






import comet  # From: unbabel-comet
import torch

import datasets


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		datasets.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      List[str]				=		"""\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
   author = \"Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon\",
   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
   month = nov,
   year = \"2020\",
   address = \"Online\",
   publisher = \"Association for Computational Linguistics\",
   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
   pages = \"2685--2702\",
}
"""

SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""

SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric('comet')
    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use
    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [0.19, 0.92]
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION			)
class     lowerCamelCase_					(   datasets.Metric			):







				def       A						(    self  ):
								"""simple docstring"""
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,       citation=_CITATION							,       homepage='''https://unbabel.github.io/COMET/html/index.html'''							,       inputs_description=_KWARGS_DESCRIPTION							,       features=datasets.Features(
								        {
								            '''sources''': datasets.Value('''string'''							,       id='''sequence'''  ),
								            '''predictions''': datasets.Value('''string'''							,       id='''sequence'''  ),
								            '''references''': datasets.Value('''string'''							,       id='''sequence'''  ),
								        }  )							,       codebase_urls=['''https://github.com/Unbabel/COMET''']							,       reference_urls=[
								        '''https://github.com/Unbabel/COMET''',
								        '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
								        '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
								    ]							,       )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								if self.config_name == "default":
												__magic_name__		:str   =   comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''  )  )
								else:
												__magic_name__		:Dict   =   comet.load_from_checkpoint(comet.download_model(self.config_name  )  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								if gpus is None:
												__magic_name__		:Optional[Any]   =   1 if torch.cuda.is_available() else 0
								__magic_name__		:List[Any]   =   {'''src''': sources, '''mt''': predictions, '''ref''': references}
								__magic_name__		:List[str]   =   [dict(zip(__lowerCAmelCase							,       __lowerCAmelCase  )  ) for t in zip(*data.values()  )]
								__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.scorer.predict(__lowerCAmelCase							,       gpus=__lowerCAmelCase							,       progress_bar=__lowerCAmelCase  )
								return {"mean_score": mean_score, "scores": scores}











"""simple docstring"""







from statistics import mean

import numpy as np

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





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

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

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

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

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

	return turn_around_time

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





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


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

			UpperCamelCase     =							calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			UpperCamelCase     =							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}')




