

"""simple docstring"""


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
  import timm


if is_torch_available():
  from torch import Tensor

class      __a							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[int]       = """pixel_values"""
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = False
     _SCREAMING_SNAKE_CASE						:str       = TimmBackboneConfig

     def __init__(						self	,    _a	,    **_a  )  ->      Optional[Any]:


           """simple docstring"""


           requires_backends(self	,    """timm"""  )
           super().__init__(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	config

           if config.backbone is None:
                 raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""  )

           if config.backbone not in timm.list_models():
                 raise ValueError(f'''backbone {config.backbone} is not supported by timm.'''  )

           if hasattr(_a	,    """out_features"""  ) and config.out_features is not None:
                 raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	getattr(_a	,    """use_pretrained_backbone"""	,    _a  )
           if pretrained is None:
                 raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""  )

           # We just take the final layer by default. This matches the default for the transformers models.
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	config.out_indices if getattr(_a	,    """out_indices"""	,    _a  ) is not None else (-1,)

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	timm.create_model(
               config.backbone	,    pretrained=_a	,    features_only=config.features_only	,    in_chans=config.num_channels	,    out_indices=_a	,    **_a	,    )
           # These are used to control the output of the model when called. If output_hidden_states is True, then
           # return_layers is modified to include all layers.
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self._backbone.return_layers
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	{layer["""module"""]: str(_a  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
           super()._init_backbone(_a  )

     @classmethod
     def 					_a			(						cls	,    _a	,    *_a	,    **_a  )  ->      Any:


           """simple docstring"""


           requires_backends(cls	,    ["""vision""", """timm"""]  )
           from ...models.timm_backbone import TimmBackboneConfig

           SCREAMING_SNAKE_CASE__   :      Dict      		=	kwargs.pop("""config"""	,    TimmBackboneConfig()  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	kwargs.pop("""use_timm_backbone"""	,    _a  )
           if not use_timm:
                 raise ValueError("""use_timm_backbone must be True for timm backbones"""  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	kwargs.pop("""num_channels"""	,    config.num_channels  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	kwargs.pop("""features_only"""	,    config.features_only  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	kwargs.pop("""use_pretrained_backbone"""	,    config.use_pretrained_backbone  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	kwargs.pop("""out_indices"""	,    config.out_indices  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	TimmBackboneConfig(
               backbone=_a	,    num_channels=_a	,    features_only=_a	,    use_pretrained_backbone=_a	,    out_indices=_a	,    )
           return super()._from_config(_a	,    **_a  )

     def 					_a			(						self	,    _a  )  ->      List[Any]:


           """simple docstring"""


           pass





     def 					_a			(						self	,    _a	,    _a=None	,    _a=None	,    _a=None	,    **_a  )  ->      Union[BackboneOutput, Tuple[Tensor, ...]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	return_dict if return_dict is not None else self.config.use_return_dict
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	output_attentions if output_attentions is not None else self.config.output_attentions

           if output_attentions:
                 raise ValueError("""Cannot output attentions for timm backbones at the moment"""  )

           if output_hidden_states:
                 # We modify the return layers to include all the stages of the backbone
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self._all_layers
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	self._backbone(_a	,    **_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	self._return_layers
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tuple(hidden_states[i] for i in self.out_indices  )
           else:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self._backbone(_a	,    **_a  )
                 SCREAMING_SNAKE_CASE__   :      Any      		=	None

           SCREAMING_SNAKE_CASE__   :      str      		=	tuple(_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tuple(_a  ) if hidden_states is not None else None

           if not return_dict:
                 SCREAMING_SNAKE_CASE__   :      Any      		=	(feature_maps,)
                 if output_hidden_states:
                       SCREAMING_SNAKE_CASE__   :      Any      		=	output + (hidden_states,)
                 return output

           return BackboneOutput(feature_maps=_a	,    hidden_states=_a	,    attentions=_a  )







"""simple docstring"""


from __future__ import annotations

import time

import numpy as np

a					:Optional[Any]    = [8, 5, 9, 7]
a					:List[Any]    = [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a					:int    = [
    [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	,    _a	,    _a	,    _a	,    )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	claim_vector
           SCREAMING_SNAKE_CASE__   :      Any      		=	allocated_resources_table
           SCREAMING_SNAKE_CASE__   :      Any      		=	maximum_claim_table

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


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self	,    **_a  )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.__need()
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.__allocated_resources_table
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.__available_resources()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	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:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	False
                 for each_need in need_list:
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	True
                       for index, need in enumerate(_a  ):
                             if need > available_resources[index]:
                                   SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	False
                                   break
                       if execution:
                             SCREAMING_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:
                                         SCREAMING_SNAKE_CASE__   :      Tuple      		=	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
                             SCREAMING_SNAKE_CASE__   :      Dict      		=	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 					_a			(						self  )  ->      Any:


           """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()




