
'''simple docstring'''

from __future__ import annotations

import time

import numpy as np

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


class      a_     :




       def __init__(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      ):
         _lowerCAmelCase				:     List[Any]       =    claim_vector
         _lowerCAmelCase				:     Tuple       =    allocated_resources_table
         _lowerCAmelCase				:     Dict       =    maximum_claim_table




       def 							__UpperCamelCase				(   self     ):
         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     ):
         return np.array(self.__claim_vector     ) - np.array(
             self.__processes_resource_summation()     )




       def 							__UpperCamelCase				(   self     ):
         return [
             list(np.array(self.__maximum_claim_table[i]     ) - np.array(__snake_case     )     )
             for i, allocated_resource in enumerate(self.__allocated_resources_table     )
         ]




       def 							__UpperCamelCase				(   self     ):
         return {self.__need().index(__snake_case     ): i for i in self.__need()}




       def 							__UpperCamelCase				(   self      ,      **snake_case_     ):
         _lowerCAmelCase				:     Tuple       =    self.__need()
         _lowerCAmelCase				:     str       =    self.__allocated_resources_table
         _lowerCAmelCase				:     Tuple       =    self.__available_resources()
         _lowerCAmelCase				:     Dict       =    self.__need_index_manager()
         for kw, val in kwargs.items():
           if kw and val is True:
             self.__pretty_data()
         print("""_""" * 5_0 + """\n"""     )
         while need_list:
           _lowerCAmelCase				:     Union[str, Any]       =    False
           for each_need in need_list:
             _lowerCAmelCase				:     Dict       =    True
             for index, need in enumerate(__snake_case     ):
               if need > available_resources[index]:
                 _lowerCAmelCase				:     str       =    False
                 break
             if execution:
               _lowerCAmelCase				:     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:
                   _lowerCAmelCase				:     Optional[int]       =    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
               _lowerCAmelCase				:     Any       =    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 							__UpperCamelCase				(   self     ):
         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()





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			List[str]  				=			{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git_vision_model'





			def __init__(		self					,  __snake_case=768					,  __snake_case=3072					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3					,  __snake_case=224					,  __snake_case=16					,  __snake_case="quick_gelu"					,  __snake_case=1e-5					,  __snake_case=0.0					,  __snake_case=0.02					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =num_channels
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from GITConfig
						if config_dict.get('model_type'					) == "git":
									__a        =config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls					,  'model_type'					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git'





			def __init__(		self					,  __snake_case=None					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=6					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1024					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=True					,  __snake_case=False					,  __snake_case=101					,  __snake_case=102					,  __snake_case=None					,  **__snake_case					,  )   -> Optional[int]:




						'''simple docstring'''
						super().__init__(bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  pad_token_id=__snake_case					,  **__snake_case					)

						if vision_config is None:
									__a        ={}
									logger.info('vision_config is None. initializing the GitVisionConfig with default values.'					)

						__a        =GitVisionConfig(**__snake_case					)
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =position_embedding_type
						__a        =use_cache
						__a        =tie_word_embeddings
						__a        =num_image_with_embedding

						__a        =bos_token_id
						__a        =eos_token_id





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.__class__.model_type
						return output






