




"""simple docstring"""






from collections import deque






class lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Tuple       =       process_name  # process name
							_lowerCamelCase	:     int       =       arrival_time  # arrival time of the process
							# completion time of finished process or last interrupted time
							_lowerCamelCase	:     int       =       arrival_time
							_lowerCamelCase	:     Any       =       burst_time  # remaining burst time
							_lowerCamelCase	:     Union[str, Any]       =       0  # total time of the process wait in ready queue
							_lowerCamelCase	:     List[str]       =       0  # time from arrival time to completion time






class lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							# total number of mlfq's queues
							_lowerCamelCase	:     str       =       number_of_queues
							# time slice of queues that round robin algorithm applied
							_lowerCamelCase	:     Tuple       =       time_slices
							# unfinished process is in this ready_queue
							_lowerCamelCase	:     Any       =       queue
							# current time
							_lowerCamelCase	:     Union[str, Any]       =       current_time
							# finished process is in this sequence queue
							_lowerCamelCase	:     deque[Process]       =       deque()
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       []
							for i in range(len(self.finish_queue    )    ):
											sequence.append(self.finish_queue[i].process_name    )
							return sequence
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     List[Any]       =       []
							for i in range(len(lowercase    )    ):
											waiting_times.append(queue[i].waiting_time    )
							return waiting_times
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       []
							for i in range(len(lowercase    )    ):
											turnaround_times.append(queue[i].turnaround_time    )
							return turnaround_times
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Union[str, Any]       =       []
							for i in range(len(lowercase    )    ):
											completion_times.append(queue[i].stop_time    )
							return completion_times
			def    A_       (    self    , lowercase    ):
							return [q.burst_time for q in queue]
			def    A_       (    self    , lowercase    ):
							process.waiting_time += self.current_time - process.stop_time
							return process.waiting_time
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     deque[Process]       =       deque()  # sequence deque of finished process
							while len(lowercase    ) != 0:
											_lowerCamelCase	:     List[Any]       =       ready_queue.popleft()  # current process

											# if process's arrival time is later than current time, update current time
											if self.current_time < cp.arrival_time:
															self.current_time += cp.arrival_time

											# update waiting time of current process
											self.update_waiting_time(lowercase    )
											# update current time
											self.current_time += cp.burst_time
											# finish the process and set the process's burst-time 0
											_lowerCamelCase	:     str       =       0
											# set the process's turnaround time because it is finished
											_lowerCamelCase	:     Optional[Any]       =       self.current_time - cp.arrival_time
											# set the completion time
											_lowerCamelCase	:     List[Any]       =       self.current_time
											# add the process to queue that has finished queue
											finished.append(lowercase    )

							self.finish_queue.extend(lowercase    )  # add finished process to finish queue
							# FCFS will finish all remaining processes
							return finished
			def    A_       (    self    , lowercase    , lowercase    ):
							_lowerCamelCase	:     deque[Process]       =       deque()  # sequence deque of terminated process
							# just for 1 cycle and unfinished processes will go back to queue
							for _ in range(len(lowercase    )    ):
											_lowerCamelCase	:     Optional[Any]       =       ready_queue.popleft()  # current process

											# if process's arrival time is later than current time, update current time
											if self.current_time < cp.arrival_time:
															self.current_time += cp.arrival_time

											# update waiting time of unfinished processes
											self.update_waiting_time(lowercase    )
											# if the burst time of process is bigger than time-slice
											if cp.burst_time > time_slice:
															# use CPU for only time-slice
															self.current_time += time_slice
															# update remaining burst time
															cp.burst_time -= time_slice
															# update end point time
															_lowerCamelCase	:     str       =       self.current_time
															# locate the process behind the queue because it is not finished
															ready_queue.append(lowercase    )
											else:
															# use CPU for remaining burst time
															self.current_time += cp.burst_time
															# set burst time 0 because the process is finished
															_lowerCamelCase	:     Optional[Any]       =       0
															# set the finish time
															_lowerCamelCase	:     int       =       self.current_time
															# update the process' turnaround time because it is finished
															_lowerCamelCase	:     List[Any]       =       self.current_time - cp.arrival_time
															# add the process to queue that has finished queue
															finished.append(lowercase    )

							self.finish_queue.extend(lowercase    )  # add finished process to finish queue
							# return finished processes queue and remaining processes queue
							return finished, ready_queue






			def    A_       (    self    ):

