



'''simple docstring'''

import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dataset							,		_SCREAMING_SNAKE_CASE					:						Dict[str, str]							)      ->						Union[str, Any]:







	"""simple docstring"""
	lowerCAmelCase   =     args.log_outputs
	lowerCAmelCase   =     """_""".join(args.dataset.split("""/"""							) + [args.config, args.split]							)

	# load metric
	lowerCAmelCase   =     load_metric("""wer"""							)
	lowerCAmelCase   =     load_metric("""cer"""							)

	# compute metrics
	lowerCAmelCase   =     wer.compute(references=result["""target"""]							,		predictions=result["""prediction"""]							)
	lowerCAmelCase   =     cer.compute(references=result["""target"""]							,		predictions=result["""prediction"""]							)

	# print & log results
	lowerCAmelCase   =     f'WER: {wer_result}\nCER: {cer_result}'
	print(_SCREAMING_SNAKE_CASE							)

	with open(f'{dataset_id}_eval_results.txt'							,		"""w"""							) as f:
		f.write(_SCREAMING_SNAKE_CASE							)

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		lowerCAmelCase   =     f'log_{dataset_id}_predictions.txt'
		lowerCAmelCase   =     f'log_{dataset_id}_targets.txt'

		with open(_SCREAMING_SNAKE_CASE							,		"""w"""							) as p, open(_SCREAMING_SNAKE_CASE							,		"""w"""							) as t:
			# mapping function to write output
			def write_to_file(_SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							):
				p.write(f'{i}' + """\n"""							)
				p.write(batch["""prediction"""] + """\n"""							)
				t.write(f'{i}' + """\n"""							)
				t.write(batch["""target"""] + """\n"""							)

			result.map(_SCREAMING_SNAKE_CASE							,		with_indices=_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     """[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	lowerCAmelCase   =     re.sub(_SCREAMING_SNAKE_CASE							,		""""""							,		text.lower()							)

	# In addition, we can normalize the target text, e.g. removing new lines characters etc...
	# note that order is important here!
	lowerCAmelCase   =     ["""\n\n""", """\n""", """   """, """  """]

	for t in token_sequences_to_ignore:
		lowerCAmelCase   =     """ """.join(text.split(_SCREAMING_SNAKE_CASE							)							)

	return text






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						List[str]:







	"""simple docstring"""
	# load dataset
	lowerCAmelCase   =     load_dataset(args.dataset							,		args.config							,		split=args.split							,		use_auth_token=_SCREAMING_SNAKE_CASE							)

	# for testing: only process the first two examples as a test
	# dataset = dataset.select(range(10))

	# load processor
	lowerCAmelCase   =     AutoFeatureExtractor.from_pretrained(args.model_id							)
	lowerCAmelCase   =     feature_extractor.sampling_rate

	# resample audio
	lowerCAmelCase   =     dataset.cast_column("""audio"""							,		Audio(sampling_rate=_SCREAMING_SNAKE_CASE							)							)

	# load eval pipeline
	if args.device is None:
		lowerCAmelCase   =     0 if torch.cuda.is_available() else -1
	lowerCAmelCase   =     pipeline("""automatic-speech-recognition"""							,		model=args.model_id							,		device=args.device							)

	# map function to decode audio
	def map_to_pred(_SCREAMING_SNAKE_CASE					:						str							):
		lowerCAmelCase   =     asr(
		    batch["""audio"""]["""array"""]							,		chunk_length_s=args.chunk_length_s							,		stride_length_s=args.stride_length_s							)

		lowerCAmelCase   =     prediction["""text"""]
		lowerCAmelCase   =     normalize_text(batch["""sentence"""]							)
		return batch

	# run inference on all examples
	lowerCAmelCase   =     dataset.map(_SCREAMING_SNAKE_CASE							,		remove_columns=dataset.column_names							)

	# compute and log_results
	# do not change function below
	log_results(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				UpperCAmelCase     		=   argparse.ArgumentParser()

				parser.add_argument(
				    '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
				)
				parser.add_argument(
				    '--dataset',
				    type=str,
				    required=True,
				    help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
				)
				parser.add_argument(
				    '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
				)
				parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
				parser.add_argument(
				    '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
				)
				parser.add_argument(
				    '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
				)
				parser.add_argument(
				    '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
				)
				parser.add_argument(
				    '--device',
				    type=int,
				    default=None,
				    help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
				)
				UpperCAmelCase     		=   parser.parse_args()

				main(args)




'''simple docstring'''

from __future__ import annotations

import pandas as pd






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	lowerCAmelCase   =     [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i]

	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	lowerCAmelCase   =     999_999_999
	lowerCAmelCase   =     0
	lowerCAmelCase   =     False

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

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

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

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

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

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

			if waiting_time[short] < 0:
				lowerCAmelCase   =     0

        # Increment time
		increment_time += 1
	return waiting_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i] + waiting_time[i]
	return turn_around_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						None:







	"""simple docstring"""
	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     total_waiting_time + waiting_time[i]
		lowerCAmelCase   =     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')
				UpperCAmelCase     		=   int(input())
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   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))
								UpperCAmelCase		,       UpperCAmelCase     		=   map(int, input().split())

				UpperCAmelCase     		=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				UpperCAmelCase     		=   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)