
"""simple docstring"""





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class 		SCREAMING_SNAKE_CASE_					(		unittest.TestCase					):




		"""simple docstring"""





		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					tempfile.mkdtemp()

				__SCREAMING_SNAKE_CASE				=					BlipImageProcessor()
				__SCREAMING_SNAKE_CASE				=					GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""")

				__SCREAMING_SNAKE_CASE				=					BlipaProcessor(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__)

				processor.save_pretrained(self.tmpdirname)




		def       snake_case_	(	self				,    **lowerCAmelCase__):
				return AutoProcessor.from_pretrained(self.tmpdirname				,    **SCREAMING_SNAKE_CASE__).tokenizer




		def       snake_case_	(	self				,    **lowerCAmelCase__):
				return AutoProcessor.from_pretrained(self.tmpdirname				,    **SCREAMING_SNAKE_CASE__).image_processor




		def       snake_case_	(	self):
				shutil.rmtree(self.tmpdirname)




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					[np.random.randint(2_5_5				,    size=(3, 3_0, 4_0_0)				,    dtype=np.uinta)]

				__SCREAMING_SNAKE_CASE				=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__				,    0				,    -1)) for x in image_inputs]

				return image_inputs




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					BlipaProcessor(tokenizer=self.get_tokenizer()				,    image_processor=self.get_image_processor())
				processor.save_pretrained(self.tmpdirname)

				__SCREAMING_SNAKE_CASE				=					self.get_tokenizer(bos_token="""(BOS)"""				,    eos_token="""(EOS)""")
				__SCREAMING_SNAKE_CASE				=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__				,    padding_value=1.0)

				__SCREAMING_SNAKE_CASE				=					BlipaProcessor.from_pretrained(
				    self.tmpdirname				,    bos_token="""(BOS)"""				,    eos_token="""(EOS)"""				,    do_normalize=SCREAMING_SNAKE_CASE__				,    padding_value=1.0)

				self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab())
				self.assertIsInstance(processor.tokenizer				,    SCREAMING_SNAKE_CASE__)

				self.assertEqual(processor.image_processor.to_json_string()				,    image_processor_add_kwargs.to_json_string())
				self.assertIsInstance(processor.image_processor				,    SCREAMING_SNAKE_CASE__)




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.get_image_processor()
				__SCREAMING_SNAKE_CASE				=					self.get_tokenizer()

				__SCREAMING_SNAKE_CASE				=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,    image_processor=SCREAMING_SNAKE_CASE__)

				__SCREAMING_SNAKE_CASE				=					self.prepare_image_inputs()

				__SCREAMING_SNAKE_CASE				=					image_processor(SCREAMING_SNAKE_CASE__				,    return_tensors="""np""")
				__SCREAMING_SNAKE_CASE				=					processor(images=SCREAMING_SNAKE_CASE__				,    return_tensors="""np""")

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1E-2)




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.get_image_processor()
				__SCREAMING_SNAKE_CASE				=					self.get_tokenizer()

				__SCREAMING_SNAKE_CASE				=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,    image_processor=SCREAMING_SNAKE_CASE__)

				__SCREAMING_SNAKE_CASE				=					"""lower newer"""

				__SCREAMING_SNAKE_CASE				=					processor(text=SCREAMING_SNAKE_CASE__)

				__SCREAMING_SNAKE_CASE				=					tokenizer(SCREAMING_SNAKE_CASE__				,    return_token_type_ids=SCREAMING_SNAKE_CASE__)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]				,    encoded_processor[key])




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.get_image_processor()
				__SCREAMING_SNAKE_CASE				=					self.get_tokenizer()

				__SCREAMING_SNAKE_CASE				=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,    image_processor=SCREAMING_SNAKE_CASE__)

				__SCREAMING_SNAKE_CASE				=					"""lower newer"""
				__SCREAMING_SNAKE_CASE				=					self.prepare_image_inputs()

				__SCREAMING_SNAKE_CASE				=					processor(text=SCREAMING_SNAKE_CASE__				,    images=SCREAMING_SNAKE_CASE__)

				self.assertListEqual(list(inputs.keys())				,    ["""pixel_values""", """input_ids""", """attention_mask"""])

				# test if it raises when no input is passed
				with pytest.raises(SCREAMING_SNAKE_CASE__):
						processor()




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.get_image_processor()
				__SCREAMING_SNAKE_CASE				=					self.get_tokenizer()

				__SCREAMING_SNAKE_CASE				=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,    image_processor=SCREAMING_SNAKE_CASE__)

				__SCREAMING_SNAKE_CASE				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				__SCREAMING_SNAKE_CASE				=					processor.batch_decode(SCREAMING_SNAKE_CASE__)
				__SCREAMING_SNAKE_CASE				=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__)

				self.assertListEqual(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__)




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.get_image_processor()
				__SCREAMING_SNAKE_CASE				=					self.get_tokenizer()

				__SCREAMING_SNAKE_CASE				=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,    image_processor=SCREAMING_SNAKE_CASE__)

				__SCREAMING_SNAKE_CASE				=					"""lower newer"""
				__SCREAMING_SNAKE_CASE				=					self.prepare_image_inputs()

				__SCREAMING_SNAKE_CASE				=					processor(text=SCREAMING_SNAKE_CASE__				,    images=SCREAMING_SNAKE_CASE__)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys())				,    ["""pixel_values""", """input_ids""", """attention_mask"""])









"""simple docstring"""







from statistics import mean

import numpy as np

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





	A__											=					0
	# Number of processes finished
	A__											=					0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	A__											=					[0] * no_of_process
	# List to include calculation results
	A__											=					[0] * no_of_process

	# Sort by arrival time.
	A__											=					[burst_time[i] for i in np.argsort(UpperCAmelCase_     )]
	A__											=					[process_name[i] for i in np.argsort(UpperCAmelCase_     )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		A__											=					0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			A__											=					arrival_time[i]

		A__											=					0
		# Index showing the location of the process being performed
		A__											=					0
		# Saves the current response ratio.
		A__											=					0
		for i in range(0,	UpperCAmelCase_     ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				A__											=					(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				A__											=					temp
				A__											=					i

        # Calculate the turn around time
		A__											=					current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		A__											=					1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





	A__											=					[0] * no_of_process
	for i in range(0,	UpperCAmelCase_     ):
		A__											=					turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
			UpperCamelCase     =							5
			UpperCamelCase     =							["""A""", """B""", """C""", """D""", """E"""]
			UpperCamelCase     =							[1, 2, 3, 4, 5]
			UpperCamelCase     =							[1, 2, 3, 4, 5]

			UpperCamelCase     =							calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			UpperCamelCase     =							calculate_waiting_time(
			    process_name, turn_around_time, burst_time, no_of_process
			)

			print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
			for i in range(0, no_of_process):
						print(
						    f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
						    f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
						)

			print(f'average waiting time : {mean(waiting_time):.5f}')
			print(f'average turn around time : {mean(turn_around_time):.5f}')




