
import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    NezhaForMaskedLM,
							    NezhaForMultipleChoice,
							    NezhaForNextSentencePrediction,
							    NezhaForPreTraining,
							    NezhaForQuestionAnswering,
							    NezhaForSequenceClassification,
							    NezhaForTokenClassification,
							    NezhaModel,
							)
							from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST







class     lowercase		:




							def __init__(    self       ,A__       ,A__=1_3       ,A__=7       ,A__=True       ,A__=True       ,A__=True       ,A__=True       ,A__=9_9       ,A__=3_2       ,A__=5       ,A__=4       ,A__=3_7       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=1_2_8       ,A__=3_2       ,A__=1_6       ,A__=2       ,A__=0.02       ,A__=3       ,A__=4       ,A__=None       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						seq_length
												lowercase						     =						is_training
												lowercase						     =						use_input_mask
												lowercase						     =						use_token_type_ids
												lowercase						     =						use_labels
												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_act
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						type_sequence_label_size
												lowercase						     =						initializer_range
												lowercase						     =						num_labels
												lowercase						     =						num_choices
												lowercase						     =						scope




							def 			A__				(    self):
												lowercase						     =						ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size)

												lowercase						     =						None
												if self.use_input_mask:
																	lowercase						     =						random_attention_mask([self.batch_size, self.seq_length])

												lowercase						     =						None
												if self.use_token_type_ids:
																	lowercase						     =						ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size)

												lowercase						     =						None
												lowercase						     =						None
												lowercase						     =						None
												if self.use_labels:
																	lowercase						     =						ids_tensor([self.batch_size]       ,self.type_sequence_label_size)
																	lowercase						     =						ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels)
																	lowercase						     =						ids_tensor([self.batch_size]       ,self.num_choices)

												lowercase						     =						self.get_config()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def 			A__				(    self):
												return NezhaConfig(
												    vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,is_decoder=__lowercase       ,initializer_range=self.initializer_range       ,)




							def 			A__				(    self):
												(
												    lowercase	
												)						     =						self.prepare_config_and_inputs()

												lowercase						     =						True
												lowercase						     =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
												lowercase						     =						ids_tensor([self.batch_size, self.seq_length]       ,vocab_size=2)

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    input_mask,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						NezhaModel(config=__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(__lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase)
												lowercase						     =						model(__lowercase       ,token_type_ids=__lowercase)
												lowercase						     =						model(__lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))
												self.parent.assertEqual(result.pooler_output.shape       ,(self.batch_size, self.hidden_size))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,):
												lowercase						     =						True
												lowercase						     =						NezhaModel(__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(
												    __lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,encoder_hidden_states=__lowercase       ,encoder_attention_mask=__lowercase       ,)
												lowercase						     =						model(
												    __lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,encoder_hidden_states=__lowercase       ,)
												lowercase						     =						model(__lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase)
												self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))
												self.parent.assertEqual(result.pooler_output.shape       ,(self.batch_size, self.hidden_size))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						NezhaForMaskedLM(config=__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(__lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,labels=__lowercase)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						NezhaForNextSentencePrediction(config=__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(
												    __lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,labels=__lowercase       ,)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, 2))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						NezhaForPreTraining(config=__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(
												    __lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,labels=__lowercase       ,next_sentence_label=__lowercase       ,)
												self.parent.assertEqual(result.prediction_logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size))
												self.parent.assertEqual(result.seq_relationship_logits.shape       ,(self.batch_size, 2))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						NezhaForQuestionAnswering(config=__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(
												    __lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,start_positions=__lowercase       ,end_positions=__lowercase       ,)
												self.parent.assertEqual(result.start_logits.shape       ,(self.batch_size, self.seq_length))
												self.parent.assertEqual(result.end_logits.shape       ,(self.batch_size, self.seq_length))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						NezhaForSequenceClassification(__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(__lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,labels=__lowercase)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						NezhaForTokenClassification(config=__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						model(__lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,labels=__lowercase)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_choices
												lowercase						     =						NezhaForMultipleChoice(config=__lowercase)
												model.to(__lowercase)
												model.eval()
												lowercase						     =						input_ids.unsqueeze(1).expand(-1       ,self.num_choices       ,-1).contiguous()
												lowercase						     =						token_type_ids.unsqueeze(1).expand(-1       ,self.num_choices       ,-1).contiguous()
												lowercase						     =						input_mask.unsqueeze(1).expand(-1       ,self.num_choices       ,-1).contiguous()
												lowercase						     =						model(
												    __lowercase       ,attention_mask=__lowercase       ,token_type_ids=__lowercase       ,labels=__lowercase       ,)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_choices))