							#  all queues except last one have round_robin algorithm
							for i in range(self.number_of_queues - 1    ):
											_lowerCamelCase,					_lowerCamelCase	:     Optional[int]       =       self.round_robin(
											    self.ready_queue    , self.time_slices[i]    )
							#  the last queue has first_come_first_served algorithm
							self.first_come_first_served(self.ready_queue    )

							return self.finish_queue


if __name__ == "__main__":
			import doctest

			lowercase__			=		Process("""P1""", 0, 53)
			lowercase__			=		Process("""P2""", 0, 17)
			lowercase__			=		Process("""P3""", 0, 68)
			lowercase__			=		Process("""P4""", 0, 24)
			lowercase__			=		3
			lowercase__			=		[17, 25]
			lowercase__			=		deque([Pa, Pa, Pa, Pa])

			if len(time_slices) != number_of_queues - 1:
						raise SystemExit(0)

			doctest.testmod(extraglobs={"""queue""": deque([Pa, Pa, Pa, Pa])})

			lowercase__			=		Process("""P1""", 0, 53)
			lowercase__			=		Process("""P2""", 0, 17)
			lowercase__			=		Process("""P3""", 0, 68)
			lowercase__			=		Process("""P4""", 0, 24)
			lowercase__			=		3
			lowercase__			=		[17, 25]
			lowercase__			=		deque([Pa, Pa, Pa, Pa])
			lowercase__			=		MLFQ(number_of_queues, time_slices, queue, 0)
			lowercase__			=		mlfq.multi_level_feedback_queue()

			# print total waiting times of processes(P1, P2, P3, P4)
			print(
			    F"waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
			)
			# print completion times of processes(P1, P2, P3, P4)
			print(
			    F"completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
			)
			# print total turnaround times of processes(P1, P2, P3, P4)
			print(
			    F"turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
			)
			# print sequence of finished processes
			print(
			    F"sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}"
			)





"""simple docstring"""






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
			import torch

			from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer






class lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=13    , lowercase=7    , lowercase=True    , lowercase=True    , lowercase=False    , lowercase=True    , lowercase=99    , lowercase=32    , lowercase=5    , lowercase=4    , lowercase=37    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=16    , lowercase=2    , lowercase=0.02    , lowercase=3    , lowercase=4    , lowercase=None    , ):
							_lowerCamelCase	:     Tuple       =       parent
							_lowerCamelCase	:     Optional[int]       =       batch_size
							_lowerCamelCase	:     str       =       seq_length
							_lowerCamelCase	:     Dict       =       is_training
							_lowerCamelCase	:     List[Any]       =       use_input_mask
							_lowerCamelCase	:     Optional[int]       =       use_token_type_ids
							_lowerCamelCase	:     str       =       use_labels
							_lowerCamelCase	:     int       =       vocab_size
							_lowerCamelCase	:     Any       =       hidden_size
							_lowerCamelCase	:     Tuple       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     Union[str, Any]       =       intermediate_size
							_lowerCamelCase	:     Dict       =       hidden_act
							_lowerCamelCase	:     Any       =       hidden_dropout_prob
							_lowerCamelCase	:     str       =       attention_probs_dropout_prob
							_lowerCamelCase	:     List[Any]       =       max_position_embeddings
							_lowerCamelCase	:     Any       =       type_vocab_size
							_lowerCamelCase	:     Dict       =       type_sequence_label_size
							_lowerCamelCase	:     List[Any]       =       initializer_range
							_lowerCamelCase	:     Any       =       num_labels
							_lowerCamelCase	:     List[str]       =       num_choices
							_lowerCamelCase	:     Tuple       =       scope
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size    )

							_lowerCamelCase	:     int       =       None
							if self.use_input_mask:
											_lowerCamelCase	:     str       =       random_attention_mask([self.batch_size, self.seq_length]    )

							_lowerCamelCase	:     Tuple       =       None
							if self.use_token_type_ids:
											_lowerCamelCase	:     Optional[int]       =       ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size    )

