





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=						logging.get_logger("""transformers.models.speecht5""")


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	hf_model.apply_weight_norm()

	snake_case_						:     List[Any]     =		checkpoint['input_conv.weight_g']
	snake_case_						:     Dict     =		checkpoint['input_conv.weight_v']
	snake_case_						:     Union[str, Any]     =		checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates				)				):
		snake_case_						:     List[str]     =		checkpoint[f"""upsamples.{i}.1.weight_g"""]
		snake_case_						:     Tuple     =		checkpoint[f"""upsamples.{i}.1.weight_v"""]
		snake_case_						:     List[str]     =		checkpoint[f"""upsamples.{i}.1.bias"""]

	for i in range(len(config.upsample_rates				) * len(config.resblock_kernel_sizes				)				):
		for j in range(len(config.resblock_dilation_sizes				)				):
			snake_case_						:     Dict     =		checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
			snake_case_						:     Tuple     =		checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
			snake_case_						:     List[str]     =		checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

			snake_case_						:     str     =		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
			snake_case_						:     List[str]     =		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
			snake_case_						:     Optional[int]     =		checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

	snake_case_						:     List[Any]     =		checkpoint['output_conv.1.weight_g']
	snake_case_						:     Tuple     =		checkpoint['output_conv.1.weight_v']
	snake_case_						:     Union[str, Any]     =		checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a=None				,	__a=None				,	):
	if config_path is not None:
		snake_case_						:     Union[str, Any]     =		SpeechTaHifiGanConfig.from_pretrained(__a				)
	else:
		snake_case_						:     Optional[int]     =		SpeechTaHifiGanConfig()

	snake_case_						:     Any     =		SpeechTaHifiGan(__a				)

	snake_case_						:     Tuple     =		torch.load(__a				)
	load_weights(orig_checkpoint['model']['generator']				,	__a				,	__a				)

	snake_case_						:     int     =		np.load(__a				)
	snake_case_						:     Any     =		stats[0].reshape(-1				)
	snake_case_						:     int     =		stats[1].reshape(-1				)
	snake_case_						:     List[Any]     =		torch.from_numpy(__a				).float()
	snake_case_						:     List[Any]     =		torch.from_numpy(__a				).float()

	model.save_pretrained(__a				)

	if repo_id:
		print('Pushing to the hub...'				)
		model.push_to_hub(__a				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
						parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
						)
						parser.add_argument(
						    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
						)

						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_hifigan_checkpoint(
						    args.checkpoint_path,
						    args.stats_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.push_to_hub,
						)








from __future__ import annotations

import pandas as pd


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Optional[Any]     =		[0] * no_of_processes
	snake_case_						:     Tuple     =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		burst_time[i]

	snake_case_						:     Optional[Any]     =		0
	snake_case_						:     Dict     =		0
	snake_case_						:     Any     =		9_99_99_99_99
	snake_case_						:     Tuple     =		0
	snake_case_						:     List[Any]     =		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:
					snake_case_						:     str     =		remaining_time[j]
					snake_case_						:     Any     =		j
					snake_case_						:     List[str]     =		True

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

		snake_case_						:     Any     =		remaining_time[short]
		if minm == 0:
			snake_case_						:     Dict     =		9_99_99_99_99

		if remaining_time[short] == 0:
			complete += 1
			snake_case_						:     List[str]     =		False

			# Find finish time of current process
			snake_case_						:     List[str]     =		increment_time + 1

			# Calculate waiting time
			snake_case_						:     Any     =		finish_time - arrival_time[short]
			snake_case_						:     Any     =		finar - burst_time[short]

			if waiting_time[short] < 0:
				snake_case_						:     Optional[int]     =		0

        # Increment time
		increment_time += 1
	return waiting_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		[0] * no_of_processes
	for i in range(__a				):
		snake_case_						:     str     =		burst_time[i] + waiting_time[i]
	return turn_around_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		0
	snake_case_						:     Optional[Any]     =		0
	for i in range(__a				):
		snake_case_						:     int     =		total_waiting_time + waiting_time[i]
		snake_case_						:     Optional[Any]     =		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""")
						_SCREAMING_SNAKE_CASE 		=						int(input())
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						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))
												_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE 		=						map(int, input().split())

						_SCREAMING_SNAKE_CASE 		=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_SCREAMING_SNAKE_CASE 		=						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)

