = None			,						a__ = 0			,						a__ = None			,						a__ = None			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = True			,						a__ = None			,						**a__			,						) ->      BatchFeature:






            '''simple docstring'''





            if images is None and text is None:
                 raise ValueError("""You have to specify at least images or text."""    )

            __snake_case			:str          =						BatchFeature()

            if text is not None:
                 __snake_case			:List[Any]          =						self.tokenizer(
                     text=a__			,						add_special_tokens=a__			,						padding=a__			,						truncation=a__			,						max_length=a__			,						stride=a__			,						pad_to_multiple_of=a__			,						return_attention_mask=a__			,						return_overflowing_tokens=a__			,						return_special_tokens_mask=a__			,						return_offsets_mapping=a__			,						return_token_type_ids=a__			,						return_length=a__			,						verbose=a__			,						return_tensors=a__			,						**a__			,						)
                 encoding.update(a__    )
                 __snake_case			:str          =						self.qformer_tokenizer(
                     text=a__			,						add_special_tokens=a__			,						padding=a__			,						truncation=a__			,						max_length=a__			,						stride=a__			,						pad_to_multiple_of=a__			,						return_attention_mask=a__			,						return_overflowing_tokens=a__			,						return_special_tokens_mask=a__			,						return_offsets_mapping=a__			,						return_token_type_ids=a__			,						return_length=a__			,						verbose=a__			,						return_tensors=a__			,						**a__			,						)
                 __snake_case			:Any          =						qformer_text_encoding.pop("""input_ids"""    )
                 __snake_case			:Union[str, Any]          =						qformer_text_encoding.pop("""attention_mask"""    )

            if images is not None:
                 __snake_case			:List[str]          =						self.image_processor(a__			,						return_tensors=a__    )
                 encoding.update(a__    )

            return encoding



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      Optional[Any]:






            '''simple docstring'''





            return self.tokenizer.batch_decode(*a__			,						**a__    )



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      Union[str, Any]:






            '''simple docstring'''





            return self.tokenizer.decode(*a__			,						**a__    )



       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.tokenizer.model_input_names
            __snake_case			:Any          =						self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )



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






            '''simple docstring'''





            if os.path.isfile(a__    ):
                 raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''    )
            os.makedirs(a__			,						exist_ok=a__    )
            __snake_case			:List[Any]          =						os.path.join(a__			,						"""qformer_tokenizer"""    )
            self.qformer_tokenizer.save_pretrained(a__    )
            return super().save_pretrained(a__			,						**a__    )



       @classmethod
       def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:str          =						AutoTokenizer.from_pretrained(a__			,						subfolder="""qformer_tokenizer"""    )
            __snake_case			:List[Any]          =						cls._get_arguments_from_pretrained(a__			,						**a__    )
            args.append(a__    )
            return cls(*a__    )










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()


