



'''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 _A     :





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=13       ,				__UpperCAmelCase=7       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=False       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=99       ,				__UpperCAmelCase=32       ,				__UpperCAmelCase=5       ,				__UpperCAmelCase=4       ,				__UpperCAmelCase=37       ,				__UpperCAmelCase="gelu"       ,				__UpperCAmelCase=0.1       ,				__UpperCAmelCase=0.1       ,				__UpperCAmelCase=512       ,				__UpperCAmelCase=16       ,				__UpperCAmelCase=2       ,				__UpperCAmelCase=0.02       ,				__UpperCAmelCase=3       ,				__UpperCAmelCase=4       ,				__UpperCAmelCase=None       ,				)				->   Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[int]   =   parent
					__UpperCAmelCase      :					List[Any]   =   batch_size
					__UpperCAmelCase      :					Tuple   =   seq_length
					__UpperCAmelCase      :					Any   =   is_training
					__UpperCAmelCase      :					Dict   =   use_input_mask
					__UpperCAmelCase      :					Any   =   use_token_type_ids
					__UpperCAmelCase      :					str   =   use_labels
					__UpperCAmelCase      :					Optional[int]   =   vocab_size
					__UpperCAmelCase      :					List[str]   =   hidden_size
					__UpperCAmelCase      :					List[str]   =   num_hidden_layers
					__UpperCAmelCase      :					Optional[int]   =   num_attention_heads
					__UpperCAmelCase      :					str   =   intermediate_size
					__UpperCAmelCase      :					Any   =   hidden_act
					__UpperCAmelCase      :					List[str]   =   hidden_dropout_prob
					__UpperCAmelCase      :					List[str]   =   attention_probs_dropout_prob
					__UpperCAmelCase      :					str   =   max_position_embeddings
					__UpperCAmelCase      :					Tuple   =   type_vocab_size
					__UpperCAmelCase      :					Optional[int]   =   type_sequence_label_size
					__UpperCAmelCase      :					Tuple   =   initializer_range
					__UpperCAmelCase      :					Optional[Any]   =   num_labels
					__UpperCAmelCase      :					Optional[int]   =   num_choices
					__UpperCAmelCase      :					Optional[int]   =   scope





		def 							__A				(   self )				->   List[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					Tuple   =   ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size )

					__UpperCAmelCase      :					List[Any]   =   None
					if self.use_input_mask:
								__UpperCAmelCase      :					List[Any]   =   random_attention_mask([self.batch_size, self.seq_length] )

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

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

					__UpperCAmelCase      :					str   =   self.get_config()

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





		def 							__A				(   self )				->   Optional[int]:

					'''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=UpperCAmelCase__       ,				initializer_range=self.initializer_range       ,				)





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					Any   =   LlamaModel(config=UpperCAmelCase__ )
					model.to(UpperCAmelCase__ )
					model.eval()
					__UpperCAmelCase      :					Optional[Any]   =   model(UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__ )
					__UpperCAmelCase      :					Union[str, Any]   =   model(UpperCAmelCase__ )
					self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size) )





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				)				->   int:

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   True
					__UpperCAmelCase      :					str   =   LlamaModel(UpperCAmelCase__ )
					model.to(UpperCAmelCase__ )
					model.eval()
					__UpperCAmelCase      :					Any   =   model(
					    UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				encoder_hidden_states=UpperCAmelCase__       ,				encoder_attention_mask=UpperCAmelCase__       ,				)
					__UpperCAmelCase      :					Optional[int]   =   model(
					    UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				encoder_hidden_states=UpperCAmelCase__       ,				)
					__UpperCAmelCase      :					List[Any]   =   model(UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__ )
					self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size) )





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				)				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   LlamaForCausalLM(config=UpperCAmelCase__ )
					model.to(UpperCAmelCase__ )
					model.eval()
					__UpperCAmelCase      :					Union[str, Any]   =   model(UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				labels=UpperCAmelCase__ )
					self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size) )





