
'''simple docstring'''

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_  ):


 @register_to_config
 def __init__(    self  : Tuple			,							*,
     a__  : int = 4			,							a__  : int = 768			,							a__  : int			,							a__  : str			,							):
  '''simple docstring'''

  super().__init__()

  lowerCAmelCase__     :   Optional[Any]     		=	nn.Parameter(torch.zeros(a__    )    )

  # parameters for additional clip time embeddings
  lowerCAmelCase__     :   int     		=	nn.Linear(a__			,							a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	nn.Linear(a__			,							a__    )

  # parameters for encoder hidden states
  lowerCAmelCase__     :   str     		=	clip_extra_context_tokens
  lowerCAmelCase__     :   Any     		=	nn.Linear(
      a__			,							self.clip_extra_context_tokens * cross_attention_dim    )
  lowerCAmelCase__     :   List[str]     		=	nn.Linear(a__			,							a__    )
  lowerCAmelCase__     :   Dict     		=	nn.LayerNorm(a__    )


 def    _A	(    self  : Dict			,							*, a__  : Tuple			,							a__  : Tuple			,							a__  : List[str]			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  if do_classifier_free_guidance:
   # Add the classifier free guidance embeddings to the image embeddings
   lowerCAmelCase__     :   Union[str, Any]     		=	image_embeddings.shape[0]
   lowerCAmelCase__     :   List[str]     		=	self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
   lowerCAmelCase__     :   Dict     		=	classifier_free_guidance_embeddings.expand(
       a__			,							-1    )
   lowerCAmelCase__     :   Optional[Any]     		=	torch.cat([classifier_free_guidance_embeddings, image_embeddings]			,							dim=0    )

  # The image embeddings batch size and the text embeddings batch size are equal
  assert image_embeddings.shape[0] == prompt_embeds.shape[0]

  lowerCAmelCase__     :   Tuple     		=	prompt_embeds.shape[0]

  # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
  # adding CLIP embeddings to the existing timestep embedding, ...
  lowerCAmelCase__     :   Union[str, Any]     		=	self.embedding_proj(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	self.clip_image_embeddings_project_to_time_embeddings(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	time_projected_image_embeddings + time_projected_prompt_embeds

  # ... and by projecting CLIP embeddings into four
  # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
  lowerCAmelCase__     :   List[str]     		=	self.clip_extra_context_tokens_proj(a__    )
  lowerCAmelCase__     :   Any     		=	clip_extra_context_tokens.reshape(a__			,							-1			,							self.clip_extra_context_tokens    )
  lowerCAmelCase__     :   Dict     		=	clip_extra_context_tokens.permute(0			,							2			,							1    )

  lowerCAmelCase__     :   List[Any]     		=	self.encoder_hidden_states_proj(a__    )
  lowerCAmelCase__     :   Tuple     		=	self.text_encoder_hidden_states_norm(a__    )
  lowerCAmelCase__     :   List[Any]     		=	torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]			,							dim=1    )

  return text_encoder_hidden_states, additive_clip_time_embeddings







'''simple docstring'''

from __future__ import annotations

import pandas as pd


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	[0] * no_of_processes
 lowerCAmelCase__     :   Any     		=	[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i]

 lowerCAmelCase__     :   str     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	9_9_9_9_9_9_9_9_9
 lowerCAmelCase__     :   int     		=	0
 lowerCAmelCase__     :   str     		=	False

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

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

  lowerCAmelCase__     :   Optional[Any]     		=	remaining_time[short]
  if minm == 0:
   lowerCAmelCase__     :   Optional[int]     		=	9_9_9_9_9_9_9_9_9

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

   # Find finish time of current process
   lowerCAmelCase__     :   Tuple     		=	increment_time + 1

   # Calculate waiting time
   lowerCAmelCase__     :   Dict     		=	finish_time - arrival_time[short]
   lowerCAmelCase__     :   Optional[Any]     		=	finar - burst_time[short]

   if waiting_time[short] < 0:
    lowerCAmelCase__     :   Tuple     		=	0

        # Increment time
  increment_time += 1
 return waiting_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	[0] * no_of_processes
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i] + waiting_time[i]
 return turn_around_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	0
 lowerCAmelCase__     :   Optional[int]     		=	0
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Tuple     		=	total_waiting_time + waiting_time[i]
  lowerCAmelCase__     :   str     		=	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""")
     snake_case									=    int(input())
     snake_case									=    [0] * no_of_processes
     snake_case									=    [0] * no_of_processes
     snake_case									=    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))
          snake_case					,					snake_case									=    map(int, input().split())

     snake_case									=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     snake_case									=    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)