							def 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()
												(
												    lowercase	
												)						     =						config_and_inputs
												lowercase						     =						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_torch
class     lowercase		( UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  unittest.TestCase		):
							lowercase_	:	Any	 =(
							    (
							        NezhaModel,
							        NezhaForMaskedLM,
							        NezhaForMultipleChoice,
							        NezhaForNextSentencePrediction,
							        NezhaForPreTraining,
							        NezhaForQuestionAnswering,
							        NezhaForSequenceClassification,
							        NezhaForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_	:	Dict	 =(
							    {
							        """feature-extraction""": NezhaModel,
							        """fill-mask""": NezhaForMaskedLM,
							        """question-answering""": NezhaForQuestionAnswering,
							        """text-classification""": NezhaForSequenceClassification,
							        """token-classification""": NezhaForTokenClassification,
							        """zero-shot""": NezhaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase_	:	Optional[int]	 =True




							def 			A__				(    self       ,A__       ,A__       ,A__=False):
												lowercase						     =						super()._prepare_for_class(__lowercase       ,__lowercase       ,return_labels=__lowercase)

												if return_labels:
																	if model_class in get_values(__lowercase):
																						lowercase						     =						torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length)       ,dtype=torch.long       ,device=__lowercase)
																						lowercase						     =						torch.zeros(
																						    self.model_tester.batch_size       ,dtype=torch.long       ,device=__lowercase)
												return inputs_dict




							def 			A__				(    self):
												lowercase						     =						NezhaModelTester(self)
												lowercase						     =						ConfigTester(self       ,config_class=__lowercase       ,hidden_size=3_7)




							def 			A__				(    self):
												self.config_tester.run_common_tests()




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__lowercase)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(*__lowercase)




							def 			A__				(    self):
												# This regression test was failing with PyTorch < 1.3
												(
												    lowercase	
												)						     =						self.model_tester.prepare_config_and_inputs_for_decoder()

												lowercase						     =						None

												self.model_tester.create_and_check_model_as_decoder(
												    __lowercase       ,__lowercase       ,__lowercase       ,__lowercase       ,__lowercase       ,__lowercase       ,__lowercase       ,__lowercase       ,__lowercase       ,)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__lowercase)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*__lowercase)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_next_sequence_prediction(*__lowercase)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__lowercase)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__lowercase)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__lowercase)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__lowercase)




							@slow
							def 			A__				(    self):
												for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase						     =						NezhaModel.from_pretrained(__lowercase)
																	self.assertIsNotNone(__lowercase)





							@slow
							@require_torch_gpu
							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																	# NezhaForMultipleChoice behaves incorrectly in JIT environments.
																	if model_class == NezhaForMultipleChoice:
																						return

																	lowercase						     =						True
																	lowercase						     =						model_class(config=__lowercase)

																	lowercase						     =						self._prepare_for_class(__lowercase       ,__lowercase)
																	lowercase						     =						torch.jit.trace(
																	    __lowercase       ,(inputs_dict['''input_ids'''].to('''cpu'''), inputs_dict['''attention_mask'''].to('''cpu''')))

																	with tempfile.TemporaryDirectory() as tmp:
																						torch.jit.save(__lowercase       ,os.path.join(__lowercase       ,'''bert.pt'''))
																						lowercase						     =						torch.jit.load(os.path.join(__lowercase       ,'''bert.pt''')       ,map_location=__lowercase)
																						loaded(inputs_dict['''input_ids'''].to(__lowercase)       ,inputs_dict['''attention_mask'''].to(__lowercase))







@require_torch
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						NezhaModel.from_pretrained('''sijunhe/nezha-cn-base''')
												lowercase						     =						torch.tensor([[0, 1, 2, 3, 4, 5]])
												lowercase						     =						torch.tensor([[0, 1, 1, 1, 1, 1]])
												with torch.no_grad():
																	lowercase						     =						model(__lowercase       ,attention_mask=__lowercase)[0]
												lowercase						     =						torch.Size((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,__lowercase)
												lowercase						     =						torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]])

												self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,__lowercase       ,atol=1E-4))





							@slow
							def 			A__				(    self):
												lowercase						     =						NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base''')
												lowercase						     =						torch.tensor([[0, 1, 2, 3, 4, 5]])
												lowercase						     =						torch.tensor([[1, 1, 1, 1, 1, 1]])
												with torch.no_grad():
																	lowercase						     =						model(__lowercase       ,attention_mask=__lowercase)[0]
												lowercase						     =						torch.Size((1, 6, 2_1_1_2_8))
												self.assertEqual(output.shape       ,__lowercase)
												lowercase						     =						torch.tensor(
												    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]])

												self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,__lowercase       ,atol=1E-4))




from statistics import mean

import numpy as np



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						0
					# Number of processes finished
					lowercase						     =						0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					lowercase						     =						[0] * no_of_process
					# List to include calculation results
					lowercase						     =						[0] * no_of_process

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

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

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

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

					return turn_around_time



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						[0] * no_of_process
					for i in range(0			,      lowerCAmelCase__		):
										lowercase						     =						turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
							lowercase__		:Dict			  =   5
							lowercase__		:str			  =   ["A", "B", "C", "D", "E"]
							lowercase__		:Optional[int]			  =   [1, 2, 3, 4, 5]
							lowercase__		:List[Any]			  =   [1, 2, 3, 4, 5]

							lowercase__		:List[str]			  =   calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							lowercase__		:List[Any]			  =   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}')




