



"""simple docstring"""


from __future__ import annotations

import time

import numpy as np

snake_case_    					=	[8, 5, 9, 7]
snake_case_    					=	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
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			:int       ,  lowercase_			:List[Any]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Optional[int]       ,  )    ->      None:
            UpperCAmelCase					=			claim_vector
            UpperCAmelCase					=			allocated_resources_table
            UpperCAmelCase					=			maximum_claim_table






      def      UpperCAmelCase__					( self			:Optional[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			:Union[str, Any] )    ->      list[int]:
            return np.array(self.__claim_vector ) - np.array(
                self.__processes_resource_summation() )






      def      UpperCAmelCase__					( self			:str )    ->      list[list[int]]:
            return [
                list(np.array(self.__maximum_claim_table[i] ) - np.array(_lowerCamelCase ) )
                for i, allocated_resource in enumerate(self.__allocated_resources_table )
            ]






      def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      dict[int, list[int]]:
            return {self.__need().index(_lowerCamelCase ): i for i in self.__need()}






      def      UpperCAmelCase__					( self			:Optional[int]       ,  **lowercase_			:int )    ->      None:
            UpperCAmelCase					=			self.__need()
            UpperCAmelCase					=			self.__allocated_resources_table
            UpperCAmelCase					=			self.__available_resources()
            UpperCAmelCase					=			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					=			False
                  for each_need in need_list:
                        UpperCAmelCase					=			True
                        for index, need in enumerate(_lowerCamelCase ):
                              if need > available_resources[index]:
                                    UpperCAmelCase					=			False
                                    break
                        if execution:
                              UpperCAmelCase					=			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					=			original_need_index
                              print(f"""Process {process_number + 1} is executing.""" )
                              # remove the process run from stack
                              need_list.remove(_lowerCamelCase )
                              # update available/freed resources stack
                              UpperCAmelCase					=			np.array(_lowerCamelCase ) + np.array(
                                  alloc_resources_table[process_number] )
                              print(
                                  'Updated available resource stack for processes: '
                                  + ' '.join([str(_lowerCamelCase ) 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[int] )    ->      Any:
            print(' ' * 9 + 'Allocated Resource Table' )
            for item in self.__allocated_resources_table:
                  print(
                      f"""P{self.__allocated_resources_table.index(_lowerCamelCase ) + 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(_lowerCamelCase ) + 1}"""
                      + ' '.join(f"""{it:>8}""" for it in item )
                      + '\n' )
            print(
                'Current Usage by Active Processes: '
                + ' '.join(str(_lowerCamelCase ) for x in self.__claim_vector ) )
            print(
                'Initial Available Resources:       '
                + ' '.join(str(_lowerCamelCase ) for x in self.__available_resources() ) )
            time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()










"""simple docstring"""


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
						if (
						    any('norm' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												UpperCAmelCase					=			pt_tensor.transpose(2		,     3		,     1		,     0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase					=			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('weight',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('bias',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=42						):
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase					=			{k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						UpperCAmelCase					=			flax_model.init_weights(PRNGKey(lowercase_						)						)

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase					=			rename_key(lowercase_						)
												UpperCAmelCase					=			tuple(renamed_pt_key.split('.'						)						)

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
												UpperCAmelCase					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





