






'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
	import textwrap

	from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
	import torch

	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	__magic_name__		:     Optional[int]					=    False


__magic_name__		:     Any					=    logging.get_logger(__name__)
__magic_name__		:     Dict					=    '''ybelkada/fonts'''



def     A__						(	)     ->				List[str]:


					if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
										raise ImportError(
										    F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
										    "Pix2StructImageProcessor. Please upgrade torch."		)



def     A__						(	A_    ,      A_    ,      A_		)     ->				Dict:
					requires_backends(lowerCamelCase_    ,      ["torch"]		)
					_check_torch_version()

					_lowercase			= image_tensor.unsqueeze(0		)
					_lowercase			= torch.nn.functional.unfold(lowerCamelCase_    ,      (patch_height, patch_width)    ,      stride=(patch_height, patch_width)		)
					_lowercase			= patches.reshape(image_tensor.size(0		)    ,      image_tensor.size(1		)    ,      lowerCamelCase_    ,      lowerCamelCase_    ,      -1		)
					_lowercase			= patches.permute(0    ,      4    ,      2    ,      3    ,      1		).reshape(
					    image_tensor.size(2		) // patch_height    ,      image_tensor.size(3		) // patch_width    ,      image_tensor.size(1		) * patch_height * patch_width    ,      )
					return patches.unsqueeze(0		)



def     A__						(	A_    ,      A_ = 36    ,      A_ = "black"    ,      A_ = "white"    ,      A_ = 5    ,      A_ = 5    ,      A_ = 5    ,      A_ = 5    ,      A_ = None    ,      A_ = None    ,      )     ->				Optional[Any]:
					requires_backends(lowerCamelCase_    ,      "vision"		)
					# Add new lines so that each line is no more than 80 characters.

					_lowercase			= textwrap.TextWrapper(width=80		)
					_lowercase			= wrapper.wrap(text=lowerCamelCase_		)
					_lowercase			= "\n".join(lowerCamelCase_		)

					if font_bytes is not None and font_path is None:
										_lowercase			= io.BytesIO(lowerCamelCase_		)
					elif font_path is not None:
										_lowercase			= font_path
					else:
										_lowercase			= hf_hub_download(lowerCamelCase_    ,      "Arial.TTF"		)
					_lowercase			= ImageFont.truetype(lowerCamelCase_    ,      encoding="UTF-8"    ,      size=lowerCamelCase_		)

					# Use a temporary canvas to determine the width and height in pixels when
					# rendering the text.
					_lowercase			= ImageDraw.Draw(Image.new("RGB"    ,      (1, 1)    ,      lowerCamelCase_		)		)
					_lowercase			= temp_draw.textbbox((0, 0)    ,      lowerCamelCase_    ,      lowerCamelCase_		)

					# Create the actual image with a bit of padding around the text.
					_lowercase			= text_width + left_padding + right_padding
					_lowercase			= text_height + top_padding + bottom_padding
					_lowercase			= Image.new("RGB"    ,      (image_width, image_height)    ,      lowerCamelCase_		)
					_lowercase			= ImageDraw.Draw(lowerCamelCase_		)
					draw.text(xy=(left_padding, top_padding)    ,      text=lowerCamelCase_    ,      fill=lowerCamelCase_    ,      font=lowerCamelCase_		)
					return image



def     A__						(	A_    ,      A_    ,      **A_		)     ->				Tuple:
					requires_backends(lowerCamelCase_    ,      "vision"		)

					# Convert to PIL image if necessary
					_lowercase			= to_pil_image(lowerCamelCase_		)

					_lowercase			= render_text(lowerCamelCase_    ,      **lowerCamelCase_		)
					_lowercase			= max(header_image.width    ,      image.width		)

					_lowercase			= int(image.height * (new_width / image.width)		)
					_lowercase			= int(header_image.height * (new_width / header_image.width)		)

					_lowercase			= Image.new("RGB"    ,      (new_width, new_height + new_header_height)    ,      "white"		)
					new_image.paste(header_image.resize((new_width, new_header_height)		)    ,      (0, 0)		)
					new_image.paste(image.resize((new_width, new_height)		)    ,      (0, new_header_height)		)

					# Convert back to the original framework if necessary
					_lowercase			= to_numpy_array(lowerCamelCase_		)

					if infer_channel_dimension_format(lowerCamelCase_		) == ChannelDimension.LAST:
										_lowercase			= to_channel_dimension_format(lowerCamelCase_    ,      ChannelDimension.LAST		)

					return new_image







class 	UpperCamelCase__					(     UpperCamelCase_			):

		"""simple docstring"""
		UpperCAmelCase__     			=						["""flattened_patches"""]




		def __init__( self						:						Any							,    __A						:						bool = True							,    __A						:						bool = True							,    __A						:						Dict[str, int] = None							,    __A						:						int = 2_0_4_8							,    __A						:						bool = False							,    **__A						:						Optional[Any]							,    ):







							"""simple docstring"""
							super().__init__(**a__    )
							_lowercase			= patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
							_lowercase			= do_normalize
							_lowercase			= do_convert_rgb
							_lowercase			= max_patches
							_lowercase			= is_vqa




		def       snake_case     ( self						:						List[str]							,    __A						:						np.ndarray							,    __A						:						int							,    __A						:						dict							,    **__A						:						Any    ):







							"""simple docstring"""
							requires_backends(self.extract_flattened_patches							,    "torch"    )
							_check_torch_version()

