





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    __A       :

			'''simple docstring'''







			def __init__(       self			,    __lowerCAmelCase			,    __lowerCAmelCase=1_3			,    __lowerCAmelCase=7			,    __lowerCAmelCase=True			,    __lowerCAmelCase=True			,    __lowerCAmelCase=False			,    __lowerCAmelCase=True			,    __lowerCAmelCase=9_9			,    __lowerCAmelCase=3_2			,    __lowerCAmelCase=5			,    __lowerCAmelCase=4			,    __lowerCAmelCase=3_7			,    __lowerCAmelCase="gelu"			,    __lowerCAmelCase=0.1			,    __lowerCAmelCase=0.1			,    __lowerCAmelCase=5_1_2			,    __lowerCAmelCase=1_6			,    __lowerCAmelCase=2			,    __lowerCAmelCase=0.02			,    __lowerCAmelCase=3			,    __lowerCAmelCase=4			,    __lowerCAmelCase=None			,    ):



				'''simple docstring'''







				lowerCamelCase__														=						parent
				lowerCamelCase__														=						batch_size
				lowerCamelCase__														=						seq_length
				lowerCamelCase__														=						is_training
				lowerCamelCase__														=						use_input_mask
				lowerCamelCase__														=						use_token_type_ids
				lowerCamelCase__														=						use_labels
				lowerCamelCase__														=						vocab_size
				lowerCamelCase__														=						hidden_size
				lowerCamelCase__														=						num_hidden_layers
				lowerCamelCase__														=						num_attention_heads
				lowerCamelCase__														=						intermediate_size
				lowerCamelCase__														=						hidden_act
				lowerCamelCase__														=						hidden_dropout_prob
				lowerCamelCase__														=						attention_probs_dropout_prob
				lowerCamelCase__														=						max_position_embeddings
				lowerCamelCase__														=						type_vocab_size
				lowerCamelCase__														=						type_sequence_label_size
				lowerCamelCase__														=						initializer_range
				lowerCamelCase__														=						num_labels
				lowerCamelCase__														=						num_choices
				lowerCamelCase__														=						scope





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size				)

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

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

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

				lowerCamelCase__														=						self.get_config()

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





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				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=__lowerCAmelCase			,    initializer_range=self.initializer_range			,    )





			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase				):



				'''simple docstring'''







				lowerCamelCase__														=						LlamaModel(config=__lowerCAmelCase				)
				model.to(__lowerCAmelCase				)
				model.eval()
				lowerCamelCase__														=						model(__lowerCAmelCase			,    attention_mask=__lowerCAmelCase				)
				lowerCamelCase__														=						model(__lowerCAmelCase				)
				self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)				)





			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    ):



				'''simple docstring'''







				lowerCamelCase__														=						True
				lowerCamelCase__														=						LlamaModel(__lowerCAmelCase				)
				model.to(__lowerCAmelCase				)
				model.eval()
				lowerCamelCase__														=						model(
				    __lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    encoder_hidden_states=__lowerCAmelCase			,    encoder_attention_mask=__lowerCAmelCase			,    )
				lowerCamelCase__														=						model(
				    __lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    encoder_hidden_states=__lowerCAmelCase			,    )
				lowerCamelCase__														=						model(__lowerCAmelCase			,    attention_mask=__lowerCAmelCase				)
				self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)				)





			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    ):



				'''simple docstring'''







				lowerCamelCase__														=						LlamaForCausalLM(config=__lowerCAmelCase				)
				model.to(__lowerCAmelCase				)
				model.eval()
				lowerCamelCase__														=						model(__lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    labels=__lowerCAmelCase				)
				self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size)				)





			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    ):



				'''simple docstring'''







				lowerCamelCase__														=						True
				lowerCamelCase__														=						True
				lowerCamelCase__														=						LlamaForCausalLM(config=__lowerCAmelCase				)
				model.to(__lowerCAmelCase				)
				model.eval()

				# first forward pass
				lowerCamelCase__														=						model(
				    __lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    encoder_hidden_states=__lowerCAmelCase			,    encoder_attention_mask=__lowerCAmelCase			,    use_cache=__lowerCAmelCase			,    )
				lowerCamelCase__														=						outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				lowerCamelCase__														=						ids_tensor((self.batch_size, 3)			,    config.vocab_size				)
				lowerCamelCase__														=						ids_tensor((self.batch_size, 3)			,    vocab_size=2				)

