
from __future__ import annotations

import pandas as pd






def 						__magic_name__ (    __a			:       Union[str, Any]       ,		__a			:       Any       ,		__a			:       str						):



     '''simple docstring'''




     UpperCamelCase__				=				[0] * no_of_processes
     UpperCamelCase__				=				[0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(A__						):
          UpperCamelCase__				=				burst_time[i]

     UpperCamelCase__				=				0
     UpperCamelCase__				=				0
     UpperCamelCase__				=				999_999_999
     UpperCamelCase__				=				0
     UpperCamelCase__				=				False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(A__						):
               if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                    if remaining_time[j] < minm:
                         UpperCamelCase__				=				remaining_time[j]
                         UpperCamelCase__				=				j
                         UpperCamelCase__				=				True

          if not check:
               increment_time += 1
               continue
          remaining_time[short] -= 1

          UpperCamelCase__				=				remaining_time[short]
          if minm == 0:
               UpperCamelCase__				=				999_999_999

          if remaining_time[short] == 0:
               complete += 1
               UpperCamelCase__				=				False

               # Find finish time of current process
               UpperCamelCase__				=				increment_time + 1

               # Calculate waiting time
               UpperCamelCase__				=				finish_time - arrival_time[short]
               UpperCamelCase__				=				finar - burst_time[short]

               if waiting_time[short] < 0:
                    UpperCamelCase__				=				0

        # Increment time
          increment_time += 1
     return waiting_time






def 						__magic_name__ (    __a			:       List[str]       ,		__a			:       str       ,		__a			:       str						):



     '''simple docstring'''




     UpperCamelCase__				=				[0] * no_of_processes
     for i in range(A__						):
          UpperCamelCase__				=				burst_time[i] + waiting_time[i]
     return turn_around_time






def 						__magic_name__ (    __a			:       Optional[Any]       ,		__a			:       Tuple       ,		__a			:       List[Any]						):



     '''simple docstring'''




     UpperCamelCase__				=				0
     UpperCamelCase__				=				0
     for i in range(A__						):
          UpperCamelCase__				=				total_waiting_time + waiting_time[i]
          UpperCamelCase__				=				total_turn_around_time + turn_around_time[i]
     print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}"						)
     print("""Average turn around time ="""       ,		total_turn_around_time / no_of_processes						)


if __name__ == "__main__":
    print('''Enter how many process you want to analyze''')
    lowerCamelCase_							=     int(input())
    lowerCamelCase_							=     [0] * no_of_processes
    lowerCamelCase_							=     [0] * no_of_processes
    lowerCamelCase_							=     list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
        lowerCamelCase_							=     map(int, input().split())

    lowerCamelCase_							=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    lowerCamelCase_							=     burst_time
    lowerCamelCase_							=     no_of_processes
    lowerCamelCase_							=     waiting_time
    lowerCamelCase_							=     calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    lowerCamelCase_							=     pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            '''Process''',
            '''BurstTime''',
            '''ArrivalTime''',
            '''WaitingTime''',
            '''TurnAroundTime''',
        ],
    )

    # Printing the dataFrame
    pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
    print(fcfs)












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           'decoder.project_in_dim.weight': 'decoder.project_in.weight',
           'decoder.project_out_dim.weight': 'decoder.project_out.weight',
           'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

       # Check results
       Path(A__				).mkdir(exist_ok=A__				)
       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       str									=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


