
"""simple docstring"""



import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor





class 				a			(					unittest.TestCase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=7     ,	UpperCamelCase_=3     ,	UpperCamelCase_=18     ,	UpperCamelCase_=30     ,	UpperCamelCase_=400     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	):
          UpperCAmelCase__	:      Any     							=					size if size is not None else {'height': 18, 'width': 18}
          UpperCAmelCase__	:      Any     							=					parent
          UpperCAmelCase__	:      int     							=					batch_size
          UpperCAmelCase__	:      int     							=					num_channels
          UpperCAmelCase__	:      Tuple     							=					image_size
          UpperCAmelCase__	:      Any     							=					min_resolution
          UpperCAmelCase__	:      Optional[Any]     							=					max_resolution
          UpperCAmelCase__	:      Optional[int]     							=					do_resize
          UpperCAmelCase__	:      Optional[int]     							=					size
          UpperCAmelCase__	:      List[Any]     							=					do_normalize



      def  __snake_case							(   self      ):
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                      [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
                  ]      ),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }





@require_torch
@require_vision
class 				a			(					lowerCAmelCase_						,		unittest.TestCase      ):
      UpperCamelCase							:   List[Any]           =     ImageGPTImageProcessor if is_vision_available() else None



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					ImageGPTImageProcessingTester(self      )



      @property
      def  __snake_case							(   self      ):
          return self.image_processor_tester.prepare_image_processor_dict()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(__lowerCAmelCase     ,	'clusters'      )      )
          self.assertTrue(hasattr(__lowerCAmelCase     ,	'do_resize'      )      )
          self.assertTrue(hasattr(__lowerCAmelCase     ,	'size'      )      )
          self.assertTrue(hasattr(__lowerCAmelCase     ,	'do_normalize'      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.image_processing_class.from_dict(self.image_processor_dict      )
          self.assertEqual(image_processor.size     ,	{'height': 18, 'width': 18}      )

          UpperCAmelCase__	:      Optional[Any]     							=					self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42      )
          self.assertEqual(image_processor.size     ,	{'height': 42, 'width': 42}      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.image_processing_class(**self.image_processor_dict      )
          UpperCAmelCase__	:      Optional[int]     							=					json.loads(image_processor.to_json_string()      )
          for key, value in self.image_processor_dict.items():
              if key == "clusters":
                  self.assertTrue(np.array_equal(__lowerCAmelCase     ,	obj[key]      )      )
              else:
                  self.assertEqual(obj[key]     ,	__lowerCAmelCase      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.image_processing_class(**self.image_processor_dict      )

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase__	:      Dict     							=					os.path.join(__lowerCAmelCase     ,	'image_processor.json'      )
              image_processor_first.to_json_file(__lowerCAmelCase      )
              UpperCAmelCase__	:      Tuple     							=					self.image_processing_class.from_json_file(__lowerCAmelCase      ).to_dict()

          UpperCAmelCase__	:      Dict     							=					image_processor_first.to_dict()
          for key, value in image_processor_first.items():
              if key == "clusters":
                  self.assertTrue(np.array_equal(__lowerCAmelCase     ,	image_processor_second[key]      )      )
              else:
                  self.assertEqual(image_processor_first[key]     ,	__lowerCAmelCase      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.image_processing_class(**self.image_processor_dict      )

          with tempfile.TemporaryDirectory() as tmpdirname:
              image_processor_first.save_pretrained(__lowerCAmelCase      )
              UpperCAmelCase__	:      Tuple     							=					self.image_processing_class.from_pretrained(__lowerCAmelCase      ).to_dict()

          UpperCAmelCase__	:      Optional[int]     							=					image_processor_first.to_dict()
          for key, value in image_processor_first.items():
              if key == "clusters":
                  self.assertTrue(np.array_equal(__lowerCAmelCase     ,	image_processor_second[key]      )      )
              else:
                  self.assertEqual(image_processor_first[key]     ,	__lowerCAmelCase      )



      @unittest.skip('ImageGPT requires clusters at initialization'      )
      def  __snake_case							(   self      ):
          pass




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      List[str]     							=					load_dataset('hf-internal-testing/fixtures_image_utils'    ,split='test' )

    UpperCAmelCase__	:      Dict     							=					Image.open(dataset[4]['file'] )
    UpperCAmelCase__	:      Tuple     							=					Image.open(dataset[5]['file'] )

    UpperCAmelCase__	:      List[str]     							=					[imagea, imagea]

    return images





@require_vision
@require_torch
class 				a			(					unittest.TestCase      ):



      @slow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

          UpperCAmelCase__	:      Dict     							=					prepare_images()

          # test non-batched
          UpperCAmelCase__	:      List[Any]     							=					image_processing(images[0]     ,	return_tensors='pt'      )

          self.assertIsInstance(encoding.input_ids     ,	torch.LongTensor      )
          self.assertEqual(encoding.input_ids.shape     ,	(1, 1_024)      )

          UpperCAmelCase__	:      int     							=					[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist()     ,	__lowerCAmelCase      )

          # test batched
          UpperCAmelCase__	:      str     							=					image_processing(__lowerCAmelCase     ,	return_tensors='pt'      )

          self.assertIsInstance(encoding.input_ids     ,	torch.LongTensor      )
          self.assertEqual(encoding.input_ids.shape     ,	(2, 1_024)      )

          UpperCAmelCase__	:      str     							=					[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,	__lowerCAmelCase      )


"""simple docstring"""


from __future__ import annotations

import pandas as pd






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  _UpperCAmelCase    			=   [0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i]

  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   9_99_99_99_99
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   False

  # Process until all processes are completed
  while complete != no_of_processes:
    for j in range(lowercase		):
      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    			=   9_99_99_99_99

    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      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i] + waiting_time[i]
  return turn_around_time






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  for i in range(lowercase		):
    _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""")
    UpperCAmelCase__			=    int(input())
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    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))
        UpperCAmelCase__		, UpperCAmelCase__			=    map(int, input().split())

    UpperCAmelCase__			=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    UpperCAmelCase__			=    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)