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				)				->   List[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   True
					__UpperCAmelCase      :					Any   =   True
					__UpperCAmelCase      :					List[str]   =   LlamaForCausalLM(config=UpperCAmelCase__ )
					model.to(UpperCAmelCase__ )
					model.eval()

					# first forward pass
					__UpperCAmelCase      :					List[Any]   =   model(
					    UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				encoder_hidden_states=UpperCAmelCase__       ,				encoder_attention_mask=UpperCAmelCase__       ,				use_cache=UpperCAmelCase__       ,				)
					__UpperCAmelCase      :					Optional[Any]   =   outputs.past_key_values

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

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

					__UpperCAmelCase      :					Dict   =   model(
					    UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				encoder_hidden_states=UpperCAmelCase__       ,				encoder_attention_mask=UpperCAmelCase__       ,				output_hidden_states=UpperCAmelCase__       ,				)["""hidden_states"""][0]
					__UpperCAmelCase      :					List[Any]   =   model(
					    UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				encoder_hidden_states=UpperCAmelCase__       ,				encoder_attention_mask=UpperCAmelCase__       ,				past_key_values=UpperCAmelCase__       ,				output_hidden_states=UpperCAmelCase__       ,				)["""hidden_states"""][0]

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





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					__UpperCAmelCase      :					Tuple   =   self.prepare_config_and_inputs()
					(
					    (
					    __UpperCAmelCase
					) ,     (
					    __UpperCAmelCase
					) ,     (
					    __UpperCAmelCase
					) ,     (
					    __UpperCAmelCase
					) ,     (
					    __UpperCAmelCase
					) ,     (
					    __UpperCAmelCase
					) ,     (
					    __UpperCAmelCase
					) ,     
					)      :					Optional[Any]   =   config_and_inputs
					__UpperCAmelCase      :					Optional[int]   =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
					return config, inputs_dict



@require_torch
class _A     (			__SCREAMING_SNAKE_CASE		, __SCREAMING_SNAKE_CASE		, __SCREAMING_SNAKE_CASE		, unittest.TestCase			):
		_SCREAMING_SNAKE_CASE    :			Optional[Any]  					=	(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
		_SCREAMING_SNAKE_CASE    :			str  					=	(LlamaForCausalLM,) if is_torch_available() else ()
		_SCREAMING_SNAKE_CASE    :			Optional[Any]  					=	(
		    {
		        "feature-extraction": LlamaModel,
		        "text-classification": LlamaForSequenceClassification,
		        "text-generation": LlamaForCausalLM,
		        "zero-shot": LlamaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_SCREAMING_SNAKE_CASE    :			Union[str, Any]  					=	False
		_SCREAMING_SNAKE_CASE    :			List[Any]  					=	False





		def 							__A				(   self )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					Any   =   LlamaModelTester(self )
					__UpperCAmelCase      :					str   =   ConfigTester(self       ,				config_class=UpperCAmelCase__       ,				hidden_size=37 )





		def 							__A				(   self )				->   List[Any]:

					'''simple docstring'''
					self.config_tester.run_common_tests()





		def 							__A				(   self )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[Any]   =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCAmelCase__ )





		def 							__A				(   self )				->   List[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[Any]   =   self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
								__UpperCAmelCase      :					List[str]   =   type
								self.model_tester.create_and_check_model(*UpperCAmelCase__ )





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					__UpperCAmelCase ,     __UpperCAmelCase      :					Optional[Any]   =   self.model_tester.prepare_config_and_inputs_for_common()
					__UpperCAmelCase      :					str   =   3
					__UpperCAmelCase      :					Optional[int]   =   input_dict["""input_ids"""]
					__UpperCAmelCase      :					str   =   input_ids.ne(1 ).to(UpperCAmelCase__ )
					__UpperCAmelCase      :					Union[str, Any]   =   ids_tensor([self.model_tester.batch_size]       ,				self.model_tester.type_sequence_label_size )
					__UpperCAmelCase      :					int   =   LlamaForSequenceClassification(UpperCAmelCase__ )
					model.to(UpperCAmelCase__ )
					model.eval()
					__UpperCAmelCase      :					Union[str, Any]   =   model(UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				labels=UpperCAmelCase__ )
					self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels) )





		def 							__A				(   self )				->   str:

