






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_ :



	'''simple docstring'''




	def __init__(			self			,     a			,     a=13			,     a=7			,     a=True			,     a=True			,     a=False			,     a=True			,     a=99			,     a=32			,     a=5			,     a=4			,     a=37			,     a="gelu"			,     a=0.1			,     a=0.1			,     a=5_12			,     a=16			,     a=2			,     a=0.02			,     a=3			,     a=4			,     a=None			,     )			->       Any:
								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 				_UpperCamelCase     (			self   )			->       List[str]:
								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 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								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=a			,     initializer_range=self.initializer_range			,     )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a			,     a			,     a   )			->       List[str]:
								snake_case_           =				LlamaModel(config=a   )
								model.to(a   )
								model.eval()
								snake_case_           =				model(a			,     attention_mask=a   )
								snake_case_           =				model(a   )
								self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     )			->       Any:
								snake_case_           =				True
								snake_case_           =				LlamaModel(a   )
								model.to(a   )
								model.eval()
								snake_case_           =				model(
								    a			,     attention_mask=a			,     encoder_hidden_states=a			,     encoder_attention_mask=a			,     )
								snake_case_           =				model(
								    a			,     attention_mask=a			,     encoder_hidden_states=a			,     )
								snake_case_           =				model(a			,     attention_mask=a   )
								self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     )			->       Dict:
								snake_case_           =				LlamaForCausalLM(config=a   )
								model.to(a   )
								model.eval()
								snake_case_           =				model(a			,     attention_mask=a			,     labels=a   )
								self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     )			->       str:
								snake_case_           =				True
								snake_case_           =				True
								snake_case_           =				LlamaForCausalLM(config=a   )
								model.to(a   )
								model.eval()

								# first forward pass
								snake_case_           =				model(
								    a			,     attention_mask=a			,     encoder_hidden_states=a			,     encoder_attention_mask=a			,     use_cache=a			,     )
								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(
								    a			,     attention_mask=a			,     encoder_hidden_states=a			,     encoder_attention_mask=a			,     output_hidden_states=a			,     )['hidden_states'][0]
								snake_case_           =				model(
								    a			,     attention_mask=a			,     encoder_hidden_states=a			,     encoder_attention_mask=a			,     past_key_values=a			,     output_hidden_states=a			,     )['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(a			,     a			,     atol=1E-3   )   )






	def 				_UpperCamelCase     (			self   )			->       Dict:
								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_ (     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 				_UpperCamelCase     (			self   )			->       str:
								snake_case_           =				LlamaModelTester(self   )
								snake_case_           =				ConfigTester(self			,     config_class=a			,     hidden_size=37   )




	def 				_UpperCamelCase     (			self   )			->       Any:
								self.config_tester.run_common_tests()




	def 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a   )




	def 				_UpperCamelCase     (			self   )			->       Tuple:
								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(*a   )




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								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(a   )
								snake_case_           =				ids_tensor([self.model_tester.batch_size]			,     self.model_tester.type_sequence_label_size   )
								snake_case_           =				LlamaForSequenceClassification(a   )
								model.to(a   )
								model.eval()
								snake_case_           =				model(a			,     attention_mask=a			,     labels=a   )
								self.assertEqual(result.logits.shape			,     (self.model_tester.batch_size, self.model_tester.num_labels)   )




	def 				_UpperCamelCase     (			self   )			->       int:
								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(a   )
								snake_case_           =				ids_tensor([self.model_tester.batch_size]			,     self.model_tester.type_sequence_label_size   )
								snake_case_           =				LlamaForSequenceClassification(a   )
								model.to(a   )
								model.eval()
								snake_case_           =				model(a			,     attention_mask=a			,     labels=a   )
								self.assertEqual(result.logits.shape			,     (self.model_tester.batch_size, self.model_tester.num_labels)   )




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								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(a   )
								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(a   )
								model.to(a   )
								model.eval()
								snake_case_           =				model(a			,     attention_mask=a			,     labels=a   )
								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 				_UpperCamelCase     (			self   )			->       List[str]:
								pass






