



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		int      							=     ["input_features"]



					def __init__(  self       :							Dict					,       _snake_case       :							str=80					,       _snake_case       :							List[Any]=1_60_00					,       _snake_case       :							str=1_60					,       _snake_case       :							Any=30					,       _snake_case       :							Dict=4_00					,       _snake_case       :							int=0.0					,       _snake_case       :							Tuple=False					,       **_snake_case       :							Optional[Any]					,       ):






								"""simple docstring"""


								super().__init__(
								    feature_size=_snake_case					,       sampling_rate=_snake_case					,       padding_value=_snake_case					,       return_attention_mask=_snake_case					,       **_snake_case					,       )
								A__	  =     n_fft
								A__	  =     hop_length
								A__	  =     chunk_length
								A__	  =     chunk_length * sampling_rate
								A__	  =     self.n_samples // hop_length
								A__	  =     sampling_rate
								A__	  =     mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2					,       num_mel_filters=_snake_case					,       min_frequency=0.0					,       max_frequency=8000.0					,       sampling_rate=_snake_case					,       norm='slaney'					,       mel_scale='slaney'					,       )



					def      _a				(  self       :							List[str]					,       _snake_case       :							np.array       ):






								"""simple docstring"""


								A__	  =     spectrogram(
								    _snake_case					,       window_function(self.n_fft					,       'hann'       )					,       frame_length=self.n_fft					,       hop_length=self.hop_length					,       power=2.0					,       mel_filters=self.mel_filters					,       log_mel='log10'					,       )
								A__	  =     log_spec[:, :-1]
								A__	  =     np.maximum(_snake_case					,       log_spec.max() - 8.0       )
								A__	  =     (log_spec + 4.0) / 4.0
								return log_spec



					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def      _a				(  _snake_case       :							List[np.ndarray]					,       _snake_case       :							List[np.ndarray]					,       _snake_case       :							float = 0.0       ):






								"""simple docstring"""


								if attention_mask is not None:
											A__	  =     np.array(_snake_case					,       np.intaa       )
											A__	  =     []

											for vector, length in zip(_snake_case					,       attention_mask.sum(-1       )       ):
														A__	  =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
														if length < normed_slice.shape[0]:
																	A__	  =     padding_value

														normed_input_values.append(_snake_case       )
								else:
											A__	  =     [(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

								return normed_input_values



					def __call__(  self       :							str					,       _snake_case       :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,       _snake_case       :							bool = True					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[str] = "max_length"					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       **_snake_case       :							Optional[int]					,       ):






								"""simple docstring"""


								if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
														raise ValueError(
														    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
														    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
														    F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''       )
								else:
											logger.warning(
											    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
											    'Failing to do so can result in silent errors that might be hard to debug.'       )

								A__	  =     isinstance(_snake_case					,       np.ndarray       ) and len(raw_speech.shape       ) > 1
								if is_batched_numpy and len(raw_speech.shape       ) > 2:
											raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''       )
								A__	  =     is_batched_numpy or (
								    isinstance(_snake_case					,       (list, tuple)       ) and (isinstance(raw_speech[0]					,       (np.ndarray, tuple, list)       ))
								)

								if is_batched:
											A__	  =     [np.asarray([speech]					,       dtype=np.floataa       ).T for speech in raw_speech]
								elif not is_batched and not isinstance(_snake_case					,       np.ndarray       ):
											A__	  =     np.asarray(_snake_case					,       dtype=np.floataa       )
								elif isinstance(_snake_case					,       np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
											A__	  =     raw_speech.astype(np.floataa       )

								# always return batch
								if not is_batched:
											A__	  =     [np.asarray([raw_speech]       ).T]

								A__	  =     BatchFeature({'input_features': raw_speech}       )

								# convert into correct format for padding

								A__	  =     self.pad(
								    _snake_case					,       padding=_snake_case					,       max_length=max_length if max_length else self.n_samples					,       truncation=_snake_case					,       pad_to_multiple_of=_snake_case					,       return_attention_mask=return_attention_mask or do_normalize					,       )

								# zero-mean and unit-variance normalization
								if do_normalize:
											A__	  =     self.zero_mean_unit_var_norm(
											    padded_inputs['input_features']					,       attention_mask=padded_inputs['attention_mask']					,       padding_value=self.padding_value					,       )
											A__	  =     np.stack(padded_inputs['input_features']					,       axis=0       )

								# make sure list is in array format
								A__	  =     padded_inputs.get('input_features'       ).transpose(2					,       0					,       1       )