							_lowerCamelCase	:     List[Any]       =       None
							_lowerCamelCase	:     Optional[Any]       =       None
							_lowerCamelCase	:     Optional[Any]       =       None
							if self.use_labels:
											_lowerCamelCase	:     Any       =       ids_tensor([self.batch_size]    , self.type_sequence_label_size    )
											_lowerCamelCase	:     Tuple       =       ids_tensor([self.batch_size, self.seq_length]    , self.num_labels    )
											_lowerCamelCase	:     Optional[Any]       =       ids_tensor([self.batch_size]    , self.num_choices    )

							_lowerCamelCase	:     List[Any]       =       self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def    A_       (    self    ):
							return LlamaConfig(
							    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    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     str       =       LlamaModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    )
							_lowerCamelCase	:     Any       =       model(lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     str       =       True
							_lowerCamelCase	:     Dict       =       LlamaModel(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , )
							_lowerCamelCase	:     Tuple       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , )
							_lowerCamelCase	:     Union[str, Any]       =       model(lowercase    , attention_mask=lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     Optional[Any]       =       LlamaForCausalLM(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     int       =       True
							_lowerCamelCase	:     Optional[Any]       =       True
							_lowerCamelCase	:     str       =       LlamaForCausalLM(config=lowercase    )
							model.to(lowercase    )
							model.eval()

							# first forward pass
							_lowerCamelCase	:     Dict       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , use_cache=lowercase    , )
							_lowerCamelCase	:     Union[str, Any]       =       outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							_lowerCamelCase	:     List[Any]       =       ids_tensor((self.batch_size, 3)    , config.vocab_size    )
							_lowerCamelCase	:     str       =       ids_tensor((self.batch_size, 3)    , vocab_size=2    )

							# append to next input_ids and
							_lowerCamelCase	:     Optional[Any]       =       torch.cat([input_ids, next_tokens]    , dim=-1    )
							_lowerCamelCase	:     int       =       torch.cat([input_mask, next_mask]    , dim=-1    )

							_lowerCamelCase	:     Any       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , output_hidden_states=lowercase    , )['hidden_states'][0]
							_lowerCamelCase	:     Dict       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , past_key_values=lowercase    , output_hidden_states=lowercase    , )['hidden_states'][0]

							# select random slice
							_lowerCamelCase	:     Optional[Any]       =       ids_tensor((1,)    , output_from_past.shape[-1]    ).item()
							_lowerCamelCase	:     Any       =       output_from_no_past[:, -3:, random_slice_idx].detach()
							_lowerCamelCase	:     Any       =       output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(lowercase    , lowercase    , atol=1E-3    )    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       self.prepare_config_and_inputs()
							(
							    (
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					
							)	:     List[str]       =       config_and_inputs
							_lowerCamelCase	:     int       =       {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
			lowerCamelCase__ =     (LlamaForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__ =     (
			    {
			        """feature-extraction""": LlamaModel,
			        """text-classification""": LlamaForSequenceClassification,
			        """text-generation""": LlamaForCausalLM,
			        """zero-shot""": LlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       LlamaModelTester(self    )
							_lowerCamelCase	:     List[Any]       =       ConfigTester(self    , config_class=lowercase    , hidden_size=37    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											_lowerCamelCase	:     Any       =       type
											self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[str]       =       3
							_lowerCamelCase	:     Dict       =       input_dict['input_ids']
							_lowerCamelCase	:     List[str]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     Tuple       =       ids_tensor([self.model_tester.batch_size]    , self.model_tester.type_sequence_label_size    )
							_lowerCamelCase	:     Union[str, Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[str]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.assertEqual(result.logits.shape    , (self.model_tester.batch_size, self.model_tester.num_labels)    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Optional[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     Dict       =       3
							_lowerCamelCase	:     List[str]       =       'single_label_classification'
							_lowerCamelCase	:     Union[str, Any]       =       input_dict['input_ids']
							_lowerCamelCase	:     List[Any]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       ids_tensor([self.model_tester.batch_size]    , self.model_tester.type_sequence_label_size    )
							_lowerCamelCase	:     List[Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     int       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.assertEqual(result.logits.shape    , (self.model_tester.batch_size, self.model_tester.num_labels)    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[Any]       =       3
							_lowerCamelCase	:     Union[str, Any]       =       'multi_label_classification'
							_lowerCamelCase	:     Dict       =       input_dict['input_ids']
							_lowerCamelCase	:     Optional[Any]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     List[Any]       =       ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]    , self.model_tester.type_sequence_label_size    ).to(torch.float    )
							_lowerCamelCase	:     Optional[Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     int       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.assertEqual(result.logits.shape    , (self.model_tester.batch_size, self.model_tester.num_labels)    )
			@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'    )
			def    A_       (    self    ):
							pass






