






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			snake_case__   (							nn.Module):

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int
       lowerCamelCase							:				float 				= 0.0
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				bool 				= True
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				jnp.dtype 				= jnp.floataa



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






            '''simple docstring'''





            __snake_case			:List[str]          =						[]
            __snake_case			:Union[str, Any]          =						[]

            for i in range(self.num_layers    ):
                 __snake_case			:List[str]          =						self.in_channels if i == 0 else self.out_channels

                 __snake_case			:Any          =						FlaxResnetBlockaD(
                     in_channels=lowerCAmelCase_			,						out_channels=self.out_channels			,						dropout_prob=self.dropout			,						dtype=self.dtype			,						)
                 resnets.append(lowerCAmelCase_    )

                 __snake_case			:Optional[Any]          =						FlaxTransformeraDModel(
                     in_channels=self.out_channels			,						n_heads=self.num_attention_heads			,						d_head=self.out_channels // self.num_attention_heads			,						depth=1			,						use_linear_projection=self.use_linear_projection			,						only_cross_attention=self.only_cross_attention			,						use_memory_efficient_attention=self.use_memory_efficient_attention			,						dtype=self.dtype			,						)
                 attentions.append(lowerCAmelCase_    )

            __snake_case			:Optional[Any]          =						resnets
            __snake_case			:List[Any]          =						attentions

            if self.add_downsample:
                 __snake_case			:Optional[Any]          =						FlaxDownsampleaD(self.out_channels			,						dtype=self.dtype    )



       def __call__(							self			,						a__			,						a__			,						a__			,						a__=True    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:int          =						()

            for resnet, attn in zip(self.resnets			,						self.attentions    ):
                 __snake_case			:Dict          =						resnet(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )
                 __snake_case			:List[Any]          =						attn(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )
                 output_states += (hidden_states,)

            if self.add_downsample:
                 __snake_case			:List[Any]          =						self.downsamplers_a(lowerCAmelCase_    )
                 output_states += (hidden_states,)

            return hidden_states, output_states




class 			snake_case__   (							nn.Module):

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int
       lowerCamelCase							:				float 				= 0.0
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				bool 				= True
       lowerCamelCase							:				jnp.dtype 				= jnp.floataa



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






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						[]

            for i in range(self.num_layers    ):
                 __snake_case			:Union[str, Any]          =						self.in_channels if i == 0 else self.out_channels

                 __snake_case			:Any          =						FlaxResnetBlockaD(
                     in_channels=lowerCAmelCase_			,						out_channels=self.out_channels			,						dropout_prob=self.dropout			,						dtype=self.dtype			,						)
                 resnets.append(lowerCAmelCase_    )
            __snake_case			:Tuple          =						resnets

            if self.add_downsample:
                 __snake_case			:Optional[Any]          =						FlaxDownsampleaD(self.out_channels			,						dtype=self.dtype    )



       def __call__(							self			,						a__			,						a__			,						a__=True    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						()

            for resnet in self.resnets:
                 __snake_case			:Optional[Any]          =						resnet(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )
                 output_states += (hidden_states,)

            if self.add_downsample:
                 __snake_case			:Union[str, Any]          =						self.downsamplers_a(lowerCAmelCase_    )
                 output_states += (hidden_states,)

            return hidden_states, output_states




class 			snake_case__   (							nn.Module):

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int
       lowerCamelCase							:				int
       lowerCamelCase							:				float 				= 0.0
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				bool 				= True
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				jnp.dtype 				= jnp.floataa



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






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						[]
            __snake_case			:Optional[Any]          =						[]

            for i in range(self.num_layers    ):
                 __snake_case			:Optional[int]          =						self.in_channels if (i == self.num_layers - 1) else self.out_channels
                 __snake_case			:Tuple          =						self.prev_output_channel if i == 0 else self.out_channels

                 __snake_case			:int          =						FlaxResnetBlockaD(
                     in_channels=resnet_in_channels + res_skip_channels			,						out_channels=self.out_channels			,						dropout_prob=self.dropout			,						dtype=self.dtype			,						)
                 resnets.append(lowerCAmelCase_    )

                 __snake_case			:Dict          =						FlaxTransformeraDModel(
                     in_channels=self.out_channels			,						n_heads=self.num_attention_heads			,						d_head=self.out_channels // self.num_attention_heads			,						depth=1			,						use_linear_projection=self.use_linear_projection			,						only_cross_attention=self.only_cross_attention			,						use_memory_efficient_attention=self.use_memory_efficient_attention			,						dtype=self.dtype			,						)
                 attentions.append(lowerCAmelCase_    )

