"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase     :				int    				=    {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__lowerCAmelCase     :				List[str]    				=    ["RobertaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__lowerCAmelCase     :				Optional[int]    				=    [
			    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "RobertaForCausalLM",
			    "RobertaForMaskedLM",
			    "RobertaForMultipleChoice",
			    "RobertaForQuestionAnswering",
			    "RobertaForSequenceClassification",
			    "RobertaForTokenClassification",
			    "RobertaModel",
			    "RobertaPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__lowerCAmelCase     :				Any    				=    [
			    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFRobertaForCausalLM",
			    "TFRobertaForMaskedLM",
			    "TFRobertaForMultipleChoice",
			    "TFRobertaForQuestionAnswering",
			    "TFRobertaForSequenceClassification",
			    "TFRobertaForTokenClassification",
			    "TFRobertaMainLayer",
			    "TFRobertaModel",
			    "TFRobertaPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__lowerCAmelCase     :				Optional[Any]    				=    [
			    "FlaxRobertaForCausalLM",
			    "FlaxRobertaForMaskedLM",
			    "FlaxRobertaForMultipleChoice",
			    "FlaxRobertaForQuestionAnswering",
			    "FlaxRobertaForSequenceClassification",
			    "FlaxRobertaForTokenClassification",
			    "FlaxRobertaModel",
			    "FlaxRobertaPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
			from .tokenization_roberta import RobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roberta_fast import RobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roberta import (
						    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RobertaForCausalLM,
						    RobertaForMaskedLM,
						    RobertaForMultipleChoice,
						    RobertaForQuestionAnswering,
						    RobertaForSequenceClassification,
						    RobertaForTokenClassification,
						    RobertaModel,
						    RobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roberta import (
						    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRobertaForCausalLM,
						    TFRobertaForMaskedLM,
						    TFRobertaForMultipleChoice,
						    TFRobertaForQuestionAnswering,
						    TFRobertaForSequenceClassification,
						    TFRobertaForTokenClassification,
						    TFRobertaMainLayer,
						    TFRobertaModel,
						    TFRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roberta import (
						    FlaxRobertaForCausalLM,
						    FlaxRobertaForMaskedLM,
						    FlaxRobertaForMultipleChoice,
						    FlaxRobertaForQuestionAnswering,
						    FlaxRobertaForSequenceClassification,
						    FlaxRobertaForTokenClassification,
						    FlaxRobertaModel,
						    FlaxRobertaPreTrainedModel,
						)

else:
			import sys

			__lowerCAmelCase     :				int    				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""




from __future__ import annotations

from statistics import mean







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		lowerCAmelCase__   = [0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i]
		lowerCAmelCase__   = []

		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				lowerCAmelCase__   = []
				lowerCAmelCase__   = -1

				for i in range(lowerCamelCase__  ):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase__  )

				if len(lowerCamelCase__  ) > 0:
						lowerCAmelCase__   = ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										lowerCAmelCase__   = i
						total_time += burst_time[target_process]
						completed += 1
						lowerCAmelCase__   = 0
						lowerCAmelCase__   = (
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




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


if __name__ == "__main__":
			print("[TEST CASE 01]")

			__lowerCAmelCase     :				str    				=    4
			__lowerCAmelCase     :				Dict    				=    [2, 5, 3, 7]
			__lowerCAmelCase     :				List[str]    				=    [0, 0, 0, 0]
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_turnaroundtime(
			    burst_time, no_of_processes, waiting_time
			)

			# Printing the Result
			print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
			for i, process_id in enumerate(list(range(1, 5))):
						print(
						    F"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
						    F"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
						)
			print(F"\nAverage waiting time = {mean(waiting_time):.5f}")
			print(F"Average turnaround time = {mean(turn_around_time):.5f}")




