


'''simple docstring'''





from __future__ import annotations

import pandas as pd


def        a__     (					UpperCamelCase_						:      list[int],   UpperCamelCase_						:      list[int],   UpperCamelCase_						:      int						):
   UpperCAmelCase__	:Optional[Any]      =      [0] * no_of_processes
   UpperCAmelCase__	:Dict      =      [0] * no_of_processes
   # Copy the burst time into remaining_time[]
   for i in range(UpperCamelCase_						):
      UpperCAmelCase__	:Any      =      burst_time[i]

   UpperCAmelCase__	:Any      =      0
   UpperCAmelCase__	:Union[str, Any]      =      0
   UpperCAmelCase__	:Optional[int]      =      999_999_999
   UpperCAmelCase__	:List[str]      =      0
   UpperCAmelCase__	:Tuple      =      False

   # Process until all processes are completed
   while complete != no_of_processes:
      for j in range(UpperCamelCase_						):
         if arrival_time[j] <= increment_time and remaining_time[j] > 0:
            if remaining_time[j] < minm:
               UpperCAmelCase__	:Union[str, Any]      =      remaining_time[j]
               UpperCAmelCase__	:str      =      j
               UpperCAmelCase__	:List[str]      =      True

      if not check:
         increment_time += 1
         continue
      remaining_time[short] -= 1

      UpperCAmelCase__	:Optional[int]      =      remaining_time[short]
      if minm == 0:
         UpperCAmelCase__	:List[str]      =      999_999_999

      if remaining_time[short] == 0:
         complete += 1
         UpperCAmelCase__	:int      =      False

         # Find finish time of current process
         UpperCAmelCase__	:Dict      =      increment_time + 1

         # Calculate waiting time
         UpperCAmelCase__	:int      =      finish_time - arrival_time[short]
         UpperCAmelCase__	:int      =      finar - burst_time[short]

         if waiting_time[short] < 0:
            UpperCAmelCase__	:Union[str, Any]      =      0

        # Increment time
      increment_time += 1
   return waiting_time


def        a__     (					UpperCamelCase_						:      list[int],   UpperCamelCase_						:      int,   UpperCamelCase_						:      list[int]						):
   UpperCAmelCase__	:List[str]      =      [0] * no_of_processes
   for i in range(UpperCamelCase_						):
      UpperCAmelCase__	:List[Any]      =      burst_time[i] + waiting_time[i]
   return turn_around_time


def        a__     (					UpperCamelCase_						:      list[int],   UpperCamelCase_						:      list[int],   UpperCamelCase_						:      int						):
   UpperCAmelCase__	:List[Any]      =      0
   UpperCAmelCase__	:int      =      0
   for i in range(UpperCamelCase_						):
      UpperCAmelCase__	:Dict      =      total_waiting_time + waiting_time[i]
      UpperCAmelCase__	:int      =      total_turn_around_time + turn_around_time[i]
   print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''						)
   print('''Average turn around time =''',   total_turn_around_time / no_of_processes						)


if __name__ == "__main__":
    print('''Enter how many process you want to analyze''')
    __lowerCamelCase												=						int(input())
    __lowerCamelCase												=						[0] * no_of_processes
    __lowerCamelCase												=						[0] * no_of_processes
    __lowerCamelCase												=						list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
        __lowerCamelCase					,		__lowerCamelCase												=						map(int, input().split())

    __lowerCamelCase												=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    __lowerCamelCase												=						burst_time
    __lowerCamelCase												=						no_of_processes
    __lowerCamelCase												=						waiting_time
    __lowerCamelCase												=						calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    __lowerCamelCase												=						pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            '''Process''',
            '''BurstTime''',
            '''ArrivalTime''',
            '''WaitingTime''',
            '''TurnAroundTime''',
        ],
    )

