



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__SCREAMING_SNAKE_CASE :      List[str]             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Any             ={
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}


class   SCREAMING_SNAKE_CASE__				(   lowerCAmelCase_					):
	"""simple docstring"""


	A__    :    Tuple						=				"""plbart"""
	A__    :    Tuple						=				["""past_key_values"""]
	A__    :    Optional[int]						=				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


	def __init__(		self					,   A=5_00_05					,   A=10_24					,   A=6					,   A=30_72					,   A=12					,   A=6					,   A=30_72					,   A=12					,   A=0.0					,   A=0.0					,   A=True					,   A=True					,   A="gelu"					,   A=7_68					,   A=0.1					,   A=0.1					,   A=0.0					,   A=0.02					,   A=0.0					,   A=True					,   A=1					,   A=0					,   A=2					,   A=2					,   **A					,   ) ->							List[str]:
				A:      str														=   vocab_size
				A:      Optional[int]														=   max_position_embeddings
				A:      List[str]														=   d_model
				A:      Union[str, Any]														=   encoder_ffn_dim
				A:      Optional[Any]														=   encoder_layers
				A:      Dict														=   encoder_attention_heads
				A:      Dict														=   decoder_ffn_dim
				A:      Tuple														=   decoder_layers
				A:      str														=   decoder_attention_heads
				A:      int														=   dropout
				A:      str														=   attention_dropout
				A:      Optional[Any]														=   activation_dropout
				A:      Any														=   activation_function
				A:      List[str]														=   init_std
				A:      List[Any]														=   encoder_layerdrop
				A:      Optional[Any]														=   decoder_layerdrop
				A:      Any														=   classifier_dropout
				A:      Optional[Any]														=   use_cache
				A:      Tuple														=   encoder_layers
				A:      int														=   scale_embedding  # scale factor will be sqrt(d_model) if True
				super().__init__(
				    pad_token_id=__SCREAMING_SNAKE_CASE					,   bos_token_id=__SCREAMING_SNAKE_CASE					,   eos_token_id=__SCREAMING_SNAKE_CASE					,   is_encoder_decoder=__SCREAMING_SNAKE_CASE					,   forced_eos_token_id=__SCREAMING_SNAKE_CASE					,   **__SCREAMING_SNAKE_CASE					,   )







class   SCREAMING_SNAKE_CASE__				(   lowerCAmelCase_					):
	"""simple docstring"""


	@property
	def      a__ (		self			) ->							Optional[Any]:
				return OrderedDict(
				    [
				        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
				        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
				    ]			)





	@property
	def      a__ (		self			) ->							List[str]:

				if self.use_past:
							return OrderedDict(
							    [
							        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
							        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
							        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
							    ]			)
				else:
							return OrderedDict(
							    [
							        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
							        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
							    ]			)










'''simple docstring'''
from __future__ import annotations



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			A:      int														=   [0] * len(lowerCamelCase__				)
			for i in range(1							,							len(lowerCamelCase__				)				):
						A:      Dict														=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]							,							lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(lowerCamelCase__				)
			]



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)


if __name__ == "__main__":
					# process id's
					__SCREAMING_SNAKE_CASE :      Optional[Any]             =[1, 2, 3]

					# ensure that we actually have processes
					if len(processes) == 0:
										print('Zero amount of processes')
										raise SystemExit(0)

					# duration time of all processes
					__SCREAMING_SNAKE_CASE :      Dict             =[19, 8, 9]

					# ensure we can match each id to a duration time
					if len(duration_times) != len(processes):
										print('Unable to match all id\'s with their duration time')
										raise SystemExit(0)

					# get the waiting times and the turnaround times
					__SCREAMING_SNAKE_CASE :      Tuple             =calculate_waiting_times(duration_times)
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_turnaround_times(duration_times, waiting_times)

					# get the average times
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_average_waiting_time(waiting_times)
					__SCREAMING_SNAKE_CASE :      Union[str, Any]             =calculate_average_turnaround_time(turnaround_times)

					# print all the results
					print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
					for i, process in enumerate(processes):
										print(
										    f"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
										    f"""{turnaround_times[i]}"""
										)
					print(f"""Average waiting time = {average_waiting_time}""")
					print(f"""Average turn around time = {average_turnaround_time}""")





