'''simple docstring'''





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase  :int  				=    logging.get_logger(__name__)


_lowerCAmelCase  :Union[str, Any]  				=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :int  				=    {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_lowerCAmelCase  :str  				=    {
    """allenai/led-base-16384""": 16_384,
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[Any]							=						VOCAB_FILES_NAMES
    snake_case__							:   Union[str, Any]							=						PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   Tuple							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   int							=						LEDTokenizer
    snake_case__							:   Optional[int]							=						["input_ids", "attention_mask"]






    def __init__(       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__="replace"    ,							lowercase__="<s>"    ,							lowercase__="</s>"    ,							lowercase__="</s>"    ,							lowercase__="<s>"    ,							lowercase__="<unk>"    ,							lowercase__="<pad>"    ,							lowercase__="<mask>"    ,							lowercase__=False    ,							lowercase__=True    ,							**lowercase__    ,							)							-> str:
         super().__init__(
             lowercase__    ,							lowercase__    ,							tokenizer_file=lowercase__    ,							errors=lowercase__    ,							bos_token=lowercase__    ,							eos_token=lowercase__    ,							sep_token=lowercase__    ,							cls_token=lowercase__    ,							unk_token=lowercase__    ,							pad_token=lowercase__    ,							mask_token=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__    ,							**lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Dict    	=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get('add_prefix_space'    ,							lowercase__ ) != add_prefix_space:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       getattr(lowercase__    ,							pre_tok_state.pop('type' ) )
              SCREAMING_SNAKE_CASE					:			Dict    	=       add_prefix_space
              SCREAMING_SNAKE_CASE					:			str    	=       pre_tok_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         SCREAMING_SNAKE_CASE					:			Dict    	=       'post_processor'
         SCREAMING_SNAKE_CASE					:			Dict    	=       getattr(self.backend_tokenizer    ,							lowercase__    ,							lowercase__ )
         if tokenizer_component_instance:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       json.loads(tokenizer_component_instance.__getstate__() )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tuple(state['sep'] )
              if "cls" in state:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       tuple(state['cls'] )

              SCREAMING_SNAKE_CASE					:			str    	=       False

              if state.get('add_prefix_space'    ,							lowercase__ ) != add_prefix_space:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       add_prefix_space
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True

              if state.get('trim_offsets'    ,							lowercase__ ) != trim_offsets:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       trim_offsets
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True

              if changes_to_apply:
                   SCREAMING_SNAKE_CASE					:			str    	=       getattr(lowercase__    ,							state.pop('type' ) )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       component_class(**lowercase__ )
                   setattr(self.backend_tokenizer    ,							lowercase__    ,							lowercase__ )






    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def       _UpperCamelCase    (       self )							-> str:
         if self._mask_token is None:
              if self.verbose:
                   logger.error('Using mask_token, but it is not set yet.' )
              return None
         return str(self._mask_token )






    @mask_token.setter
    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       AddedToken(lowercase__    ,							lstrip=lowercase__    ,							rstrip=lowercase__ ) if isinstance(lowercase__    ,							lowercase__ ) else value
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       value






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			int    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  'to use it with pretokenized inputs.' )

         return super()._batch_encode_plus(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			Dict    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  'to use it with pretokenized inputs.' )

         return super()._encode_plus(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple[str]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self._tokenizer.model.save(lowercase__    ,							name=lowercase__ )
         return tuple(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> List[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [self.sep_token_id]
         SCREAMING_SNAKE_CASE					:			str    	=       [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = PaddingStrategy.DO_NOT_PAD    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> dict:
         SCREAMING_SNAKE_CASE					:			int    	=       super()._pad(
             encoded_inputs=lowercase__    ,							max_length=lowercase__    ,							padding_strategy=lowercase__    ,							pad_to_multiple_of=lowercase__    ,							return_attention_mask=lowercase__    ,							)

         # Load from model defaults
         if return_attention_mask is None:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       'attention_mask' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              SCREAMING_SNAKE_CASE					:			str    	=       len(encoded_inputs['global_attention_mask'] ) != len(lowercase__ )

              if needs_to_be_padded:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       len(lowercase__ ) - len(encoded_inputs['global_attention_mask'] )

                   if self.padding_side == "right":
                        # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                        SCREAMING_SNAKE_CASE					:			str    	=       (
                            encoded_inputs['global_attention_mask'] + [-1] * difference
                        )
                   elif self.padding_side == "left":
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       [-1] * difference + encoded_inputs[
                            'global_attention_mask'
                        ]
                   else:
                        raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )

         return encoded_inputs




'''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}""")



