
import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowercase_ 				=			{"""LayoutLMv2Config""", """LayoutLMv3Config"""}





@is_pipeline_test
class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     lowerCAmelCase_							   =			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

     if model_mapping is not None:
          lowerCAmelCase_							   =			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
     if tf_model_mapping is not None:
          lowerCAmelCase_							   =			{
              config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
          }



     @require_torch
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		pipeline(
           task='''text-classification'''     ,						model='''hf-internal-testing/tiny-random-distilbert'''     ,						framework='''pt''' )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		text_classifier('''This is great !''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''LABEL_0''', '''score''': 0.5_04}] )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		text_classifier('''This is great !'''     ,						top_k=2 )
       self.assertEqual(
           nested_simplify(_A )     ,						[{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}] )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		text_classifier(['''This is great !''', '''This is bad''']     ,						top_k=2 )
       self.assertEqual(
           nested_simplify(_A )     ,						[
               [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
               [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
           ]     ,						)

       __SCREAMING_SNAKE_CASE      :	str					=		text_classifier('''This is great !'''     ,						top_k=1 )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''LABEL_0''', '''score''': 0.5_04}] )

       # Legacy behavior
       __SCREAMING_SNAKE_CASE      :	str					=		text_classifier('''This is great !'''     ,						return_all_scores=_A )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''LABEL_0''', '''score''': 0.5_04}] )

       __SCREAMING_SNAKE_CASE      :	str					=		text_classifier('''This is great !'''     ,						return_all_scores=_A )
       self.assertEqual(
           nested_simplify(_A )     ,						[[{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}]] )

       __SCREAMING_SNAKE_CASE      :	str					=		text_classifier(['''This is great !''', '''Something else''']     ,						return_all_scores=_A )
       self.assertEqual(
           nested_simplify(_A )     ,						[
               [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
               [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
           ]     ,						)

       __SCREAMING_SNAKE_CASE      :	Dict					=		text_classifier(['''This is great !''', '''Something else''']     ,						return_all_scores=_A )
       self.assertEqual(
           nested_simplify(_A )     ,						[
               {'''label''': '''LABEL_0''', '''score''': 0.5_04},
               {'''label''': '''LABEL_0''', '''score''': 0.5_04},
           ]     ,						)



     @require_torch
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       import torch

       __SCREAMING_SNAKE_CASE      :	Tuple					=		pipeline(
           task='''text-classification'''     ,						model='''hf-internal-testing/tiny-random-distilbert'''     ,						framework='''pt'''     ,						device=torch.device('''cpu''' )     ,						)

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		text_classifier('''This is great !''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''LABEL_0''', '''score''': 0.5_04}] )



     @require_tf
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		pipeline(
           task='''text-classification'''     ,						model='''hf-internal-testing/tiny-random-distilbert'''     ,						framework='''tf''' )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		text_classifier('''This is great !''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''LABEL_0''', '''score''': 0.5_04}] )



     @slow
     @require_torch
     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		pipeline('''text-classification''' )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		text_classifier('''This is great !''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''POSITIVE''', '''score''': 1.0}] )
       __SCREAMING_SNAKE_CASE      :	Dict					=		text_classifier('''This is bad !''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''NEGATIVE''', '''score''': 1.0}] )
       __SCREAMING_SNAKE_CASE      :	str					=		text_classifier('''Birds are a type of animal''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''POSITIVE''', '''score''': 0.9_88}] )



     @slow
     @require_tf
     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		pipeline('''text-classification'''     ,						framework='''tf''' )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		text_classifier('''This is great !''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''POSITIVE''', '''score''': 1.0}] )
       __SCREAMING_SNAKE_CASE      :	Dict					=		text_classifier('''This is bad !''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''NEGATIVE''', '''score''': 1.0}] )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		text_classifier('''Birds are a type of animal''' )
       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': '''POSITIVE''', '''score''': 0.9_88}] )



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							int     ,						_A							:							Any     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		TextClassificationPipeline(model=_A     ,						tokenizer=_A )
       return text_classifier, ["HuggingFace is in", "This is another test"]




     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							Optional[int]     ,						_A							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		'''HuggingFace is in'''
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		text_classifier(_A )

       self.assertEqual(nested_simplify(_A )     ,						[{'''label''': ANY(_A ), '''score''': ANY(_A )}] )
       self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values() )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		['''HuggingFace is in ''', '''Paris is in France''']
       __SCREAMING_SNAKE_CASE      :	int					=		text_classifier(_A )
       self.assertEqual(
           nested_simplify(_A )     ,						[{'''label''': ANY(_A ), '''score''': ANY(_A )}, {'''label''': ANY(_A ), '''score''': ANY(_A )}]     ,						)
       self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values() )
       self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values() )

       # Forcing to get all results with `top_k=None`
       # This is NOT the legacy format
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		text_classifier(_A     ,						top_k=_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		len(model.config.idalabel.values() )
       self.assertEqual(
           nested_simplify(_A )     ,						[[{'''label''': ANY(_A ), '''score''': ANY(_A )}] * N, [{'''label''': ANY(_A ), '''score''': ANY(_A )}] * N]     ,						)

       __SCREAMING_SNAKE_CASE      :	Tuple					=		{'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		text_classifier(_A )
       self.assertEqual(
           nested_simplify(_A )     ,						{'''label''': ANY(_A ), '''score''': ANY(_A )}     ,						)
       self.assertTrue(outputs['''label'''] in model.config.idalabel.values() )

       # This might be used a text pair, but tokenizer + pipe interaction
       # makes it hard to understand that it's not using the pair properly
       # https://github.com/huggingface/transformers/issues/17305
       # We disabled this usage instead as it was outputting wrong outputs.
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[['''HuggingFace is in ''', '''Paris is in France''']]
       with self.assertRaises(_A ):
         text_classifier(_A )

       # This used to be valid for doing text pairs
       # We're keeping it working because of backward compatibility
       __SCREAMING_SNAKE_CASE      :	Any					=		text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]] )
       self.assertEqual(
           nested_simplify(_A )     ,						[{'''label''': ANY(_A ), '''score''': ANY(_A )}]     ,						)
       self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values() )







"""simple docstring"""




from __future__ import annotations

from statistics import mean






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       list[int]       ,			_lowercase		:       int   )					->list[int]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[0] * no_of_processes
	a :						int      =							[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowercase   ):
		a :						List[Any]      =							burst_time[i]
	a :						list[int]      =							[]

	a :						List[Any]      =							0
	a :						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:
		a :						str      =							[]
		a :						Tuple      =							-1

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

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

	return waiting_time






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       int       ,			_lowercase		:       list[int]   )					->list[int]:






	'''simple docstring'''






	a :						List[str]      =							[0] * no_of_processes
	for i in range(_lowercase   ):
		a :						Optional[int]      =							burst_time[i] + waiting_time[i]
	return turn_around_time


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

		a		:					Dict	      =     4
		a		:					Tuple	      =     [2, 5, 3, 7]
		a		:					List[Any]	      =     [0, 0, 0, 0]
		a		:					int	      =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		a		:					List[Any]	      =     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}''')


