
'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase					:							str   =      logging.get_logger(__name__)

lowercase					:							str   =      {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}


class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= """mgp-str"""


							def __init__( self					,     lowerCAmelCase_=[32, 1_28]					,     lowerCAmelCase_=4					,     lowerCAmelCase_=3					,     lowerCAmelCase_=27					,     lowerCAmelCase_=38					,     lowerCAmelCase_=5_02_57					,     lowerCAmelCase_=3_05_22					,     lowerCAmelCase_=7_68					,     lowerCAmelCase_=12					,     lowerCAmelCase_=12					,     lowerCAmelCase_=4.0					,     lowerCAmelCase_=True					,     lowerCAmelCase_=False					,     lowerCAmelCase_=1E-5					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=False					,     lowerCAmelCase_=0.02					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_						)

								_snake_case     		=	image_size
								_snake_case     		=	patch_size
								_snake_case     		=	num_channels
								_snake_case     		=	max_token_length
								_snake_case     		=	num_character_labels
								_snake_case     		=	num_bpe_labels
								_snake_case     		=	num_wordpiece_labels
								_snake_case     		=	hidden_size
								_snake_case     		=	num_hidden_layers
								_snake_case     		=	num_attention_heads
								_snake_case     		=	mlp_ratio
								_snake_case     		=	distilled
								_snake_case     		=	layer_norm_eps
								_snake_case     		=	drop_rate
								_snake_case     		=	qkv_bias
								_snake_case     		=	attn_drop_rate
								_snake_case     		=	drop_path_rate
								_snake_case     		=	output_aa_attentions
								_snake_case     		=	initializer_range



'''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 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=13					,     lowerCAmelCase_=7					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=False					,     lowerCAmelCase_=True					,     lowerCAmelCase_=99					,     lowerCAmelCase_=32					,     lowerCAmelCase_=5					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=5_12					,     lowerCAmelCase_=16					,     lowerCAmelCase_=2					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

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

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

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

								_snake_case     		=	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"""






								_snake_case     		=	LlamaModel(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	model(lowerCAmelCase_					,     attention_mask=lowerCAmelCase_						)
								_snake_case     		=	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"""






								_snake_case     		=	True
								_snake_case     		=	LlamaModel(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	model(
								    lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     encoder_hidden_states=lowerCAmelCase_					,     encoder_attention_mask=lowerCAmelCase_					,     )
								_snake_case     		=	model(
								    lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     encoder_hidden_states=lowerCAmelCase_					,     )
								_snake_case     		=	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"""






								_snake_case     		=	LlamaForCausalLM(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	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"""






								_snake_case     		=	True
								_snake_case     		=	True
								_snake_case     		=	LlamaForCausalLM(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

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

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

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

								_snake_case     		=	model(
								    lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     encoder_hidden_states=lowerCAmelCase_					,     encoder_attention_mask=lowerCAmelCase_					,     output_hidden_states=lowerCAmelCase_					,     )['hidden_states'][0]
								_snake_case     		=	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
								_snake_case     		=	ids_tensor((1,)					,     output_from_past.shape[-1]						).item()
								_snake_case     		=	output_from_no_past[:, -3:, random_slice_idx].detach()
								_snake_case     		=	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"""






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


@require_torch
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
							__lowercase   	= (LlamaForCausalLM,) if is_torch_available() else ()
							__lowercase   	= (
							    {
							        """feature-extraction""": LlamaModel,
							        """text-classification""": LlamaForSequenceClassification,
							        """text-generation""": LlamaForCausalLM,
							        """zero-shot""": LlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__lowercase   	= False
							__lowercase   	= False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	LlamaModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     hidden_size=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case     		=	3
								_snake_case     		=	input_dict['input_ids']
								_snake_case     		=	input_ids.ne(1						).to(lowerCAmelCase_						)
								_snake_case     		=	ids_tensor([self.model_tester.batch_size]					,     self.model_tester.type_sequence_label_size						)
								_snake_case     		=	LlamaForSequenceClassification(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	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"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case     		=	3
								_snake_case     		=	'single_label_classification'
								_snake_case     		=	input_dict['input_ids']
								_snake_case     		=	input_ids.ne(1						).to(lowerCAmelCase_						)
								_snake_case     		=	ids_tensor([self.model_tester.batch_size]					,     self.model_tester.type_sequence_label_size						)
								_snake_case     		=	LlamaForSequenceClassification(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	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"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case     		=	3
								_snake_case     		=	'multi_label_classification'
								_snake_case     		=	input_dict['input_ids']
								_snake_case     		=	input_ids.ne(1						).to(lowerCAmelCase_						)
								_snake_case     		=	ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]					,     self.model_tester.type_sequence_label_size						).to(torch.float						)
								_snake_case     		=	LlamaForSequenceClassification(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	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"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case     		=	ids_tensor([1, 10]					,     config.vocab_size						)
								_snake_case     		=	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
								_snake_case     		=	LlamaModel(lowerCAmelCase_						)
								original_model.to(lowerCAmelCase_						)
								original_model.eval()
								_snake_case     		=	original_model(lowerCAmelCase_						).last_hidden_state
								_snake_case     		=	original_model(lowerCAmelCase_						).last_hidden_state

								set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
								_snake_case     		=	{'type': scaling_type, 'factor': 10.0}
								_snake_case     		=	LlamaModel(lowerCAmelCase_						)
								scaled_model.to(lowerCAmelCase_						)
								scaled_model.eval()
								_snake_case     		=	scaled_model(lowerCAmelCase_						).last_hidden_state
								_snake_case     		=	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 		__UpperCAmelCase   (      unittest.TestCase		):


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





								"""simple docstring"""






								_snake_case     		=	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
								_snake_case     		=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'					,     device_map='auto'						)
								_snake_case     		=	model(torch.tensor([input_ids]						)						)
								# Expected mean on dim = -1
								_snake_case     		=	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
								_snake_case     		=	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, :30]					,     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"""






								_snake_case     		=	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
								_snake_case     		=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'					,     device_map='auto'						)
								_snake_case     		=	model(torch.tensor(lowerCAmelCase_						)						)
								# Expected mean on dim = -1
								_snake_case     		=	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
								_snake_case     		=	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, :30]					,     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"""






								_snake_case     		=	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
								_snake_case     		=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'					,     device_map='auto'						)
								_snake_case     		=	model(torch.tensor(lowerCAmelCase_						)						)
								# Expected mean on dim = -1
								_snake_case     		=	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
								_snake_case     		=	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"""






								_snake_case     		=	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
								_snake_case     		=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'					,     device_map='auto'						)
								_snake_case     		=	model(torch.tensor(lowerCAmelCase_						)						)

								_snake_case     		=	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
								_snake_case     		=	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, :30]					,     lowerCAmelCase_					,     atol=1E-5					,     rtol=1E-5						)




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





								"""simple docstring"""






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

								# greedy generation outputs
								_snake_case     		=	model.generate(lowerCAmelCase_					,     max_new_tokens=64					,     top_p=lowerCAmelCase_					,     temperature=1					,     do_sample=lowerCAmelCase_						)
								_snake_case     		=	tokenizer.decode(generated_ids[0]					,     skip_special_tokens=lowerCAmelCase_						)
								self.assertEqual(lowerCAmelCase_					,     lowerCAmelCase_						)