            __snake_case			:int          =						resnets
            __snake_case			:Any          =						attentions

            if self.add_upsample:
                 __snake_case			:List[str]          =						FlaxUpsampleaD(self.out_channels			,						dtype=self.dtype    )



       def __call__(							self			,						a__			,						a__			,						a__			,						a__			,						a__=True    ) ->      Optional[int]:






            '''simple docstring'''





            for resnet, attn in zip(self.resnets			,						self.attentions    ):
                 # pop res hidden states
                 __snake_case			:str          =						res_hidden_states_tuple[-1]
                 __snake_case			:Optional[Any]          =						res_hidden_states_tuple[:-1]
                 __snake_case			:List[Any]          =						jnp.concatenate((hidden_states, res_hidden_states)			,						axis=-1    )

                 __snake_case			:Dict          =						resnet(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )
                 __snake_case			:int          =						attn(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )

            if self.add_upsample:
                 __snake_case			:Tuple          =						self.upsamplers_a(lowerCAmelCase_    )

            return hidden_states




class 			snake_case__   (							nn.Module):

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int
       lowerCamelCase							:				int
       lowerCamelCase							:				float 				= 0.0
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				bool 				= True
       lowerCamelCase							:				jnp.dtype 				= jnp.floataa



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:str          =						[]

            for i in range(self.num_layers    ):
                 __snake_case			:Tuple          =						self.in_channels if (i == self.num_layers - 1) else self.out_channels
                 __snake_case			:List[Any]          =						self.prev_output_channel if i == 0 else self.out_channels

                 __snake_case			:Any          =						FlaxResnetBlockaD(
                     in_channels=resnet_in_channels + res_skip_channels			,						out_channels=self.out_channels			,						dropout_prob=self.dropout			,						dtype=self.dtype			,						)
                 resnets.append(lowerCAmelCase_    )

            __snake_case			:Optional[int]          =						resnets

            if self.add_upsample:
                 __snake_case			:Any          =						FlaxUpsampleaD(self.out_channels			,						dtype=self.dtype    )



       def __call__(							self			,						a__			,						a__			,						a__			,						a__=True    ) ->      Union[str, Any]:






            '''simple docstring'''





            for resnet in self.resnets:
                 # pop res hidden states
                 __snake_case			:List[str]          =						res_hidden_states_tuple[-1]
                 __snake_case			:int          =						res_hidden_states_tuple[:-1]
                 __snake_case			:int          =						jnp.concatenate((hidden_states, res_hidden_states)			,						axis=-1    )

                 __snake_case			:Union[str, Any]          =						resnet(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )

            if self.add_upsample:
                 __snake_case			:Optional[int]          =						self.upsamplers_a(lowerCAmelCase_    )

            return hidden_states




class 			snake_case__   (							nn.Module):

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				float 				= 0.0
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				int 				= 1
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				jnp.dtype 				= jnp.floataa



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






            '''simple docstring'''





            __snake_case			:Any          =						[
                FlaxResnetBlockaD(
                    in_channels=self.in_channels			,						out_channels=self.in_channels			,						dropout_prob=self.dropout			,						dtype=self.dtype			,						)
            ]

            __snake_case			:int          =						[]

            for _ in range(self.num_layers    ):
                 __snake_case			:Dict          =						FlaxTransformeraDModel(
                     in_channels=self.in_channels			,						n_heads=self.num_attention_heads			,						d_head=self.in_channels // self.num_attention_heads			,						depth=1			,						use_linear_projection=self.use_linear_projection			,						use_memory_efficient_attention=self.use_memory_efficient_attention			,						dtype=self.dtype			,						)
                 attentions.append(lowerCAmelCase_    )

                 __snake_case			:List[str]          =						FlaxResnetBlockaD(
                     in_channels=self.in_channels			,						out_channels=self.in_channels			,						dropout_prob=self.dropout			,						dtype=self.dtype			,						)
                 resnets.append(lowerCAmelCase_    )

            __snake_case			:Tuple          =						resnets
            __snake_case			:Optional[Any]          =						attentions



       def __call__(							self			,						a__			,						a__			,						a__			,						a__=True    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:str          =						self.resnets[0](lowerCAmelCase_			,						lowerCAmelCase_    )
            for attn, resnet in zip(self.attentions			,						self.resnets[1:]    ):
                 __snake_case			:List[Any]          =						attn(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )
                 __snake_case			:Any          =						resnet(lowerCAmelCase_			,						lowerCAmelCase_			,						deterministic=lowerCAmelCase_    )

            return hidden_states










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()


