





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : Tuple		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Any     =		0






						def UpperCAmelCase_			( self : str		)							->      Any:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'		)
							self.assertIsInstance(__snake_case						,				__snake_case		)






						def UpperCAmelCase_			( self : List[Any]		)							->      str:







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdirname:
								snake_case_						:     Optional[int]     =		Path(__snake_case		) / 'preprocessor_config.json'
								snake_case_						:     List[Any]     =		Path(__snake_case		) / 'config.json'
								json.dump(
								    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}						,				open(__snake_case						,				'w'		)						,				)
								json.dump({'model_type': 'clip'}						,				open(__snake_case						,				'w'		)		)

								snake_case_						:     Tuple     =		AutoImageProcessor.from_pretrained(__snake_case		)
								self.assertIsInstance(__snake_case						,				__snake_case		)






						def UpperCAmelCase_			( self : List[str]		)							->      Optional[Any]:







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdirname:
								snake_case_						:     Any     =		Path(__snake_case		) / 'preprocessor_config.json'
								snake_case_						:     Optional[Any]     =		Path(__snake_case		) / 'config.json'
								json.dump(
								    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}						,				open(__snake_case						,				'w'		)						,				)
								json.dump({'model_type': 'clip'}						,				open(__snake_case						,				'w'		)		)

								snake_case_						:     List[str]     =		AutoImageProcessor.from_pretrained(__snake_case		)
								self.assertIsInstance(__snake_case						,				__snake_case		)






						def UpperCAmelCase_			( self : Tuple		)							->      Optional[int]:







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdirname:
								snake_case_						:     Dict     =		CLIPConfig()

								# Create a dummy config file with image_proceesor_type
								snake_case_						:     Dict     =		Path(__snake_case		) / 'preprocessor_config.json'
								snake_case_						:     List[Any]     =		Path(__snake_case		) / 'config.json'
								json.dump(
								    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}						,				open(__snake_case						,				'w'		)						,				)
								json.dump({'model_type': 'clip'}						,				open(__snake_case						,				'w'		)		)

								# remove image_processor_type to make sure config.json alone is enough to load image processor locally
								snake_case_						:     Dict     =		AutoImageProcessor.from_pretrained(__snake_case		).to_dict()

								config_dict.pop('image_processor_type'		)
								snake_case_						:     List[str]     =		CLIPImageProcessor(**__snake_case		)

								# save in new folder
								model_config.save_pretrained(__snake_case		)
								config.save_pretrained(__snake_case		)

								snake_case_						:     Union[str, Any]     =		AutoImageProcessor.from_pretrained(__snake_case		)

								# make sure private variable is not incorrectly saved
								snake_case_						:     Tuple     =		json.loads(config.to_json_string()		)
								self.assertTrue('_processor_class' not in dict_as_saved		)

							self.assertIsInstance(__snake_case						,				__snake_case		)






						def UpperCAmelCase_			( self : List[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdirname:
								snake_case_						:     Optional[int]     =		Path(__snake_case		) / 'preprocessor_config.json'
								json.dump(
								    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}						,				open(__snake_case						,				'w'		)						,				)

								snake_case_						:     List[Any]     =		AutoImageProcessor.from_pretrained(__snake_case		)
								self.assertIsInstance(__snake_case						,				__snake_case		)






						def UpperCAmelCase_			( self : int		)							->      Dict:







							"""simple docstring"""


							with self.assertRaisesRegex(
							    __snake_case						,				'clip-base is not a local folder and is not a valid model identifier'		):
								snake_case_						:     int     =		AutoImageProcessor.from_pretrained('clip-base'		)






						def UpperCAmelCase_			( self : Tuple		)							->      int:







							"""simple docstring"""


							with self.assertRaisesRegex(
							    __snake_case						,				R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'		):
								snake_case_						:     Tuple     =		AutoImageProcessor.from_pretrained(__snake_case						,				revision='aaaaaa'		)






						def UpperCAmelCase_			( self : str		)							->      Union[str, Any]:







							"""simple docstring"""


							with self.assertRaisesRegex(
							    __snake_case						,				'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'						,				):
								snake_case_						:     List[str]     =		AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'		)






						def UpperCAmelCase_			( self : List[str]		)							->      List[str]:







							"""simple docstring"""


							with self.assertRaises(__snake_case		):
								snake_case_						:     Optional[int]     =		AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'		)
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(__snake_case		):
								snake_case_						:     Dict     =		AutoImageProcessor.from_pretrained(
								    'hf-internal-testing/test_dynamic_image_processor'						,				trust_remote_code=__snake_case		)

							snake_case_						:     Optional[Any]     =		AutoImageProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_image_processor'						,				trust_remote_code=__snake_case		)
							self.assertEqual(image_processor.__class__.__name__						,				'NewImageProcessor'		)

