




"""simple docstring"""




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def       __UpperCAmelCase      (				__UpperCamelCase					):
			if "model" in orig_key:
						__lowercase	:       str				=   orig_key.replace('''model.'''					,							''''''					)
			if "norm1" in orig_key:
						__lowercase	:       str				=   orig_key.replace('''norm1'''					,							'''attention.output.LayerNorm'''					)
			if "norm2" in orig_key:
						__lowercase	:       List[Any]				=   orig_key.replace('''norm2'''					,							'''output.LayerNorm'''					)
			if "norm" in orig_key:
						__lowercase	:       Optional[Any]				=   orig_key.replace('''norm'''					,							'''LayerNorm'''					)
			if "transformer" in orig_key:
						__lowercase	:       Dict				=   orig_key.split('''.'''					)[0].split('''_'''					)[-1]
						__lowercase	:       List[str]				=   orig_key.replace(f"""transformer_{layer_num}"""					,							f"""encoder.layer.{layer_num}"""					)
			if "mha.attn" in orig_key:
						__lowercase	:       Any				=   orig_key.replace('''mha.attn'''					,							'''attention.self'''					)
			if "mha" in orig_key:
						__lowercase	:       Tuple				=   orig_key.replace('''mha'''					,							'''attention'''					)
			if "W_q" in orig_key:
						__lowercase	:       Optional[Any]				=   orig_key.replace('''W_q'''					,							'''self.query'''					)
			if "W_k" in orig_key:
						__lowercase	:       Dict				=   orig_key.replace('''W_k'''					,							'''self.key'''					)
			if "W_v" in orig_key:
						__lowercase	:       str				=   orig_key.replace('''W_v'''					,							'''self.value'''					)
			if "ff.0" in orig_key:
						__lowercase	:       List[str]				=   orig_key.replace('''ff.0'''					,							'''intermediate.dense'''					)
			if "ff.2" in orig_key:
						__lowercase	:       Dict				=   orig_key.replace('''ff.2'''					,							'''output.dense'''					)
			if "ff" in orig_key:
						__lowercase	:       str				=   orig_key.replace('''ff'''					,							'''output.dense'''					)
			if "mlm_class" in orig_key:
						__lowercase	:       Any				=   orig_key.replace('''mlm.mlm_class'''					,							'''cls.predictions.decoder'''					)
			if "mlm" in orig_key:
						__lowercase	:       int				=   orig_key.replace('''mlm'''					,							'''cls.predictions.transform'''					)
			if "backbone.backbone.encoders" in orig_key:
						__lowercase	:       Dict				=   orig_key.replace('''backbone.backbone.encoders'''					,							'''encoder.layer'''					)
			if "cls" not in orig_key:
						__lowercase	:       Union[str, Any]				=   '''mra.''' + orig_key

			return orig_key







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			for key in orig_state_dict.copy().keys():
						__lowercase	:       List[Any]				=   orig_state_dict.pop(__UpperCamelCase					)

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									__lowercase	:       Tuple				=   val

			__lowercase	:       Any				=   orig_state_dict['''cls.predictions.decoder.bias''']
			__lowercase	:       Tuple				=   torch.arange(__UpperCamelCase					).expand((1, -1)					) + 2

			return orig_state_dict







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					):
			__lowercase	:       Optional[int]				=   torch.load(__UpperCamelCase					,							map_location='''cpu'''					)['''model_state_dict''']
			__lowercase	:       Optional[int]				=   MraConfig.from_json_file(__UpperCamelCase					)
			__lowercase	:       str				=   MraForMaskedLM(__UpperCamelCase					)

			__lowercase	:       Optional[Any]				=   convert_checkpoint_helper(config.max_position_embeddings					,							__UpperCamelCase					)

			print(model.load_state_dict(__UpperCamelCase					)					)
			model.eval()
			model.save_pretrained(__UpperCamelCase					)

			print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""					)


if __name__ == "__main__":
			a_            =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
			)
			parser.add_argument(
			    '--config_file',
			    default=None,
			    type=str,
			    required=True,
			    help='The json file for Mra model config.',
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			a_            =   parser.parse_args()
			convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









"""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}")