    # Printing the dataFrame
    pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
    print(fcfs)







'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"OwlViTImageProcessor"
 UpperCAmelCase				=					("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int]=None			,       __lowerCamelCase							:	Optional[Any]=None			,       **__lowerCamelCase							:	str ):
    UpperCAmelCase__	:int      =      None
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''			,       __lowerCamelCase			,       )
       UpperCAmelCase__	:Union[str, Any]      =      kwargs.pop('''feature_extractor''' )

    UpperCAmelCase__	:List[str]      =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.''' )
    if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.''' )

    super().__init__(__lowerCamelCase			,       __lowerCamelCase )


 def __call__(      self							:	int			,       __lowerCamelCase							:	str=None			,       __lowerCamelCase							:	List[Any]=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	str="max_length"			,       __lowerCamelCase							:	Any="np"			,       **__lowerCamelCase							:	Tuple ):

    if text is None and query_images is None and images is None:
       raise ValueError(
           '''You have to specify at least one text or query image or image. All three cannot be none.''' )

    if text is not None:
       if isinstance(__lowerCamelCase			,       __lowerCamelCase ) or (isinstance(__lowerCamelCase			,       __lowerCamelCase ) and not isinstance(text[0]			,       __lowerCamelCase )):
          UpperCAmelCase__	:Any      =      [self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )]

       elif isinstance(__lowerCamelCase			,       __lowerCamelCase ) and isinstance(text[0]			,       __lowerCamelCase ):
          UpperCAmelCase__	:Tuple      =      []

          # Maximum number of queries across batch
          UpperCAmelCase__	:List[str]      =      max([len(__lowerCamelCase ) for t in text] )

          # Pad all batch samples to max number of text queries
          for t in text:
             if len(__lowerCamelCase ) != max_num_queries:
                UpperCAmelCase__	:str      =      t + [''' '''] * (max_num_queries - len(__lowerCamelCase ))

             UpperCAmelCase__	:Tuple      =      self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )
             encodings.append(__lowerCamelCase )
       else:
          raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )

       if return_tensors == "np":
          UpperCAmelCase__	:List[Any]      =      np.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Any      =      np.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "jax" and is_flax_available():
          import jax.numpy as jnp

          UpperCAmelCase__	:List[Any]      =      jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Union[str, Any]      =      jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "pt" and is_torch_available():
          import torch

          UpperCAmelCase__	:Any      =      torch.cat([encoding['''input_ids'''] for encoding in encodings]			,       dim=0 )
          UpperCAmelCase__	:List[str]      =      torch.cat([encoding['''attention_mask'''] for encoding in encodings]			,       dim=0 )

       elif return_tensors == "tf" and is_tf_available():
          import tensorflow as tf

          UpperCAmelCase__	:Optional[Any]      =      tf.stack([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:int      =      tf.stack([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       else:
          raise ValueError('''Target return tensor type could not be returned''' )

       UpperCAmelCase__	:List[Any]      =      BatchEncoding()
       UpperCAmelCase__	:Union[str, Any]      =      input_ids
       UpperCAmelCase__	:Dict      =      attention_mask

    if query_images is not None:
       UpperCAmelCase__	:Tuple      =      BatchEncoding()
       UpperCAmelCase__	:int      =      self.image_processor(
           __lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase ).pixel_values
       UpperCAmelCase__	:Optional[int]      =      query_pixel_values

    if images is not None:
       UpperCAmelCase__	:str      =      self.image_processor(__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )

    if text is not None and images is not None:
       UpperCAmelCase__	:Dict      =      image_features.pixel_values
       return encoding
    elif query_images is not None and images is not None:
       UpperCAmelCase__	:Tuple      =      image_features.pixel_values
       return encoding
    elif text is not None or query_images is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**__lowerCamelCase )			,       tensor_type=__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	int ):
    return self.image_processor.post_process(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Tuple ):
    return self.image_processor.post_process_object_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       *__lowerCamelCase							:	Tuple			,       **__lowerCamelCase							:	str ):
    return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[Any]			,       **__lowerCamelCase							:	Union[str, Any] ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       *__lowerCamelCase							:	Union[str, Any]			,       **__lowerCamelCase							:	Dict ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor_class





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor



