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

import numpy as np
import torch

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


_UpperCAmelCase :			Optional[int]              =	logging.get_logger(__name__)


class     lowercase (		lowerCAmelCase__       ):
		__lowercase	:	List[str]     					=    ["input_features", "is_longer"]



		def __init__(			self    ,				A_=64    ,				A_=48_000    ,				A_=480    ,				A_=10    ,				A_=1_024    ,				A_=0.0    ,				A_=False    ,				A_ = 0    ,				A_ = 14_000    ,				A_ = None    ,				A_ = "fusion"    ,				A_ = "repeatpad"    ,				**A_    ,				)  -> Any:
				"""simple docstring"""
				super().__init__(
				    feature_size=_SCREAMING_SNAKE_CASE    ,				sampling_rate=_SCREAMING_SNAKE_CASE    ,				padding_value=_SCREAMING_SNAKE_CASE    ,				return_attention_mask=_SCREAMING_SNAKE_CASE    ,				**_SCREAMING_SNAKE_CASE    ,				)
				UpperCamelCase          =					top_db
				UpperCamelCase          =					truncation
				UpperCamelCase          =					padding
				UpperCamelCase          =					fft_window_size
				UpperCamelCase          =					(fft_window_size >> 1) + 1
				UpperCamelCase          =					hop_length
				UpperCamelCase          =					max_length_s
				UpperCamelCase          =					max_length_s * sampling_rate
				UpperCamelCase          =					sampling_rate
				UpperCamelCase          =					frequency_min
				UpperCamelCase          =					frequency_max
				UpperCamelCase          =					mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=_SCREAMING_SNAKE_CASE    ,				min_frequency=_SCREAMING_SNAKE_CASE    ,				max_frequency=_SCREAMING_SNAKE_CASE    ,				sampling_rate=_SCREAMING_SNAKE_CASE    ,				norm=_SCREAMING_SNAKE_CASE    ,				mel_scale='htk'    ,				)
				UpperCamelCase          =					mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=_SCREAMING_SNAKE_CASE    ,				min_frequency=_SCREAMING_SNAKE_CASE    ,				max_frequency=_SCREAMING_SNAKE_CASE    ,				sampling_rate=_SCREAMING_SNAKE_CASE    ,				norm='slaney'    ,				mel_scale='slaney'    ,				)



		def __UpperCamelCase			(			self	)  -> Dict[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					copy.deepcopy(self.__dict__	)
				UpperCamelCase          =					self.__class__.__name__
				if "mel_filters" in output:
						del output["mel_filters"]
				if "mel_filters_slaney" in output:
						del output["mel_filters_slaney"]
				return output



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase          =					spectrogram(
				    _SCREAMING_SNAKE_CASE    ,				window_function(self.fft_window_size    ,				'hann'	)    ,				frame_length=self.fft_window_size    ,				hop_length=self.hop_length    ,				power=2.0    ,				mel_filters=_SCREAMING_SNAKE_CASE    ,				log_mel='dB'    ,				)
				return log_mel_spectrogram.T



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					np.array_split(list(range(0    ,				total_frames - chunk_frames + 1	)	)    ,				3	)
				if len(ranges[1]	) == 0:
						# if the audio is too short, we just use the first chunk
						UpperCamelCase          =					[0]
				if len(ranges[2]	) == 0:
						# if the audio is too short, we just use the first chunk
						UpperCamelCase          =					[0]
				# randomly choose index for each part
				UpperCamelCase          =					np.random.choice(ranges[0]	)
				UpperCamelCase          =					np.random.choice(ranges[1]	)
				UpperCamelCase          =					np.random.choice(ranges[2]	)

				UpperCamelCase          =					mel[idx_front : idx_front + chunk_frames, :]
				UpperCamelCase          =					mel[idx_middle : idx_middle + chunk_frames, :]
				UpperCamelCase          =					mel[idx_back : idx_back + chunk_frames, :]

