'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_lowerCAmelCase  :Any  				=    logging.get_logger(__name__)
logging.set_verbosity_info()


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	int:





     '''simple docstring'''





     if "xprophetnet" in prophetnet_checkpoint_path:
          SCREAMING_SNAKE_CASE					:			int    	=       XLMProphetNetForConditionalGenerationOld.from_pretrained(a_		)
          SCREAMING_SNAKE_CASE					:			str    	=       XLMProphetNetForConditionalGeneration.from_pretrained(
              a_       ,      output_loading_info=a_		)
     else:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ProphetNetForConditionalGenerationOld.from_pretrained(a_		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       ProphetNetForConditionalGeneration.from_pretrained(
              a_       ,      output_loading_info=a_		)

     SCREAMING_SNAKE_CASE					:			str    	=       ['key_proj', 'value_proj', 'query_proj']

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {
         'self_attn': 'ngram_self_attn',
         'cross_attn': 'encoder_attn',
         'cross_attn_layer_norm': 'encoder_attn_layer_norm',
         'feed_forward_layer_norm': 'final_layer_norm',
         'feed_forward': '',
         'intermediate': 'fc1',
         'output': 'fc2',
         'key_proj': 'k_proj',
         'query_proj': 'q_proj',
         'value_proj': 'v_proj',
         'word_embeddings': 'embed_tokens',
         'embeddings_layer_norm': 'emb_layer_norm',
         'relative_pos_embeddings': 'relative_linear',
         'ngram_embeddings': 'ngram_input_embed',
         'position_embeddings': 'embed_positions',
     }

     for key in loading_info["missing_keys"]:
          SCREAMING_SNAKE_CASE					:			str    	=       key.split('.'		)

          if attributes[0] == "lm_head":
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       prophet
               SCREAMING_SNAKE_CASE					:			Dict    	=       prophet_old
          else:
               SCREAMING_SNAKE_CASE					:			int    	=       prophet.prophetnet
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       prophet_old.model

          SCREAMING_SNAKE_CASE					:			str    	=       False
          for attribute in attributes:
               if attribute in mapping:
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       mapping[attribute]
                    if not hasattr(a_       ,      a_		) and len(a_		) > 0:
                         SCREAMING_SNAKE_CASE					:			List[str]    	=       attribute
               elif hasattr(a_       ,      a_		):
                    SCREAMING_SNAKE_CASE					:			int    	=       attribute

               if attribute == "weight":
                    assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       old_model.weight
                    logger.info(f"""{attribute} is initialized."""		)
                    SCREAMING_SNAKE_CASE					:			Dict    	=       True
                    break
               elif attribute == "bias":
                    assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       old_model.bias
                    logger.info(f"""{attribute} is initialized"""		)
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       True
                    break
               elif attribute in special_keys and hasattr(a_       ,      'in_proj_weight'		):
                    SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       old_model.in_proj_weight.shape[0] // 3
                    SCREAMING_SNAKE_CASE					:			Tuple    	=       getattr(a_       ,      a_		)
                    param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                    param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                    if attribute == "query_proj":
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
                         SCREAMING_SNAKE_CASE					:			List[Any]    	=       nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

                    elif attribute == "key_proj":
                         SCREAMING_SNAKE_CASE					:			List[Any]    	=       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                    elif attribute == "value_proj":
                         SCREAMING_SNAKE_CASE					:			str    	=       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                         SCREAMING_SNAKE_CASE					:			List[str]    	=       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       True
                    break
               elif attribute == "position_embeddings":
                    assert (
                        model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                    ), "Hidden size has to match"
                    assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       nn.Parameter(old_model.embed_positions.weight[:512, :]		)
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       True
                    break

               if attribute.isdigit():
                    SCREAMING_SNAKE_CASE					:			List[str]    	=       model[int(a_		)]
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       old_model[int(a_		)]
               else:
                    SCREAMING_SNAKE_CASE					:			List[str]    	=       getattr(a_       ,      a_		)

                    if old_attribute == "":
                         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       old_model
                    else:
                         if not hasattr(a_       ,      a_		):
                              raise ValueError(f"""{old_model} does not have {old_attribute}"""		)
                         SCREAMING_SNAKE_CASE					:			Dict    	=       getattr(a_       ,      a_		)

          if not is_key_init:
               raise ValueError(f"""{key} was not correctly initialized!"""		)

     print(f"""Saving model to {pytorch_dump_folder_path}"""		)
     prophet.save_pretrained(a_		)


if __name__ == "__main__":
     _lowerCAmelCase  :Optional[int]  				=    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _lowerCAmelCase  :Dict  				=    parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''





from __future__ import annotations

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0] * no_of_processes
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0

     # When processes are not completed,
     # A process whose arrival time has passed \
     # and has remaining execution time is put into the ready_process.
     # The shortest process in the ready_process, target_process is executed.

     while completed != no_of_processes:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
          SCREAMING_SNAKE_CASE					:			Dict    	=       -1

          for i in range(a_		):
               if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                    ready_process.append(a_		)

          if len(a_		) > 0:
               SCREAMING_SNAKE_CASE					:			Dict    	=       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       i
               total_time += burst_time[target_process]
               completed += 1
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       0
               SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


if __name__ == "__main__":
 print("""[TEST CASE 01]""")

 _lowerCAmelCase  :Optional[int]  				=    4
 _lowerCAmelCase  :Optional[int]  				=    [2, 5, 3, 7]
 _lowerCAmelCase  :List[str]  				=    [0, 0, 0, 0]
 _lowerCAmelCase  :Union[str, Any]  				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 _lowerCAmelCase  :Any  				=    calculate_turnaroundtime(
     burst_time, no_of_processes, waiting_time
 )

 # Printing the Result
 print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
 for i, process_id in enumerate(list(range(1, 5))):
  print(
      f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
      f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
  )
 print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
 print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")



