




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}






class lowerCAmelCase__	(     lowercase,	lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""focalnet"""
			def __init__(  self  ,      lowercase=224  ,      lowercase=4  ,      lowercase=3  ,      lowercase=96  ,      lowercase=False  ,      lowercase=[192, 384, 768, 768]  ,      lowercase=[2, 2, 6, 2]  ,      lowercase=[2, 2, 2, 2]  ,      lowercase=[3, 3, 3, 3]  ,      lowercase="gelu"  ,      lowercase=4.0  ,      lowercase=0.0  ,      lowercase=0.1  ,      lowercase=False  ,      lowercase=1E-4  ,      lowercase=False  ,      lowercase=False  ,      lowercase=False  ,      lowercase=0.02  ,      lowercase=1E-5  ,      lowercase=32  ,      lowercase=None  ,      lowercase=None  ,      **lowercase  ,      ):
							super().__init__(**lowercase  )

							_lowerCamelCase     :			Optional[int]    =    image_size
							_lowerCamelCase     :			Union[str, Any]    =    patch_size
							_lowerCamelCase     :			int    =    num_channels
							_lowerCamelCase     :			Any    =    embed_dim
							_lowerCamelCase     :			List[Any]    =    use_conv_embed
							_lowerCamelCase     :			List[str]    =    hidden_sizes
							_lowerCamelCase     :			int    =    depths
							_lowerCamelCase     :			Optional[int]    =    focal_levels
							_lowerCamelCase     :			Any    =    focal_windows
							_lowerCamelCase     :			str    =    hidden_act
							_lowerCamelCase     :			int    =    mlp_ratio
							_lowerCamelCase     :			Tuple    =    hidden_dropout_prob
							_lowerCamelCase     :			Any    =    drop_path_rate
							_lowerCamelCase     :			List[Any]    =    use_layerscale
							_lowerCamelCase     :			Optional[Any]    =    layerscale_value
							_lowerCamelCase     :			Union[str, Any]    =    use_post_layernorm
							_lowerCamelCase     :			Tuple    =    use_post_layernorm_in_modulation
							_lowerCamelCase     :			Union[str, Any]    =    normalize_modulator
							_lowerCamelCase     :			Union[str, Any]    =    initializer_range
							_lowerCamelCase     :			Any    =    layer_norm_eps
							_lowerCamelCase     :			int    =    encoder_stride
							_lowerCamelCase     :			Tuple    =    ['stem'] + [F'''stage{idx}''' for idx in range(1  ,      len(self.depths  ) + 1  )]
							_lowerCamelCase, _lowerCamelCase     :			Any    =    get_aligned_output_features_output_indices(
							    out_features=lowercase  ,      out_indices=lowercase  ,      stage_names=self.stage_names  )






from statistics import mean

import numpy as np







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						List[Any]   =						0
  # Number of processes finished
  __snake_case:						Union[str, Any]   =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						Dict   =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						Tuple   =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						int   =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						Any   =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    __snake_case:						Tuple   =						0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      __snake_case:						Any   =						arrival_time[i]

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

        # Calculate the turn around time
    __snake_case:						Optional[Any]   =						current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    __snake_case:						Optional[int]   =						1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						Union[str, Any]   =						[0] * no_of_process
  for i in range(0					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							5
      __UpperCAmelCase       :   Tuple				       =							["A", "B", "C", "D", "E"]
      __UpperCAmelCase       :   str				       =							[1, 2, 3, 4, 5]
      __UpperCAmelCase       :   Dict				       =							[1, 2, 3, 4, 5]

      __UpperCAmelCase       :   List[str]				       =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase       :   List[str]				       =							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}')