			@parameterized.expand([('linear',), ('dynamic',)]    )
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase,					_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[Any]       =       ids_tensor([1, 10]    , config.vocab_size    )
							_lowerCamelCase	:     int       =       ids_tensor([1, int(config.max_position_embeddings * 1.5    )]    , config.vocab_size    )

							set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
							_lowerCamelCase	:     Union[str, Any]       =       LlamaModel(lowercase    )
							original_model.to(lowercase    )
							original_model.eval()
							_lowerCamelCase	:     str       =       original_model(lowercase    ).last_hidden_state
							_lowerCamelCase	:     int       =       original_model(lowercase    ).last_hidden_state

							set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
							_lowerCamelCase	:     List[str]       =       {'type': scaling_type, 'factor': 10.0}
							_lowerCamelCase	:     Optional[int]       =       LlamaModel(lowercase    )
							scaled_model.to(lowercase    )
							scaled_model.eval()
							_lowerCamelCase	:     int       =       scaled_model(lowercase    ).last_hidden_state
							_lowerCamelCase	:     Union[str, Any]       =       scaled_model(lowercase    ).last_hidden_state

							# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
							# maximum sequence length, so the outputs for the short input should match.
							if scaling_type == "dynamic":
											self.assertTrue(torch.allclose(lowercase    , lowercase    , atol=1E-5    )    )
							else:
											self.assertFalse(torch.allclose(lowercase    , lowercase    , atol=1E-5    )    )

							# The output should be different for long inputs
							self.assertFalse(torch.allclose(lowercase    , lowercase    , atol=1E-5    )    )






@require_torch
class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     Dict       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'    , device_map='auto'    )
							_lowerCamelCase	:     Union[str, Any]       =       model(torch.tensor([input_ids]    )    )
							# Expected mean on dim = -1
							_lowerCamelCase	:     List[str]       =       torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]]    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_lowerCamelCase	:     Any       =       torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,]    )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]    , lowercase    , atol=1E-5    , rtol=1E-5    )
			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     str       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'    , device_map='auto'    )
							_lowerCamelCase	:     List[Any]       =       model(torch.tensor(lowercase    )    )
							# Expected mean on dim = -1
							_lowerCamelCase	:     Tuple       =       torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]]    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_lowerCamelCase	:     Dict       =       torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73]    )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]    , lowercase    , atol=1E-5    , rtol=1E-5    )
			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     Optional[Any]       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    , device_map='auto'    )
							_lowerCamelCase	:     int       =       model(torch.tensor(lowercase    )    )
							# Expected mean on dim = -1
							_lowerCamelCase	:     str       =       torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]]    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_lowerCamelCase	:     Union[str, Any]       =       torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13]    )
							# fmt: on
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
			@unittest.skip(
			    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     int       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'    , device_map='auto'    )
							_lowerCamelCase	:     Union[str, Any]       =       model(torch.tensor(lowercase    )    )

							_lowerCamelCase	:     Optional[Any]       =       torch.tensor(
							    [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]]    , dtype=torch.floataa    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# fmt: off
							_lowerCamelCase	:     List[str]       =       torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12]    )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]    , lowercase    , atol=1E-5    , rtol=1E-5    )






			@unittest.skip('Model is curently gated'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
							_lowerCamelCase	:     str       =       'Simply put, the theory of relativity states that '
							_lowerCamelCase	:     List[Any]       =       LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    )
							_lowerCamelCase	:     List[str]       =       tokenizer.encode(lowercase    , return_tensors='pt'    )
							_lowerCamelCase	:     List[str]       =       LlamaForCausalLM.from_pretrained(
							    'meta-llama/Llama-2-13b-chat-hf'    , device_map='sequential'    , use_safetensors=lowercase    )

							# greedy generation outputs
							_lowerCamelCase	:     Optional[int]       =       model.generate(lowercase    , max_new_tokens=64    , top_p=lowercase    , temperature=1    , do_sample=lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tokenizer.decode(generated_ids[0]    , skip_special_tokens=lowercase    )
							self.assertEqual(lowercase    , lowercase    )