




"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


a_            =   logging.get_logger(__name__)

a_            =   {
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
			import detectrona

class        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									="layoutlmv2"
		def __init__(		self							,		UpperCamelCase_=3_05_22							,		UpperCamelCase_=7_68							,		UpperCamelCase_=12							,		UpperCamelCase_=12							,		UpperCamelCase_=30_72							,		UpperCamelCase_="gelu"							,		UpperCamelCase_=0.1							,		UpperCamelCase_=0.1							,		UpperCamelCase_=5_12							,		UpperCamelCase_=2							,		UpperCamelCase_=0.0_2							,		UpperCamelCase_=1E-12							,		UpperCamelCase_=0							,		UpperCamelCase_=10_24							,		UpperCamelCase_=1_28							,		UpperCamelCase_=32							,		UpperCamelCase_=True							,		UpperCamelCase_=2_56							,		UpperCamelCase_=64							,		UpperCamelCase_=True							,		UpperCamelCase_=[7, 7, 2_56]							,		UpperCamelCase_=1_28							,		UpperCamelCase_=1_28							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=False							,		UpperCamelCase_=None							,		**UpperCamelCase_							,		)		->   Optional[int]:
					super().__init__(
					    vocab_size=UpperCamelCase_							,		hidden_size=UpperCamelCase_							,		num_hidden_layers=UpperCamelCase_							,		num_attention_heads=UpperCamelCase_							,		intermediate_size=UpperCamelCase_							,		hidden_act=UpperCamelCase_							,		hidden_dropout_prob=UpperCamelCase_							,		attention_probs_dropout_prob=UpperCamelCase_							,		max_position_embeddings=UpperCamelCase_							,		type_vocab_size=UpperCamelCase_							,		initializer_range=UpperCamelCase_							,		layer_norm_eps=UpperCamelCase_							,		pad_token_id=UpperCamelCase_							,		**UpperCamelCase_							,		)
					__lowercase	:       Dict				=   max_ad_position_embeddings
					__lowercase	:       Union[str, Any]				=   max_rel_pos
					__lowercase	:       str				=   rel_pos_bins
					__lowercase	:       List[str]				=   fast_qkv
					__lowercase	:       Optional[int]				=   max_rel_ad_pos
					__lowercase	:       int				=   rel_ad_pos_bins
					__lowercase	:       Optional[Any]				=   convert_sync_batchnorm
					__lowercase	:       List[str]				=   image_feature_pool_shape
					__lowercase	:       str				=   coordinate_size
					__lowercase	:       str				=   shape_size
					__lowercase	:       Optional[int]				=   has_relative_attention_bias
					__lowercase	:       List[str]				=   has_spatial_attention_bias
					__lowercase	:       Tuple				=   has_visual_segment_embedding
					__lowercase	:       Optional[Any]				=   (
					    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
					)
		@classmethod
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					return {
					    "MODEL.MASK_ON": True,
					    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
					    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
					    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
					    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
					    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
					    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
					    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
					    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
					    "MODEL.POST_NMS_TOPK_TEST": 10_00,
					    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
					    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
					    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
					    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
					    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
					    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
					    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
					    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
					    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
					    "MODEL.RESNETS.DEPTH": 1_01,
					    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
					    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
					    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
					    "MODEL.RESNETS.NUM_GROUPS": 32,
					    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
					    "MODEL.RESNETS.STRIDE_IN_1X1": False,
					}
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       List[Any]				=   detectrona.config.get_cfg()
					for k, v in self.detectrona_config_args.items():
								__lowercase	:       int				=   k.split('''.'''   )
								__lowercase	:       List[Any]				=   detectrona_config
								for attribute in attributes[:-1]:
											__lowercase	:       Optional[int]				=   getattr(UpperCamelCase_							,		UpperCamelCase_   )
								setattr(UpperCamelCase_							,		attributes[-1]							,		UpperCamelCase_   )

					return detectrona_config









"""simple docstring"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [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
			a_            =   [1_9, 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
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

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