				# append to next input_ids and
				lowerCamelCase__														=						torch.cat([input_ids, next_tokens]			,    dim=-1				)
				lowerCamelCase__														=						torch.cat([input_mask, next_mask]			,    dim=-1				)

				lowerCamelCase__														=						model(
				    __lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    encoder_hidden_states=__lowerCAmelCase			,    encoder_attention_mask=__lowerCAmelCase			,    output_hidden_states=__lowerCAmelCase			,    )['''hidden_states'''][0]
				lowerCamelCase__														=						model(
				    __lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    encoder_hidden_states=__lowerCAmelCase			,    encoder_attention_mask=__lowerCAmelCase			,    past_key_values=__lowerCAmelCase			,    output_hidden_states=__lowerCAmelCase			,    )['''hidden_states'''][0]

				# select random slice
				lowerCamelCase__														=						ids_tensor((1,)			,    output_from_past.shape[-1]				).item()
				lowerCamelCase__														=						output_from_no_past[:, -3:, random_slice_idx].detach()
				lowerCamelCase__														=						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(__lowerCAmelCase			,    __lowerCAmelCase			,    atol=1E-3				)				)





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						self.prepare_config_and_inputs()
				(
				    (
				    lowerCamelCase__						
				)							,      (
				    lowerCamelCase__						
				)							,      (
				    lowerCamelCase__						
				)							,      (
				    lowerCamelCase__						
				)							,      (
				    lowerCamelCase__						
				)							,      (
				    lowerCamelCase__						
				)							,      (
				    lowerCamelCase__						
				)							,      						
				)														=						config_and_inputs
				lowerCamelCase__														=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict






@require_torch
class    __A       (       snake_case_     ,							snake_case_     ,							snake_case_     ,							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 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						LlamaModelTester(self				)
				lowerCamelCase__														=						ConfigTester(self			,    config_class=__lowerCAmelCase			,    hidden_size=3_7				)





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowerCAmelCase				)





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					lowerCamelCase__														=						type
					self.model_tester.create_and_check_model(*__lowerCAmelCase				)





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__							,      lowerCamelCase__														=						self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase__														=						3
				lowerCamelCase__														=						input_dict['''input_ids''']
				lowerCamelCase__														=						input_ids.ne(1				).to(__lowerCAmelCase				)
				lowerCamelCase__														=						ids_tensor([self.model_tester.batch_size]			,    self.model_tester.type_sequence_label_size				)
				lowerCamelCase__														=						LlamaForSequenceClassification(__lowerCAmelCase				)
				model.to(__lowerCAmelCase				)
				model.eval()
				lowerCamelCase__														=						model(__lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    labels=__lowerCAmelCase				)
				self.assertEqual(result.logits.shape			,    (self.model_tester.batch_size, self.model_tester.num_labels)				)





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__							,      lowerCamelCase__														=						self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase__														=						3
				lowerCamelCase__														=						'''single_label_classification'''
				lowerCamelCase__														=						input_dict['''input_ids''']
				lowerCamelCase__														=						input_ids.ne(1				).to(__lowerCAmelCase				)
				lowerCamelCase__														=						ids_tensor([self.model_tester.batch_size]			,    self.model_tester.type_sequence_label_size				)
				lowerCamelCase__														=						LlamaForSequenceClassification(__lowerCAmelCase				)
				model.to(__lowerCAmelCase				)
				model.eval()
				lowerCamelCase__														=						model(__lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    labels=__lowerCAmelCase				)
				self.assertEqual(result.logits.shape			,    (self.model_tester.batch_size, self.model_tester.num_labels)				)





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__							,      lowerCamelCase__														=						self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase__														=						3
				lowerCamelCase__														=						'''multi_label_classification'''
				lowerCamelCase__														=						input_dict['''input_ids''']
				lowerCamelCase__														=						input_ids.ne(1				).to(__lowerCAmelCase				)
				lowerCamelCase__														=						ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]			,    self.model_tester.type_sequence_label_size				).to(torch.float				)
				lowerCamelCase__														=						LlamaForSequenceClassification(__lowerCAmelCase				)
				model.to(__lowerCAmelCase				)
				model.eval()
				lowerCamelCase__														=						model(__lowerCAmelCase			,    attention_mask=__lowerCAmelCase			,    labels=__lowerCAmelCase				)
				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 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				pass





			@parameterized.expand([('''linear''',), ('''dynamic''',)]				)
			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase				):