	@parameterized.expand([('linear',), ('dynamic',)]   )
	def 				_UpperCamelCase     (			self			,     a   )			->       Union[str, Any]:
								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(a   )
								original_model.to(a   )
								original_model.eval()
								snake_case_           =				original_model(a   ).last_hidden_state
								snake_case_           =				original_model(a   ).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(a   )
								scaled_model.to(a   )
								scaled_model.eval()
								snake_case_           =				scaled_model(a   ).last_hidden_state
								snake_case_           =				scaled_model(a   ).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(a			,     a			,     atol=1E-5   )   )
								else:
															self.assertFalse(torch.allclose(a			,     a			,     atol=1E-5   )   )

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






@require_torch
class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
	@slow
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								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([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]   )
								torch.testing.assert_close(out.mean(-1   )			,     a			,     atol=1E-2			,     rtol=1E-2   )
								# slicing logits[0, 0, 0:30]
								# fmt: off
								snake_case_           =				torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]   )
								# fmt: on
								torch.testing.assert_close(out[0, 0, :30]			,     a			,     atol=1E-5			,     rtol=1E-5   )




	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
	@slow
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								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(a   )   )
								# Expected mean on dim = -1
								snake_case_           =				torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]   )
								torch.testing.assert_close(out.mean(-1   )			,     a			,     atol=1E-2			,     rtol=1E-2   )
								# slicing logits[0, 0, 0:30]
								# fmt: off
								snake_case_           =				torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]   )
								# fmt: on
								torch.testing.assert_close(out[0, 0, :30]			,     a			,     atol=1E-5			,     rtol=1E-5   )




	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
	@slow
	def 				_UpperCamelCase     (			self   )			->       Tuple:
								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(a   )   )
								# Expected mean on dim = -1
								snake_case_           =				torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]   )
								torch.testing.assert_close(out.mean(-1   )			,     a			,     atol=1E-2			,     rtol=1E-2   )
								# slicing logits[0, 0, 0:30]
								# fmt: off
								snake_case_           =				torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]   )
								# fmt: on
								torch.testing.assert_close(out.mean(-1   )			,     a			,     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 				_UpperCamelCase     (			self   )			->       List[str]:
								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(a   )   )

								snake_case_           =				torch.tensor(
								    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]			,     dtype=torch.floataa   )
								torch.testing.assert_close(out.mean(-1   )			,     a			,     atol=1E-2			,     rtol=1E-2   )
								# fmt: off
								snake_case_           =				torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]   )
								# fmt: on
								torch.testing.assert_close(out[0, 0, :30]			,     a			,     atol=1E-5			,     rtol=1E-5   )






	@unittest.skip('Model is curently gated'   )
	@slow
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								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(a			,     return_tensors='pt'   )
								snake_case_           =				LlamaForCausalLM.from_pretrained(
								    'meta-llama/Llama-2-13b-chat-hf'			,     device_map='sequential'			,     use_safetensors=a   )

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













import unittest

import numpy as np





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = None				,	):
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)

							if shape_a[0] != shape_b[0]:
														snake_case_           =				(
														    'Expected the same number of rows for A and B. '
														    f'''Instead found A of size {shape_a} and B of size {shape_b}'''
														)
														raise ValueError(a_)

							if shape_b[1] != shape_c[1]:
														snake_case_           =				(
														    'Expected the same number of columns for B and C. '
														    f'''Instead found B of size {shape_b} and C of size {shape_c}'''
														)
														raise ValueError(a_)

							snake_case_           =				pseudo_inv
							if a_inv is None:
														try:
																					snake_case_           =				np.linalg.inv(a_)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    'Input matrix A is not invertible. Cannot compute Schur complement.')

							return mat_c - mat_b.T @ a_inv @ mat_b




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								snake_case_           =				schur_complement(a			,     a			,     a   )

								snake_case_           =				np.block([[a, b], [b.T, c]]   )

								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )

								self.assertAlmostEqual(a			,     det_a * det_s   )




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )






	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1, 3], [6, 3, 5]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	unittest.main()





