




"""simple docstring"""







from statistics import mean

import numpy as np





def 							lowercase					(snake_case__   : list      ,       snake_case__   : list      ,       snake_case__   : list      ,       snake_case__   : int )     ->  list:


				'''simple docstring'''






				lowerCAmelCase  =				0
				# Number of processes finished
				lowerCAmelCase  =				0
				# Displays the finished process.
				# If it is 0, the performance is completed if it is 1, before the performance.
				lowerCAmelCase  =				[0] * no_of_process
				# List to include calculation results
				lowerCAmelCase  =				[0] * no_of_process

				# Sort by arrival time.
				lowerCAmelCase  =				[burst_time[i] for i in np.argsort(snake_case__ )]
				lowerCAmelCase  =				[process_name[i] for i in np.argsort(snake_case__ )]
				arrival_time.sort()

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

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

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

				return turn_around_time





def 							lowercase					(snake_case__   : list      ,       snake_case__   : list      ,       snake_case__   : list      ,       snake_case__   : int )     ->  list:


				'''simple docstring'''






				lowerCAmelCase  =				[0] * no_of_process
				for i in range(0      ,       snake_case__ ):
								lowerCAmelCase  =				turn_around_time[i] - burst_time[i]
				return waiting_time


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

		a      =							calculate_turn_around_time(
		    process_name, arrival_time, burst_time, no_of_process
		)
		a      =							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}""")











"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[
				    """decoder.version""",
				    """decoder.output_projection.weight""",
				    """_float_tensor""",
				    """decoder.embed_positions._float_tensor""",
				]
				for k in ignore_keys:
								state_dict.pop(snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				emb.weight.shape
				lowerCAmelCase  =				nn.Linear(snake_case__      ,       snake_case__      ,       bias=snake_case__ )
				lowerCAmelCase  =				emb.weight.data
				return lin_layer





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )
				lowerCAmelCase  =				Namespace(**checkpoint["""cfg"""]["""model"""] )
				lowerCAmelCase  =				checkpoint["""model"""]
				remove_ignore_keys_(snake_case__ )
				lowerCAmelCase  =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

				lowerCAmelCase  =				{key.replace("""decoder"""      ,       """model""" ): val for key, val in state_dict.items()}

				lowerCAmelCase  =				XGLMConfig(
				    vocab_size=snake_case__      ,       max_position_embeddings=args.max_target_positions      ,       num_layers=args.decoder_layers      ,       attention_heads=args.decoder_attention_heads      ,       ffn_dim=args.decoder_ffn_embed_dim      ,       d_model=args.decoder_embed_dim      ,       layerdrop=args.decoder_layerdrop      ,       dropout=args.dropout      ,       attention_dropout=args.attention_dropout      ,       activation_dropout=args.activation_dropout      ,       activation_function="""gelu"""      ,       scale_embedding=not args.no_scale_embedding      ,       tie_word_embeddings=args.share_decoder_input_output_embed      ,       )

				lowerCAmelCase  =				XGLMForCausalLM(snake_case__ )
				lowerCAmelCase  =				model.load_state_dict(snake_case__      ,       strict=snake_case__ )
				print(snake_case__ )
				lowerCAmelCase  =				make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		a      =							parser.parse_args()
		a      =							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





