





"""simple docstring"""



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class 						__snake_case   :





 """simple docstring"""


 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return None






class 						__snake_case   :





 """simple docstring"""


 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return None






class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	[
     # (model_name, model_kwargs)
     ("""bert-base-cased""", {}),
     ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
 ]

 @require_tf
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowerCamelCase_   ,		'''tf'''   ,		12   ,		**lowerCamelCase_    )

 @require_torch
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowerCamelCase_   ,		'''pt'''   ,		12   ,		**lowerCamelCase_    )

 @require_torch
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     from transformers import BertModel

     __A       :		str							=	["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
     with NamedTemporaryFile(mode='''w+t'''    ) as vocab_file:
         vocab_file.write('''\n'''.join(lowerCamelCase_    )    )
         vocab_file.flush()
         __A       :		int							=	BertTokenizerFast(vocab_file.name    )

     with TemporaryDirectory() as bert_save_dir:
         __A       :		Tuple							=	BertModel(BertConfig(vocab_size=len(lowerCamelCase_    )    )    )
         model.save_pretrained(lowerCamelCase_    )
         self._test_export(lowerCamelCase_   ,		'''pt'''   ,		12   ,		lowerCamelCase_    )

 @require_tf
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         __A       :		Optional[Any]							=	self._test_export(lowerCamelCase_   ,		'''tf'''   ,		12   ,		**lowerCamelCase_    )
         __A       :		List[str]							=	quantize(Path(lowerCamelCase_    )    )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(lowerCamelCase_    ).stat().st_size:
             self.fail('''Quantized model is bigger than initial ONNX model'''    )

 @require_torch
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         __A       :		Dict							=	self._test_export(lowerCamelCase_   ,		'''pt'''   ,		12   ,		**lowerCamelCase_    )
         __A       :		Optional[int]							=	quantize(lowerCamelCase_    )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(lowerCamelCase_    ).stat().st_size:
             self.fail('''Quantized model is bigger than initial ONNX model'''    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     try:
         # Compute path
         with TemporaryDirectory() as tempdir:
             __A       :		str							=	Path(lowerCamelCase_    ).joinpath('''model.onnx'''    )

         # Remove folder if exists
         if path.parent.exists():
             path.parent.rmdir()

         # Export
         convert(lowerCamelCase_   ,		lowerCamelCase_   ,		lowerCamelCase_   ,		lowerCamelCase_   ,		lowerCamelCase_   ,		**lowerCamelCase_    )

         return path
     except Exception as e:
         self.fail(lowerCamelCase_    )

 @require_torch
 @require_tokenizers
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     from transformers import BertModel

     __A       :		int							=	BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
     __A       :		Optional[int]							=	BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
     self._test_infer_dynamic_axis(lowerCamelCase_   ,		lowerCamelCase_   ,		'''pt'''    )

 @require_tf
 @require_tokenizers
 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     from transformers import TFBertModel

     __A       :		Optional[int]							=	TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
     __A       :		Optional[int]							=	BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
     self._test_infer_dynamic_axis(lowerCamelCase_   ,		lowerCamelCase_   ,		'''tf'''    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	FeatureExtractionPipeline(lowerCamelCase_   ,		lowerCamelCase_    )

     __A       :		Optional[int]							=	["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
     __A       :		List[Any]							=	infer_shapes(lowerCamelCase_   ,		lowerCamelCase_    )

     # Assert all variables are present
     self.assertEqual(len(lowerCamelCase_    )   ,		len(lowerCamelCase_    )    )
     self.assertTrue(all(var_name in shapes for var_name in variable_names    )    )
     self.assertSequenceEqual(variable_names[:3]   ,		lowerCamelCase_    )
     self.assertSequenceEqual(variable_names[3:]   ,		lowerCamelCase_    )

     # Assert inputs are {0: batch, 1: sequence}
     for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
         self.assertDictEqual(shapes[var_name]   ,		{0: '''batch''', 1: '''sequence'''}    )

     # Assert outputs are {0: batch, 1: sequence} and {0: batch}
     self.assertDictEqual(shapes['''output_0''']   ,		{0: '''batch''', 1: '''sequence'''}    )
     self.assertDictEqual(shapes['''output_1''']   ,		{0: '''batch'''}    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	["""input_ids""", """attention_mask""", """token_type_ids"""]
     __A       :		Dict							=	{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
     __A       :		List[Any]							=	ensure_valid_input(FuncContiguousArgs()   ,		lowerCamelCase_   ,		lowerCamelCase_    )

     # Should have exactly the same number of args (all are valid)
     self.assertEqual(len(lowerCamelCase_    )   ,		3    )

     # Should have exactly the same input names
     self.assertEqual(set(lowerCamelCase_    )   ,		set(lowerCamelCase_    )    )

     # Parameter should be reordered according to their respective place in the function:
     # (input_ids, token_type_ids, attention_mask)
     self.assertEqual(lowerCamelCase_   ,		(tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask'''])    )

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     __A       :		Tuple							=	ensure_valid_input(FuncNonContiguousArgs()   ,		lowerCamelCase_   ,		lowerCamelCase_    )

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(lowerCamelCase_    )   ,		1    )
     self.assertEqual(len(lowerCamelCase_    )   ,		1    )

     # Should have only "input_ids"
     self.assertEqual(inputs_args[0]   ,		tokens['''input_ids''']    )
     self.assertEqual(ordered_input_names[0]   ,		'''input_ids'''    )







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''    )   ,		'''-test'''    )
     self.assertEqual('''/home/something/my_fake_model-test.onnx'''   ,		generated.as_posix()    )











'''simple docstring'''




from __future__ import annotations

from statistics import mean



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			[0] * no_of_processes
		SCREAMING_SNAKE_CASE :				int		=			[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[int]		=			burst_time[i]
		SCREAMING_SNAKE_CASE :				list[int]		=			[]

		SCREAMING_SNAKE_CASE :				List[str]		=			0
		SCREAMING_SNAKE_CASE :				List[Any]		=			0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
				SCREAMING_SNAKE_CASE :				Any		=			-1

				for i in range(lowerCamelCase_	):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase_	)

				if len(lowerCamelCase_	) > 0:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										SCREAMING_SNAKE_CASE :				Any		=			i
						total_time += burst_time[target_process]
						completed += 1
						SCREAMING_SNAKE_CASE :				Any		=			0
						SCREAMING_SNAKE_CASE :				Optional[int]		=			(
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			[0] * no_of_processes
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			burst_time[i] + waiting_time[i]
		return turn_around_time


if __name__ == "__main__":
							print("""[TEST CASE 01]""")

							__UpperCAmelCase            =       4
							__UpperCAmelCase            =       [2, 5, 3, 7]
							__UpperCAmelCase            =       [0, 0, 0, 0]
							__UpperCAmelCase            =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
							__UpperCAmelCase            =       calculate_turnaroundtime(
							    burst_time, no_of_processes, waiting_time
							)

							# Printing the Result
							print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
							for i, process_id in enumerate(list(range(1, 5))):
														print(
														    f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
														    f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
														)
							print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
							print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')



