
"""simple docstring"""






from statistics import mean

import numpy as np






def 						_snake_case       (	lowercase__		:  list      ,     lowercase__		:  list      ,     lowercase__		:  list      ,     lowercase__		:  int		)  ->				list:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  0
			# Number of processes finished
			lowerCAmelCase_    :Tuple			     =  0
			# Displays the finished process.
			# If it is 0, the performance is completed if it is 1, before the performance.
			lowerCAmelCase_    :Optional[int]			     =  [0] * no_of_process
			# List to include calculation results
			lowerCAmelCase_    :Union[str, Any]			     =  [0] * no_of_process

			# Sort by arrival time.
			lowerCAmelCase_    :Union[str, Any]			     =  [burst_time[i] for i in np.argsort(lowercase__		)]
			lowerCAmelCase_    :Union[str, Any]			     =  [process_name[i] for i in np.argsort(lowercase__		)]
			arrival_time.sort()

			while no_of_process > finished_process_count:
						lowerCAmelCase_    :List[Any]			     =  0
						while finished_process[i] == 1:
									i += 1
						if current_time < arrival_time[i]:
									lowerCAmelCase_    :str			     =  arrival_time[i]

						lowerCAmelCase_    :Any			     =  0
						# Index showing the location of the process being performed
						lowerCAmelCase_    :Tuple			     =  0
						# Saves the current response ratio.
						lowerCAmelCase_    :str			     =  0
						for i in range(0      ,     lowercase__		):
									if finished_process[i] == 0 and arrival_time[i] <= current_time:
												lowerCAmelCase_    :int			     =  (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
												    i
												]
									if response_ratio < temp:
												lowerCAmelCase_    :Tuple			     =  temp
												lowerCAmelCase_    :Tuple			     =  i

        # Calculate the turn around time
						lowerCAmelCase_    :Union[str, Any]			     =  current_time + burst_time[loc] - arrival_time[loc]
						current_time += burst_time[loc]
						# Indicates that the process has been performed.
						lowerCAmelCase_    :Dict			     =  1
						# Increase finished_process_count by 1
						finished_process_count += 1

			return turn_around_time






def 						_snake_case       (	lowercase__		:  list      ,     lowercase__		:  list      ,     lowercase__		:  list      ,     lowercase__		:  int		)  ->				list:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  [0] * no_of_process
			for i in range(0      ,     lowercase__		):
						lowerCAmelCase_    :Any			     =  turn_around_time[i] - burst_time[i]
			return waiting_time


if __name__ == "__main__":
				__UpperCAmelCase       =  5
				__UpperCAmelCase       =  ['A', 'B', 'C', 'D', 'E']
				__UpperCAmelCase       =  [1, 2, 3, 4, 5]
				__UpperCAmelCase       =  [1, 2, 3, 4, 5]

				__UpperCAmelCase       =  calculate_turn_around_time(
				    process_name, arrival_time, burst_time, no_of_process
				)
				__UpperCAmelCase       =  calculate_waiting_time(
				    process_name, turn_around_time, burst_time, no_of_process
				)

				print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
				for i in range(0, no_of_process):
								print(
								    F"""{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"""
								    F"""{turn_around_time[i]}\t\t\t{waiting_time[i]}"""
								)

				print(F"""average waiting time : {mean(waiting_time):.5f}""")
				print(F"""average turn around time : {mean(turn_around_time):.5f}""")








import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""conditional_detr"""
      _UpperCAmelCase				:    Optional[int]           =			["""past_key_values"""]
      _UpperCAmelCase				:    Optional[Any]           =			{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=3    ,  __magic_name__=3_0_0    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=True    ,  __magic_name__="relu"    ,  __magic_name__=2_5_6    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.02    ,  __magic_name__=1.0    ,  __magic_name__=False    ,  __magic_name__="sine"    ,  __magic_name__="resnet50"    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=1    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=0.25    ,  **__magic_name__    ,  ):
       if backbone_config is not None and use_timm_backbone:
        raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""      )

       if not use_timm_backbone:
        if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""      )
         lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
        elif isinstance(__magic_name__    ,  __magic_name__      ):
         lowerCamelCase		:	List[Any]							=      backbone_config.get("""model_type"""      )
         lowerCamelCase		:	Dict							=      CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase		:	str							=      config_class.from_dict(__magic_name__      )

       lowerCamelCase		:	Dict							=      use_timm_backbone
       lowerCamelCase		:	str							=      backbone_config
       lowerCamelCase		:	Tuple							=      num_channels
       lowerCamelCase		:	Dict							=      num_queries
       lowerCamelCase		:	Any							=      d_model
       lowerCamelCase		:	Optional[Any]							=      encoder_ffn_dim
       lowerCamelCase		:	List[str]							=      encoder_layers
       lowerCamelCase		:	Union[str, Any]							=      encoder_attention_heads
       lowerCamelCase		:	Any							=      decoder_ffn_dim
       lowerCamelCase		:	Dict							=      decoder_layers
       lowerCamelCase		:	Union[str, Any]							=      decoder_attention_heads
       lowerCamelCase		:	Dict							=      dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Union[str, Any]							=      activation_dropout
       lowerCamelCase		:	Optional[int]							=      activation_function
       lowerCamelCase		:	int							=      init_std
       lowerCamelCase		:	str							=      init_xavier_std
       lowerCamelCase		:	Tuple							=      encoder_layerdrop
       lowerCamelCase		:	str							=      decoder_layerdrop
       lowerCamelCase		:	Tuple							=      encoder_layers
       lowerCamelCase		:	Optional[int]							=      auxiliary_loss
       lowerCamelCase		:	Optional[Any]							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      backbone
       lowerCamelCase		:	Union[str, Any]							=      use_pretrained_backbone
       lowerCamelCase		:	str							=      dilation
       # Hungarian matcher
       lowerCamelCase		:	Optional[Any]							=      class_cost
       lowerCamelCase		:	Dict							=      bbox_cost
       lowerCamelCase		:	Tuple							=      giou_cost
       # Loss coefficients
       lowerCamelCase		:	Union[str, Any]							=      mask_loss_coefficient
       lowerCamelCase		:	Dict							=      dice_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      cls_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      bbox_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      giou_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      focal_alpha
       super().__init__(is_encoder_decoder=__magic_name__    ,  **__magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.encoder_attention_heads
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.d_model






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      copy.deepcopy(self.__dict__      )
       if self.backbone_config is not None:
        lowerCamelCase		:	Optional[int]							=      self.backbone_config.to_dict()
       lowerCamelCase		:	Optional[Any]							=      self.__class__.model_type
       return output





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-5






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




