"""simple docstring"""




import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase											=  logging.get_logger(__name__)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =WavaVecaForSequenceClassification.from_pretrained(_lowerCamelCase       ,    config=_lowerCamelCase				)
							A__	     =downstream_dict["""projector.weight"""]
							A__	     =downstream_dict["""projector.bias"""]
							A__	     =downstream_dict["""model.post_net.linear.weight"""]
							A__	     =downstream_dict["""model.post_net.linear.bias"""]
							return model






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =WavaVecaForAudioFrameClassification.from_pretrained(_lowerCamelCase       ,    config=_lowerCamelCase				)
							A__	     =downstream_dict["""model.linear.weight"""]
							A__	     =downstream_dict["""model.linear.bias"""]
							return model






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =WavaVecaForXVector.from_pretrained(_lowerCamelCase       ,    config=_lowerCamelCase				)
							A__	     =downstream_dict["""connector.weight"""]
							A__	     =downstream_dict["""connector.bias"""]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
														A__	     =downstream_dict[
														    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
														]
														A__	     =downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

							A__	     =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
							A__	     =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
							A__	     =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
							A__	     =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
							A__	     =downstream_dict["""objective.W"""]
							return model






@torch.no_grad()
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =torch.load(_lowerCamelCase       ,    map_location='cpu'				)

							A__	     =checkpoint["""Downstream"""]

							A__	     =WavaVecaConfig.from_pretrained(_lowerCamelCase				)
							A__	     =WavaVecaFeatureExtractor.from_pretrained(
							    _lowerCamelCase       ,    return_attention_mask=_lowerCamelCase       ,    do_normalize=_lowerCamelCase				)

							A__	     =hf_config.architectures[0]
							if arch.endswith('ForSequenceClassification'				):
														A__	     =convert_classification(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase				)
							elif arch.endswith('ForAudioFrameClassification'				):
														A__	     =convert_diarization(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase				)
							elif arch.endswith('ForXVector'				):
														A__	     =convert_xvector(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase				)
							else:
														raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''				)

							if hf_config.use_weighted_layer_sum:
														A__	     =checkpoint["""Featurizer"""]["""weights"""]

							hf_feature_extractor.save_pretrained(_lowerCamelCase				)
							hf_model.save_pretrained(_lowerCamelCase				)


if __name__ == "__main__":
				__lowerCamelCase											=  argparse.ArgumentParser()
				parser.add_argument(
				    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
				)
				parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
				parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
				__lowerCamelCase											=  parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






"""simple docstring"""




from __future__ import annotations

import pandas as pd






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							0
							A__	     =							0
							A__	     =							999_999_999
							A__	     =							0
							A__	     =							False

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

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

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

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

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

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

																					if waiting_time[short] < 0:
																												A__	     =							0

        # Increment time
														increment_time += 1
							return waiting_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							0
							A__	     =							0
							for i in range(UpperCamelCase__				):
														A__	     =							total_waiting_time + waiting_time[i]
														A__	     =							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				,   __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)