							# convert to torch
							_lowercase			= to_channel_dimension_format(a__							,    ChannelDimension.FIRST    )
							_lowercase			= torch.from_numpy(a__    )

							_lowercase			= patch_size["height"], patch_size["width"]
							_lowercase			= get_image_size(a__    )

							# maximize scale s.t.
							_lowercase			= math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)    )
							_lowercase			= max(min(math.floor(scale * image_height / patch_height    )							,    a__    )							,    1    )
							_lowercase			= max(min(math.floor(scale * image_width / patch_width    )							,    a__    )							,    1    )
							_lowercase			= max(num_feasible_rows * patch_height							,    1    )
							_lowercase			= max(num_feasible_cols * patch_width							,    1    )

							_lowercase			= torch.nn.functional.interpolate(
							    image.unsqueeze(0    )							,    size=(resized_height, resized_width)							,    mode="bilinear"							,    align_corners=a__							,    antialias=a__							,    ).squeeze(0    )

							# [1, rows, columns, patch_height * patch_width * image_channels]
							_lowercase			= torch_extract_patches(a__							,    a__							,    a__    )

							_lowercase			= patches.shape
							_lowercase			= patches_shape[1]
							_lowercase			= patches_shape[2]
							_lowercase			= patches_shape[3]

							# [rows * columns, patch_height * patch_width * image_channels]
							_lowercase			= patches.reshape([rows * columns, depth]    )

							# [rows * columns, 1]
							_lowercase			= torch.arange(a__    ).reshape([rows, 1]    ).repeat(1							,    a__    ).reshape([rows * columns, 1]    )
							_lowercase			= torch.arange(a__    ).reshape([1, columns]    ).repeat(a__							,    1    ).reshape([rows * columns, 1]    )

							# Offset by 1 so the ids do not contain zeros, which represent padding.
							row_ids += 1
							col_ids += 1

							# Prepare additional patch features.
							# [rows * columns, 1]
							_lowercase			= row_ids.to(torch.floataa    )
							_lowercase			= col_ids.to(torch.floataa    )

							# [rows * columns, 2 + patch_height * patch_width * image_channels]
							_lowercase			= torch.cat([row_ids, col_ids, patches]							,    -1    )

							# [max_patches, 2 + patch_height * patch_width * image_channels]
							_lowercase			= torch.nn.functional.pad(a__							,    [0, 0, 0, max_patches - (rows * columns)]    ).float()

							_lowercase			= to_numpy_array(a__    )

							return result




		def       snake_case     ( self						:						Optional[int]							,    __A						:						np.ndarray							,    __A						:						Optional[Union[str, ChannelDimension]] = None							,    **__A						:						Dict    ):







							"""simple docstring"""

							if image.dtype == np.uinta:
												_lowercase			= image.astype(np.floataa    )

							# take mean across the whole `image`
							_lowercase			= np.mean(a__    )
							_lowercase			= np.std(a__    )
							_lowercase			= max(a__							,    1.0 / math.sqrt(np.prod(image.shape    )    )    )

							return normalize(a__							,    mean=a__							,    std=a__							,    **a__    )




		def       snake_case     ( self						:						Any							,    __A						:						ImageInput							,    __A						:						Optional[str] = None							,    __A						:						bool = None							,    __A						:						Optional[bool] = None							,    __A						:						Optional[int] = None							,    __A						:						Optional[Dict[str, int]] = None							,    __A						:						Optional[Union[str, TensorType]] = None							,    __A						:						ChannelDimension = ChannelDimension.FIRST							,    **__A						:						Dict							,    ):







							"""simple docstring"""
							_lowercase			= do_normalize if do_normalize is not None else self.do_normalize
							_lowercase			= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							_lowercase			= patch_size if patch_size is not None else self.patch_size
							_lowercase			= max_patches if max_patches is not None else self.max_patches
							_lowercase			= self.is_vqa

							if kwargs.get("data_format"							,    a__    ) is not None:
												raise ValueError("data_format is not an accepted input as the outputs are "    )

							_lowercase			= make_list_of_images(a__    )

							if not valid_images(a__    ):
												raise ValueError(
												    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
												    "torch.Tensor, tf.Tensor or jax.ndarray."    )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
												_lowercase			= [convert_to_rgb(a__    ) for image in images]

							# All transformations expect numpy arrays.
							_lowercase			= [to_numpy_array(a__    ) for image in images]

							if is_vqa:
												if header_text is None:
																	raise ValueError("A header text must be provided for VQA models."    )
												_lowercase			= kwargs.pop("font_bytes"							,    a__    )
												_lowercase			= kwargs.pop("font_path"							,    a__    )

												if isinstance(a__							,    a__    ):
																	_lowercase			= [header_text] * len(a__    )

												_lowercase			= [
												    render_header(a__							,    header_text[i]							,    font_bytes=a__							,    font_path=a__    )
												    for i, image in enumerate(a__    )
												]

							if do_normalize:
												_lowercase			= [self.normalize(image=a__    ) for image in images]

							# convert to torch tensor and permute
							_lowercase			= [
							    self.extract_flattened_patches(image=a__							,    max_patches=a__							,    patch_size=a__    )
							    for image in images
							]

							# create attention mask in numpy
							_lowercase			= [(image.sum(axis=-1    ) != 0).astype(np.floataa    ) for image in images]

							_lowercase			= BatchFeature(
							    data={"flattened_patches": images, "attention_mask": attention_masks}							,    tensor_type=a__    )

							return encoded_outputs



'''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)