					'''simple docstring'''
					__UpperCAmelCase ,     __UpperCAmelCase      :					Union[str, Any]   =   self.model_tester.prepare_config_and_inputs_for_common()
					__UpperCAmelCase      :					Tuple   =   3
					__UpperCAmelCase      :					Optional[int]   =   """single_label_classification"""
					__UpperCAmelCase      :					Optional[int]   =   input_dict["""input_ids"""]
					__UpperCAmelCase      :					List[str]   =   input_ids.ne(1 ).to(UpperCAmelCase__ )
					__UpperCAmelCase      :					List[str]   =   ids_tensor([self.model_tester.batch_size]       ,				self.model_tester.type_sequence_label_size )
					__UpperCAmelCase      :					List[str]   =   LlamaForSequenceClassification(UpperCAmelCase__ )
					model.to(UpperCAmelCase__ )
					model.eval()
					__UpperCAmelCase      :					Tuple   =   model(UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				labels=UpperCAmelCase__ )
					self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels) )





		def 							__A				(   self )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase ,     __UpperCAmelCase      :					int   =   self.model_tester.prepare_config_and_inputs_for_common()
					__UpperCAmelCase      :					Union[str, Any]   =   3
					__UpperCAmelCase      :					Union[str, Any]   =   """multi_label_classification"""
					__UpperCAmelCase      :					Dict   =   input_dict["""input_ids"""]
					__UpperCAmelCase      :					Tuple   =   input_ids.ne(1 ).to(UpperCAmelCase__ )
					__UpperCAmelCase      :					Tuple   =   ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]       ,				self.model_tester.type_sequence_label_size ).to(torch.float )
					__UpperCAmelCase      :					str   =   LlamaForSequenceClassification(UpperCAmelCase__ )
					model.to(UpperCAmelCase__ )
					model.eval()
					__UpperCAmelCase      :					int   =   model(UpperCAmelCase__       ,				attention_mask=UpperCAmelCase__       ,				labels=UpperCAmelCase__ )
					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 )				->   str:

					'''simple docstring'''
					pass





		@parameterized.expand([("""linear""",), ("""dynamic""",)] )
		def 							__A				(   self       ,				__UpperCAmelCase )				->   Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase ,     __UpperCAmelCase      :					Optional[int]   =   self.model_tester.prepare_config_and_inputs_for_common()
					__UpperCAmelCase      :					Dict   =   ids_tensor([1, 10]       ,				config.vocab_size )
					__UpperCAmelCase      :					List[Any]   =   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
					__UpperCAmelCase      :					Optional[Any]   =   LlamaModel(UpperCAmelCase__ )
					original_model.to(UpperCAmelCase__ )
					original_model.eval()
					__UpperCAmelCase      :					Dict   =   original_model(UpperCAmelCase__ ).last_hidden_state
					__UpperCAmelCase      :					List[str]   =   original_model(UpperCAmelCase__ ).last_hidden_state

					set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
					__UpperCAmelCase      :					Tuple   =   {"""type""": scaling_type, """factor""": 10.0}
					__UpperCAmelCase      :					Dict   =   LlamaModel(UpperCAmelCase__ )
					scaled_model.to(UpperCAmelCase__ )
					scaled_model.eval()
					__UpperCAmelCase      :					str   =   scaled_model(UpperCAmelCase__ ).last_hidden_state
					__UpperCAmelCase      :					Optional[Any]   =   scaled_model(UpperCAmelCase__ ).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(UpperCAmelCase__       ,				UpperCAmelCase__       ,				atol=1E-5 ) )
					else:
								self.assertFalse(torch.allclose(UpperCAmelCase__       ,				UpperCAmelCase__       ,				atol=1E-5 ) )