								A__	  =     [self._np_extract_fbank_features(_snake_case       ) for waveform in input_features[0]]

								if isinstance(input_features[0]					,       _snake_case       ):
											A__	  =     [np.asarray(_snake_case					,       dtype=np.floataa       ) for feature in input_features]
								else:
											A__	  =     input_features

								if return_attention_mask:
											# rescale from sample (48000) to feature (3000)
											A__	  =     padded_inputs['attention_mask'][:, :: self.hop_length]

								if return_tensors is not None:
											A__	  =     padded_inputs.convert_to_tensors(_snake_case       )

								return padded_inputs



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     copy.deepcopy(self.__dict__       )
								A__	  =     self.__class__.__name__
								if "mel_filters" in output:
											del output["mel_filters"]
								return output






"""simple docstring"""


import unittest

from parameterized import parameterized

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

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


if is_torch_available():
		import torch

		from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class 					lowerCAmelCase_					:



						'''simple docstring'''


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size   )

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

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

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

							A     = self.get_config()

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> 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     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Dict	,A_  :   Optional[int]	,A_  :   Any	,A_  :   Optional[Any]	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = LlamaModel(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_   )
							A     = model(A_   )
							self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   int	,A_  :   Optional[Any]	,A_  :   Optional[int]	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Any	,A_  :   Tuple	,A_  :   Union[str, Any]	,A_  :   Dict	,)  -> List[str]:
							A     = True
							A     = LlamaModel(A_   )
							model.to(A_   )
							model.eval()
							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,)
							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,)
							A     = model(A_	,attention_mask=A_   )
							self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   List[str]	,A_  :   Optional[int]	,A_  :   Any	,A_  :   str	,A_  :   Dict	,A_  :   Dict	,A_  :   Tuple	,A_  :   Tuple	,A_  :   Dict	,)  -> Union[str, Any]:
							A     = LlamaForCausalLM(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Dict	,A_  :   Any	,A_  :   int	,A_  :   List[str]	,A_  :   Tuple	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Any	,A_  :   int	,)  -> List[Any]:
							A     = True
							A     = True
							A     = LlamaForCausalLM(config=A_   )
							model.to(A_   )
							model.eval()

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

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

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

							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,output_hidden_states=A_	,)['hidden_states'][0]
							A     = 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
							A     = ids_tensor((1,)	,output_from_past.shape[-1]   ).item()
							A     = output_from_no_past[:, -3:, random_slice_idx].detach()
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Optional[int]:
							A     = self.prepare_config_and_inputs()
							(
							    (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   (
							    A
							) ,   
							)     = config_and_inputs
							A     = {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Dict   =     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						_lowerCamelCase:     List[Any]   =     (LlamaForCausalLM,) if is_torch_available() else ()
						_lowerCamelCase:     Any   =     (
						    {
						        '''feature-extraction''': LlamaModel,
						        '''text-classification''': LlamaForSequenceClassification,
						        '''text-generation''': LlamaForCausalLM,
						        '''zero-shot''': LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCamelCase:     int   =     False
						_lowerCamelCase:     List[str]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Dict:
							A     = LlamaModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,hidden_size=37   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Any:
							self.config_tester.run_common_tests()


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Tuple:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Optional[int]:
							A     = self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								A     = type
								self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = 3
							A     = input_dict['input_ids']
							A     = input_ids.ne(1   ).to(A_   )
							A     = ids_tensor([self.model_tester.batch_size]	,self.model_tester.type_sequence_label_size   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = 3
							A     = 'single_label_classification'
							A     = input_dict['input_ids']
							A     = input_ids.ne(1   ).to(A_   )
							A     = ids_tensor([self.model_tester.batch_size]	,self.model_tester.type_sequence_label_size   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Dict:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = 3
							A     = 'multi_label_classification'
							A     = input_dict['input_ids']
							A     = input_ids.ne(1   ).to(A_   )
							A     = ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]	,self.model_tester.type_sequence_label_size   ).to(torch.float   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							pass




						@parameterized.expand([('linear',), ('dynamic',)]   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Any   )  -> str:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = ids_tensor([1, 10]	,config.vocab_size   )
							A     = 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
							A     = LlamaModel(A_   )
							original_model.to(A_   )
							original_model.eval()
							A     = original_model(A_   ).last_hidden_state
							A     = original_model(A_   ).last_hidden_state

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



						'''simple docstring'''


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

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




						@unittest.skip('Model is curently gated'   )
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							A     = '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'
							A     = 'Simply put, the theory of relativity states that '
							A     = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   )
							A     = tokenizer.encode(A_	,return_tensors='pt'   )
							A     = LlamaForCausalLM.from_pretrained(
							    'meta-llama/Llama-2-13b-chat-hf'	,device_map='sequential'	,use_safetensors=A_   )

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