






from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      =      {
    '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 	__UpperCamelCase    (    lowerCAmelCase__	):
					lowercase_						:							List[Any]			=     """plbart"""
					lowercase_						:							Optional[int]			=     ["""past_key_values"""]
					lowercase_						:							Any			=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
					def __init__(       self       :  Tuple    ,   UpperCAmelCase       :  Union[str, Any]=5_0005    ,   UpperCAmelCase       :  str=1024    ,   UpperCAmelCase       :  Optional[int]=6    ,   UpperCAmelCase       :  Any=3072    ,   UpperCAmelCase       :  List[str]=12    ,   UpperCAmelCase       :  Union[str, Any]=6    ,   UpperCAmelCase       :  Any=3072    ,   UpperCAmelCase       :  Optional[Any]=12    ,   UpperCAmelCase       :  Optional[Any]=0.0    ,   UpperCAmelCase       :  List[str]=0.0    ,   UpperCAmelCase       :  Optional[Any]=True    ,   UpperCAmelCase       :  List[Any]=True    ,   UpperCAmelCase       :  Any="gelu"    ,   UpperCAmelCase       :  Any=768    ,   UpperCAmelCase       :  Optional[int]=0.1    ,   UpperCAmelCase       :  Optional[Any]=0.1    ,   UpperCAmelCase       :  Union[str, Any]=0.0    ,   UpperCAmelCase       :  Union[str, Any]=0.0_2    ,   UpperCAmelCase       :  str=0.0    ,   UpperCAmelCase       :  Optional[Any]=True    ,   UpperCAmelCase       :  List[str]=1    ,   UpperCAmelCase       :  Any=0    ,   UpperCAmelCase       :  Union[str, Any]=2    ,   UpperCAmelCase       :  Union[str, Any]=2    ,   **UpperCAmelCase       :  Tuple    ,   )  ->					Tuple:
											lowerCAmelCase      :Union[str, Any]				  = vocab_size
											lowerCAmelCase      :Optional[Any]				  = max_position_embeddings
											lowerCAmelCase      :Optional[Any]				  = d_model
											lowerCAmelCase      :List[str]				  = encoder_ffn_dim
											lowerCAmelCase      :str				  = encoder_layers
											lowerCAmelCase      :Union[str, Any]				  = encoder_attention_heads
											lowerCAmelCase      :Any				  = decoder_ffn_dim
											lowerCAmelCase      :List[str]				  = decoder_layers
											lowerCAmelCase      :Dict				  = decoder_attention_heads
											lowerCAmelCase      :str				  = dropout
											lowerCAmelCase      :Optional[Any]				  = attention_dropout
											lowerCAmelCase      :Dict				  = activation_dropout
											lowerCAmelCase      :int				  = activation_function
											lowerCAmelCase      :List[str]				  = init_std
											lowerCAmelCase      :List[str]				  = encoder_layerdrop
											lowerCAmelCase      :List[Any]				  = decoder_layerdrop
											lowerCAmelCase      :Optional[int]				  = classifier_dropout
											lowerCAmelCase      :Any				  = use_cache
											lowerCAmelCase      :Optional[Any]				  = encoder_layers
											lowerCAmelCase      :str				  = scale_embedding  # scale factor will be sqrt(d_model) if True
											super().__init__(
											    pad_token_id=lowerCAmelCase_    ,   bos_token_id=lowerCAmelCase_    ,   eos_token_id=lowerCAmelCase_    ,   is_encoder_decoder=lowerCAmelCase_    ,   forced_eos_token_id=lowerCAmelCase_    ,   **lowerCAmelCase_    ,   )




class 	__UpperCamelCase    (    lowerCAmelCase__	):
					@property
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											    ]      )
					@property
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Mapping[str, Mapping[int, 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 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Union[str, Any]				  = [0] * len(a__   )
						for i in range(1    ,			len(a__   )   ):
												lowerCAmelCase      :Optional[int]				  = duration_times[i - 1] + waiting_times[i - 1]
						return waiting_times


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






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


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


if __name__ == "__main__":
		# process id's
		__SCREAMING_SNAKE_CASE      =      [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      =      [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      =      calculate_waiting_times(duration_times)
		__SCREAMING_SNAKE_CASE      =      calculate_turnaround_times(duration_times, waiting_times)

		# get the average times
		__SCREAMING_SNAKE_CASE      =      calculate_average_waiting_time(waiting_times)
		__SCREAMING_SNAKE_CASE      =      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}""")