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



@require_torch
class _A     (			unittest.TestCase			):





		@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
		@slow
		def 							__A				(   self )				->   Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
					__UpperCAmelCase      :					str   =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""       ,				device_map="""auto""" )
					__UpperCAmelCase      :					List[Any]   =   model(torch.tensor([input_ids] ) )
					# Expected mean on dim = -1
					__UpperCAmelCase      :					List[Any]   =   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 )       ,				UpperCAmelCase__       ,				atol=1E-2       ,				rtol=1E-2 )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					__UpperCAmelCase      :					Any   =   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]       ,				UpperCAmelCase__       ,				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 )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					Dict   =   [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
					__UpperCAmelCase      :					int   =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""       ,				device_map="""auto""" )
					__UpperCAmelCase      :					Tuple   =   model(torch.tensor(UpperCAmelCase__ ) )
					# Expected mean on dim = -1
					__UpperCAmelCase      :					List[str]   =   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 )       ,				UpperCAmelCase__       ,				atol=1E-2       ,				rtol=1E-2 )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					__UpperCAmelCase      :					str   =   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]       ,				UpperCAmelCase__       ,				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 )				->   List[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
					__UpperCAmelCase      :					Dict   =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""       ,				device_map="""auto""" )
					__UpperCAmelCase      :					Any   =   model(torch.tensor(UpperCAmelCase__ ) )
					# Expected mean on dim = -1
					__UpperCAmelCase      :					Union[str, Any]   =   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 )       ,				UpperCAmelCase__       ,				atol=1E-2       ,				rtol=1E-2 )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					__UpperCAmelCase      :					int   =   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 )       ,				UpperCAmelCase__       ,				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 )				->   Any:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
					__UpperCAmelCase      :					int   =   LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""       ,				device_map="""auto""" )
					__UpperCAmelCase      :					str   =   model(torch.tensor(UpperCAmelCase__ ) )

					__UpperCAmelCase      :					Optional[Any]   =   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 )       ,				UpperCAmelCase__       ,				atol=1E-2       ,				rtol=1E-2 )
					# fmt: off
					__UpperCAmelCase      :					Tuple   =   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]       ,				UpperCAmelCase__       ,				atol=1E-5       ,				rtol=1E-5 )





		@unittest.skip("""Model is curently gated""" )
		@slow
		def 							__A				(   self )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[int]   =   """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"""
					__UpperCAmelCase      :					Union[str, Any]   =   """Simply put, the theory of relativity states that """
					__UpperCAmelCase      :					int   =   LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" )
					__UpperCAmelCase      :					int   =   tokenizer.encode(UpperCAmelCase__       ,				return_tensors="""pt""" )
					__UpperCAmelCase      :					List[str]   =   LlamaForCausalLM.from_pretrained(
					    """meta-llama/Llama-2-13b-chat-hf"""       ,				device_map="""sequential"""       ,				use_safetensors=UpperCAmelCase__ )

					# greedy generation outputs
					__UpperCAmelCase      :					Union[str, Any]   =   model.generate(UpperCAmelCase__       ,				max_new_tokens=64       ,				top_p=UpperCAmelCase__       ,				temperature=1       ,				do_sample=UpperCAmelCase__ )
					__UpperCAmelCase      :					List[Any]   =   tokenizer.decode(generated_ids[0]       ,				skip_special_tokens=UpperCAmelCase__ )
					self.assertEqual(UpperCAmelCase__       ,				UpperCAmelCase__ )







"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 					_lowerCAmelCase  (	unittest.TestCase     ,lowercase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						load_tool("text-classification"	)
    self.tool.setup()
    __lowercase								=						load_tool("text-classification",					remote=UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.remote_tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.remote_tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)




