






"""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:
 lowerCAmelCase_	:    Optional[int]					     =      False


lowerCAmelCase_	:    Any					     =      logging.get_logger(__name__)
lowerCAmelCase_	:    Tuple					     =      '''ybelkada/fonts'''






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       requires_backends(lowerCAmelCase	,      ["""torch"""]		)
       _check_torch_version()

       UpperCAmelCase	    =			image_tensor.unsqueeze(0		)
       UpperCAmelCase	    =			torch.nn.functional.unfold(lowerCAmelCase	,      (patch_height, patch_width)	,      stride=(patch_height, patch_width)		)
       UpperCAmelCase	    =			patches.reshape(image_tensor.size(0		)	,      image_tensor.size(1		)	,      lowerCAmelCase	,      lowerCAmelCase	,      -1		)
       UpperCAmelCase	    =			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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase = 36	,      lowerCAmelCase = "black"	,      lowerCAmelCase = "white"	,      lowerCAmelCase = 5	,      lowerCAmelCase = 5	,      lowerCAmelCase = 5	,      lowerCAmelCase = 5	,      lowerCAmelCase = None	,      lowerCAmelCase = None	,      ):







       '''simple docstring'''
       requires_backends(lowerCAmelCase	,      """vision"""		)
       # Add new lines so that each line is no more than 80 characters.

       UpperCAmelCase	    =			textwrap.TextWrapper(width=80		)
       UpperCAmelCase	    =			wrapper.wrap(text=lowerCAmelCase		)
       UpperCAmelCase	    =			"""\n""".join(lowerCAmelCase		)

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

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

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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      **lowerCAmelCase		):







       '''simple docstring'''
       requires_backends(lowerCAmelCase	,      """vision"""		)

       # Convert to PIL image if necessary
       UpperCAmelCase	    =			to_pil_image(lowerCAmelCase		)

       UpperCAmelCase	    =			render_text(lowerCAmelCase	,      **lowerCAmelCase		)
       UpperCAmelCase	    =			max(header_image.width	,      image.width		)

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

       UpperCAmelCase	    =			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
       UpperCAmelCase	    =			to_numpy_array(lowerCAmelCase		)

       if infer_channel_dimension_format(lowerCAmelCase		) == ChannelDimension.LAST:
              UpperCAmelCase	    =			to_channel_dimension_format(lowerCAmelCase	,      ChannelDimension.LAST		)

       return new_image





class   UpperCamelCase_ (       a_  ):
  _A       :					List[str]				=	['flattened_patches']



  def __init__(      self ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 20_48 ,		snake_case__ = False ,		**snake_case__ ,		)			->			None:



         """simple docstring"""





         super().__init__(**snake_case__				)
         UpperCAmelCase	    =			patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			do_convert_rgb
         UpperCAmelCase	    =			max_patches
         UpperCAmelCase	    =			is_vqa



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		**snake_case__				)			->			np.ndarray:



         """simple docstring"""





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

         # convert to torch
         UpperCAmelCase	    =			to_channel_dimension_format(snake_case__ ,		ChannelDimension.FIRST				)
         UpperCAmelCase	    =			torch.from_numpy(snake_case__				)

         UpperCAmelCase    ,       UpperCAmelCase	    =			patch_size["""height"""], patch_size["""width"""]
         UpperCAmelCase    ,       UpperCAmelCase	    =			get_image_size(snake_case__				)

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

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

         # [1, rows, columns, patch_height * patch_width * image_channels]
         UpperCAmelCase	    =			torch_extract_patches(snake_case__ ,		snake_case__ ,		snake_case__				)

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

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

         # [rows * columns, 1]
         UpperCAmelCase	    =			torch.arange(snake_case__				).reshape([rows, 1]				).repeat(1 ,		snake_case__				).reshape([rows * columns, 1]				)
         UpperCAmelCase	    =			torch.arange(snake_case__				).reshape([1, columns]				).repeat(snake_case__ ,		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]
         UpperCAmelCase	    =			row_ids.to(torch.floataa				)
         UpperCAmelCase	    =			col_ids.to(torch.floataa				)

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

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

         UpperCAmelCase	    =			to_numpy_array(snake_case__				)

         return result



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		**snake_case__				)			->			np.ndarray:



         """simple docstring"""





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

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

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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = ChannelDimension.FIRST ,		**snake_case__ ,		)			->			ImageInput:



         """simple docstring"""





         UpperCAmelCase	    =			do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase	    =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
         UpperCAmelCase	    =			patch_size if patch_size is not None else self.patch_size
         UpperCAmelCase	    =			max_patches if max_patches is not None else self.max_patches
         UpperCAmelCase	    =			self.is_vqa

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

         UpperCAmelCase	    =			make_list_of_images(snake_case__				)

         if not valid_images(snake_case__				):
                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:
                UpperCAmelCase	    =			[convert_to_rgb(snake_case__				) for image in images]

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

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

                if isinstance(snake_case__ ,		snake_case__				):
                       UpperCAmelCase	    =			[header_text] * len(snake_case__				)

                UpperCAmelCase	    =			[
                    render_header(snake_case__ ,		header_text[i] ,		font_bytes=snake_case__ ,		font_path=snake_case__				)
                    for i, image in enumerate(snake_case__				)
                ]

         if do_normalize:
                UpperCAmelCase	    =			[self.normalize(image=snake_case__				) for image in images]

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

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

         UpperCAmelCase	    =			BatchFeature(
             data={"""flattened_patches""": images, """attention_mask""": attention_masks} ,		tensor_type=snake_case__				)

         return encoded_outputs








"""simple docstring"""







from __future__ import annotations

import pandas as pd






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       UpperCAmelCase	    =			[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i]

       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			999999999
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			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:
                                   UpperCAmelCase	    =			remaining_time[j]
                                   UpperCAmelCase	    =			j
                                   UpperCAmelCase	    =			True

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

              UpperCAmelCase	    =			remaining_time[short]
              if minm == 0:
                     UpperCAmelCase	    =			999999999

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

                     # Find finish time of current process
                     UpperCAmelCase	    =			increment_time + 1

                     # Calculate waiting time
                     UpperCAmelCase	    =			finish_time - arrival_time[short]
                     UpperCAmelCase	    =			finar - burst_time[short]

                     if waiting_time[short] < 0:
                            UpperCAmelCase	    =			0

        # Increment time
              increment_time += 1
       return waiting_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i] + waiting_time[i]
       return turn_around_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			total_waiting_time + waiting_time[i]
              UpperCAmelCase	    =			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_	:    Optional[Any]            =      int(input())
 lowerCAmelCase_	:    List[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    Optional[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    int            =      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_	:    Optional[Any]            =      map(int, input().split())

 lowerCAmelCase_	:    str            =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

 lowerCAmelCase_	:    str            =      burst_time
 lowerCAmelCase_	:    List[Any]            =      no_of_processes
 lowerCAmelCase_	:    int            =      waiting_time
 lowerCAmelCase_	:    int            =      calculate_turnaroundtime(bt, n, wt)

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCAmelCase_	:    List[Any]            =      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)
