




'''simple docstring'''




from typing import TYPE_CHECKING

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


snake_case__ 							=    {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    ["""LayoutLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """LayoutLMForMaskedLM""",
							    """LayoutLMForSequenceClassification""",
							    """LayoutLMForTokenClassification""",
							    """LayoutLMForQuestionAnswering""",
							    """LayoutLMModel""",
							    """LayoutLMPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFLayoutLMForMaskedLM""",
							    """TFLayoutLMForSequenceClassification""",
							    """TFLayoutLMForTokenClassification""",
							    """TFLayoutLMForQuestionAnswering""",
							    """TFLayoutLMMainLayer""",
							    """TFLayoutLMModel""",
							    """TFLayoutLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

							snake_case__ 							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''simple docstring'''




from __future__ import annotations

from statistics import mean




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  Dict			=				2
					A_			:  List[str]			=				list(lowerCamelCase__      )
					A_			:  int			=				[0] * len(lowerCamelCase__      )
					A_			:  int			=				0
					while True:
										A_			:  List[str]			=				True
										for i, burst_time in enumerate(lowerCamelCase__      ):
															if rem_burst_times[i] > 0:
																				A_			:  Any			=				False
																				if rem_burst_times[i] > quantum:
																									t += quantum
																									rem_burst_times[i] -= quantum
																				else:
																									t += rem_burst_times[i]
																									A_			:  Any			=				t - burst_time
																									A_			:  Optional[int]			=				0
										if done is True:
															return waiting_times




def     snake_case__     (			lowerCamelCase__					: list[int]      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					return [burst + waiting for burst, waiting in zip(lowerCamelCase__      ,		lowerCamelCase__      )]


if __name__ == "__main__":
							snake_case__ 							=    [3, 5, 7]
							snake_case__ 							=    calculate_waiting_times(burst_times)
							snake_case__ 							=    calculate_turn_around_times(burst_times, waiting_times)
							print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
							for i, burst_time in enumerate(burst_times):
														print(
														    F'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
														    F'{turn_around_times[i]}'
														)
							print(F'\nAverage waiting time = {mean(waiting_times):.5f}')
							print(F'Average turn around time = {mean(turn_around_times):.5f}')



