



from __future__ import annotations







def       lowerCamelCase_  (    _a       :						list[int]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Dict  		=					[0] * len(_a    )
    for i in range(1   ,		len(_a    )    ):
        UpperCAmelCase_     :       Any  		=					duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times







def       lowerCamelCase_  (    _a       :						list[int]   ,		_a       :						list[int]    ):



    '''simple docstring'''




    return [
        duration_time + waiting_times[i]
        for i, duration_time in enumerate(_a    )
    ]







def       lowerCamelCase_  (    _a       :						list[int]    ):



    '''simple docstring'''




    return sum(_a    ) / len(_a    )







def       lowerCamelCase_  (    _a       :						list[int]    ):



    '''simple docstring'''




    return sum(_a    ) / len(_a    )


if __name__ == "__main__":
      # process id's
      UpperCamelCase_									=   [1, 2, 3]

      # ensure that we actually have processes
      if len(processes) == 0:
            print('''Zero amount of processes''')
            raise SystemExit(0)

      # duration time of all processes
      UpperCamelCase_									=   [19, 8, 9]

      # ensure we can match each id to a duration time
      if len(duration_times) != len(processes):
            print('''Unable to match all id\'s with their duration time''')
            raise SystemExit(0)

      # get the waiting times and the turnaround times
      UpperCamelCase_									=   calculate_waiting_times(duration_times)
      UpperCamelCase_									=   calculate_turnaround_times(duration_times, waiting_times)

      # get the average times
      UpperCamelCase_									=   calculate_average_waiting_time(waiting_times)
      UpperCamelCase_									=   calculate_average_turnaround_time(turnaround_times)

      # print all the results
      print('''Process ID\tDuration Time\tWaiting Time\tTurnaround Time''')
      for i, process in enumerate(processes):
            print(
                F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
                F"{turnaround_times[i]}"
            )
      print(F"Average waiting time = {average_waiting_time}")
      print(F"Average turn around time = {average_turnaround_time}")





from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
      import detectrona







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[Any]       =						"layoutlmv2"


   def __init__(    self:     Tuple  ,lowerCamelCase_:     Dict=30522  ,lowerCamelCase_:     str=768  ,lowerCamelCase_:     Any=12  ,lowerCamelCase_:     Tuple=12  ,lowerCamelCase_:     int=3072  ,lowerCamelCase_:     int="gelu"  ,lowerCamelCase_:     Dict=0.1  ,lowerCamelCase_:     List[Any]=0.1  ,lowerCamelCase_:     List[Any]=512  ,lowerCamelCase_:     Tuple=2  ,lowerCamelCase_:     Dict=0.0_2  ,lowerCamelCase_:     Dict=1e-12  ,lowerCamelCase_:     Dict=0  ,lowerCamelCase_:     List[str]=1024  ,lowerCamelCase_:     Optional[Any]=128  ,lowerCamelCase_:     List[Any]=32  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     Dict=256  ,lowerCamelCase_:     int=64  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Tuple=[7, 7, 256]  ,lowerCamelCase_:     Union[str, Any]=128  ,lowerCamelCase_:     Any=128  ,lowerCamelCase_:     int=True  ,lowerCamelCase_:     Union[str, Any]=True  ,lowerCamelCase_:     Optional[int]=False  ,lowerCamelCase_:     List[str]=None  ,**lowerCamelCase_:     str  ,)	->      Any:
       super().__init__(
           vocab_size=lowerCamelCase_  ,hidden_size=lowerCamelCase_  ,num_hidden_layers=lowerCamelCase_  ,num_attention_heads=lowerCamelCase_  ,intermediate_size=lowerCamelCase_  ,hidden_act=lowerCamelCase_  ,hidden_dropout_prob=lowerCamelCase_  ,attention_probs_dropout_prob=lowerCamelCase_  ,max_position_embeddings=lowerCamelCase_  ,type_vocab_size=lowerCamelCase_  ,initializer_range=lowerCamelCase_  ,layer_norm_eps=lowerCamelCase_  ,pad_token_id=lowerCamelCase_  ,**lowerCamelCase_  ,)
       UpperCAmelCase_     :       Optional[int]  		=					max_ad_position_embeddings
       UpperCAmelCase_     :       Optional[int]  		=					max_rel_pos
       UpperCAmelCase_     :       Optional[int]  		=					rel_pos_bins
       UpperCAmelCase_     :       List[str]  		=					fast_qkv
       UpperCAmelCase_     :       int  		=					max_rel_ad_pos
       UpperCAmelCase_     :       int  		=					rel_ad_pos_bins
       UpperCAmelCase_     :       Optional[Any]  		=					convert_sync_batchnorm
       UpperCAmelCase_     :       str  		=					image_feature_pool_shape
       UpperCAmelCase_     :       List[Any]  		=					coordinate_size
       UpperCAmelCase_     :       int  		=					shape_size
       UpperCAmelCase_     :       Optional[Any]  		=					has_relative_attention_bias
       UpperCAmelCase_     :       str  		=					has_spatial_attention_bias
       UpperCAmelCase_     :       Tuple  		=					has_visual_segment_embedding
       UpperCAmelCase_     :       Optional[Any]  		=					(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )


   @classmethod
   def       A__    (    self:     Any							)	->      Dict:
       return {
           "MODEL.MASK_ON": True,
           "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
           "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
           "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
           "MODEL.POST_NMS_TOPK_TEST": 1000,
           "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
           "MODEL.ROI_HEADS.NUM_CLASSES": 5,
           "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
           "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
           "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
           "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 101,
           "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 32,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }






   def       A__    (    self:     Tuple							)	->      int:
       UpperCAmelCase_     :       Any  		=					detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
           UpperCAmelCase_     :       Optional[int]  		=					k.split("""."""							)
           UpperCAmelCase_     :       List[str]  		=					detectrona_config
           for attribute in attributes[:-1]:
               UpperCAmelCase_     :       Optional[Any]  		=					getattr(lowerCamelCase_  ,lowerCamelCase_							)
           setattr(lowerCamelCase_  ,attributes[-1]  ,lowerCamelCase_							)

       return detectrona_config