				'''simple docstring'''







				lowerCamelCase__							,      lowerCamelCase__														=						self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase__														=						ids_tensor([1, 1_0]			,    config.vocab_size				)
				lowerCamelCase__														=						ids_tensor([1, int(config.max_position_embeddings * 1.5				)]			,    config.vocab_size				)

				set_seed(4_2				)  # Fixed seed at init time so the two models get the same random weights
				lowerCamelCase__														=						LlamaModel(__lowerCAmelCase				)
				original_model.to(__lowerCAmelCase				)
				original_model.eval()
				lowerCamelCase__														=						original_model(__lowerCAmelCase				).last_hidden_state
				lowerCamelCase__														=						original_model(__lowerCAmelCase				).last_hidden_state

				set_seed(4_2				)  # Fixed seed at init time so the two models get the same random weights
				lowerCamelCase__														=						{'''type''': scaling_type, '''factor''': 10.0}
				lowerCamelCase__														=						LlamaModel(__lowerCAmelCase				)
				scaled_model.to(__lowerCAmelCase				)
				scaled_model.eval()
				lowerCamelCase__														=						scaled_model(__lowerCAmelCase				).last_hidden_state
				lowerCamelCase__														=						scaled_model(__lowerCAmelCase				).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(__lowerCAmelCase			,    __lowerCAmelCase			,    atol=1E-5				)				)
				else:
					self.assertFalse(torch.allclose(__lowerCAmelCase			,    __lowerCAmelCase			,    atol=1E-5				)				)

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






@require_torch
class    __A       (       unittest.TestCase					):

			'''simple docstring'''







			@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''				)
			@slow
			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				lowerCamelCase__														=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''			,    device_map='''auto'''				)
				lowerCamelCase__														=						model(torch.tensor([input_ids]				)				)
				# Expected mean on dim = -1
				lowerCamelCase__														=						torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]				)
				torch.testing.assert_close(out.mean(-1				)			,    __lowerCAmelCase			,    atol=1E-2			,    rtol=1E-2				)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				lowerCamelCase__														=						torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]				)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :3_0]			,    __lowerCAmelCase			,    atol=1E-5			,    rtol=1E-5				)





			@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''				)
			@slow
			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				lowerCamelCase__														=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''			,    device_map='''auto'''				)
				lowerCamelCase__														=						model(torch.tensor(__lowerCAmelCase				)				)
				# Expected mean on dim = -1
				lowerCamelCase__														=						torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]				)
				torch.testing.assert_close(out.mean(-1				)			,    __lowerCAmelCase			,    atol=1E-2			,    rtol=1E-2				)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				lowerCamelCase__														=						torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]				)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :3_0]			,    __lowerCAmelCase			,    atol=1E-5			,    rtol=1E-5				)





			@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''				)
			@slow
			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				lowerCamelCase__														=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''			,    device_map='''auto'''				)
				lowerCamelCase__														=						model(torch.tensor(__lowerCAmelCase				)				)
				# Expected mean on dim = -1
				lowerCamelCase__														=						torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]				)
				torch.testing.assert_close(out.mean(-1				)			,    __lowerCAmelCase			,    atol=1E-2			,    rtol=1E-2				)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				lowerCamelCase__														=						torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]				)
				# fmt: on
				torch.testing.assert_close(out.mean(-1				)			,    __lowerCAmelCase			,    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 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				lowerCamelCase__														=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''			,    device_map='''auto'''				)
				lowerCamelCase__														=						model(torch.tensor(__lowerCAmelCase				)				)

				lowerCamelCase__														=						torch.tensor(
				    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]			,    dtype=torch.floataa				)
				torch.testing.assert_close(out.mean(-1				)			,    __lowerCAmelCase			,    atol=1E-2			,    rtol=1E-2				)
				# fmt: off
				lowerCamelCase__														=						torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]				)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :3_0]			,    __lowerCAmelCase			,    atol=1E-5			,    rtol=1E-5				)





			@unittest.skip('''Model is curently gated'''				)
			@slow
			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						'''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__														=						'''Simply put, the theory of relativity states that '''
				lowerCamelCase__														=						LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''				)
				lowerCamelCase__														=						tokenizer.encode(__lowerCAmelCase			,    return_tensors='''pt'''				)
				lowerCamelCase__														=						LlamaForCausalLM.from_pretrained(
				    '''meta-llama/Llama-2-13b-chat-hf'''			,    device_map='''sequential'''			,    use_safetensors=__lowerCAmelCase				)

				# greedy generation outputs
				lowerCamelCase__														=						model.generate(__lowerCAmelCase			,    max_new_tokens=6_4			,    top_p=__lowerCAmelCase			,    temperature=1			,    do_sample=__lowerCAmelCase				)
				lowerCamelCase__														=						tokenizer.decode(generated_ids[0]			,    skip_special_tokens=__lowerCAmelCase				)
				self.assertEqual(__lowerCAmelCase			,    __lowerCAmelCase				)











def 		__UpperCAmelCase     (	a_ = 10**9):
							snake_case_           =				1
							snake_case_           =				2

							snake_case_           =				0
							snake_case_           =				0
							snake_case_           =				0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														snake_case_           =				2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
	print(f'{solution() = }')