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
								image_processor.save_pretrained(__snake_case		)
								snake_case_						:     int     =		AutoImageProcessor.from_pretrained(__snake_case						,				trust_remote_code=__snake_case		)
							self.assertEqual(reloaded_image_processor.__class__.__name__						,				'NewImageProcessor'		)






						def UpperCAmelCase_			( self : List[str]		)							->      Dict:







							"""simple docstring"""


							try:
								AutoConfig.register('custom'						,				__snake_case		)
								AutoImageProcessor.register(__snake_case						,				__snake_case		)
								# Trying to register something existing in the Transformers library will raise an error
								with self.assertRaises(__snake_case		):
									AutoImageProcessor.register(__snake_case						,				__snake_case		)

								with tempfile.TemporaryDirectory() as tmpdirname:
									snake_case_						:     Dict     =		Path(__snake_case		) / 'preprocessor_config.json'
									snake_case_						:     Tuple     =		Path(__snake_case		) / 'config.json'
									json.dump(
									    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}						,				open(__snake_case						,				'w'		)						,				)
									json.dump({'model_type': 'clip'}						,				open(__snake_case						,				'w'		)		)

									snake_case_						:     Tuple     =		CustomImageProcessor.from_pretrained(__snake_case		)

								# Now that the config is registered, it can be used as any other config with the auto-API
								with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(__snake_case		)
									snake_case_						:     str     =		AutoImageProcessor.from_pretrained(__snake_case		)
									self.assertIsInstance(__snake_case						,				__snake_case		)

							finally:
								if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
								if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
									del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






						def UpperCAmelCase_			( self : Dict		)							->      Optional[int]:







							"""simple docstring"""


							class SCREAMING_SNAKE_CASE_  (   A__   ):
													__magic_name__:     List[str]	 =    True

							try:
								AutoConfig.register('custom'						,				__snake_case		)
								AutoImageProcessor.register(__snake_case						,				__snake_case		)
								# If remote code is not set, the default is to use local
								snake_case_						:     List[Any]     =		AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'		)
								self.assertEqual(image_processor.__class__.__name__						,				'NewImageProcessor'		)
								self.assertTrue(image_processor.is_local		)

								# If remote code is disabled, we load the local one.
								snake_case_						:     int     =		AutoImageProcessor.from_pretrained(
								    'hf-internal-testing/test_dynamic_image_processor'						,				trust_remote_code=__snake_case		)
								self.assertEqual(image_processor.__class__.__name__						,				'NewImageProcessor'		)
								self.assertTrue(image_processor.is_local		)

								# If remote is enabled, we load from the Hub
								snake_case_						:     int     =		AutoImageProcessor.from_pretrained(
								    'hf-internal-testing/test_dynamic_image_processor'						,				trust_remote_code=__snake_case		)
								self.assertEqual(image_processor.__class__.__name__						,				'NewImageProcessor'		)
								self.assertTrue(not hasattr(__snake_case						,				'is_local'		)		)

							finally:
								if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
								if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
									del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]








from __future__ import annotations

import pandas as pd


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Optional[Any]     =		[0] * no_of_processes
	snake_case_						:     Tuple     =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		burst_time[i]

	snake_case_						:     Optional[Any]     =		0
	snake_case_						:     Dict     =		0
	snake_case_						:     Any     =		9_99_99_99_99
	snake_case_						:     Tuple     =		0
	snake_case_						:     List[Any]     =		False

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

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

		snake_case_						:     Any     =		remaining_time[short]
		if minm == 0:
			snake_case_						:     Dict     =		9_99_99_99_99

		if remaining_time[short] == 0:
			complete += 1
			snake_case_						:     List[str]     =		False

			# Find finish time of current process
			snake_case_						:     List[str]     =		increment_time + 1

			# Calculate waiting time
			snake_case_						:     Any     =		finish_time - arrival_time[short]
			snake_case_						:     Any     =		finar - burst_time[short]

			if waiting_time[short] < 0:
				snake_case_						:     Optional[int]     =		0

        # Increment time
		increment_time += 1
	return waiting_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		[0] * no_of_processes
	for i in range(__a				):
		snake_case_						:     str     =		burst_time[i] + waiting_time[i]
	return turn_around_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		0
	snake_case_						:     Optional[Any]     =		0
	for i in range(__a				):
		snake_case_						:     int     =		total_waiting_time + waiting_time[i]
		snake_case_						:     Optional[Any]     =		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""")
						_SCREAMING_SNAKE_CASE 		=						int(input())
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_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))
												_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE 		=						map(int, input().split())

						_SCREAMING_SNAKE_CASE 		=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_SCREAMING_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)