				UpperCamelCase          =					torch.tensor(mel[None, None, :]	)
				UpperCamelCase          =					torch.nn.functional.interpolate(
				    _SCREAMING_SNAKE_CASE    ,				size=[chunk_frames, 64]    ,				mode='bilinear'    ,				align_corners=_SCREAMING_SNAKE_CASE	)
				UpperCamelCase          =					mel_shrink[0][0].numpy()
				UpperCamelCase          =					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,				axis=0	)
				return mel_fusion



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> np.array:
				"""simple docstring"""
				if waveform.shape[0] > max_length:
						if truncation == "rand_trunc":
								UpperCamelCase          =					True
								# random crop to max_length (for compatibility) -> this should be handled by self.pad
								UpperCamelCase          =					len(_SCREAMING_SNAKE_CASE	) - max_length
								UpperCamelCase          =					np.random.randint(0    ,				overflow + 1	)
								UpperCamelCase          =					waveform[idx : idx + max_length]
								UpperCamelCase          =					self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,				self.mel_filters_slaney	)[None, :]
						elif truncation == "fusion":
								UpperCamelCase          =					self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,				self.mel_filters	)
								UpperCamelCase          =					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
								UpperCamelCase          =					mel.shape[0]
								if chunk_frames == total_frames:
										# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
										# In this case, we just use the whole audio.
										UpperCamelCase          =					np.stack([mel, mel, mel, mel]    ,				axis=0	)
										UpperCamelCase          =					False
								else:
										UpperCamelCase          =					self._random_mel_fusion(_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE	)
										UpperCamelCase          =					True
						else:
								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''	)

				else:
						UpperCamelCase          =					False
						# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
						if waveform.shape[0] < max_length:
								if padding == "repeat":
										UpperCamelCase          =					int(max_length / len(_SCREAMING_SNAKE_CASE	)	)
										UpperCamelCase          =					np.stack(np.tile(_SCREAMING_SNAKE_CASE    ,				n_repeat + 1	)	)[:max_length]
								if padding == "repeatpad":
										UpperCamelCase          =					int(max_length / len(_SCREAMING_SNAKE_CASE	)	)
										UpperCamelCase          =					np.stack(np.tile(_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE	)	)
								UpperCamelCase          =					np.pad(_SCREAMING_SNAKE_CASE    ,				(0, max_length - waveform.shape[0])    ,				mode='constant'    ,				constant_values=0	)

						if truncation == "fusion":
								UpperCamelCase          =					self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,				self.mel_filters	)
								UpperCamelCase          =					np.stack([input_mel, input_mel, input_mel, input_mel]    ,				axis=0	)
						else:
								UpperCamelCase          =					self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,				self.mel_filters_slaney	)[None, :]

				return input_mel, longer



		def __call__(			self    ,				A_    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				**A_    ,				)  -> BatchFeature:
				"""simple docstring"""
				UpperCamelCase          =					truncation if truncation is not None else self.truncation
				UpperCamelCase          =					padding if padding else self.padding

				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.'	)

				UpperCamelCase          =					isinstance(_SCREAMING_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}'''	)
				UpperCamelCase          =					is_batched_numpy or (
				    isinstance(_SCREAMING_SNAKE_CASE    ,				(list, tuple)	) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)	))
				)

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

				# always return batch
				if not is_batched:
						UpperCamelCase          =					[np.asarray(_SCREAMING_SNAKE_CASE	)]

				# convert to mel spectrogram, truncate and pad if needed.
				UpperCamelCase          =					[
				    self._get_input_mel(_SCREAMING_SNAKE_CASE    ,				max_length if max_length else self.nb_max_samples    ,				_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE	)
				    for waveform in raw_speech
				]

				UpperCamelCase          =					[]
				UpperCamelCase          =					[]
				for mel, longer in padded_inputs:
						input_mel.append(_SCREAMING_SNAKE_CASE	)
						is_longer.append(_SCREAMING_SNAKE_CASE	)

				if truncation == "fusion" and sum(_SCREAMING_SNAKE_CASE	) == 0:
						# if no audio is longer than 10s, then randomly select one audio to be longer
						UpperCamelCase          =					np.random.randint(0    ,				len(_SCREAMING_SNAKE_CASE	)	)
						UpperCamelCase          =					True

				if isinstance(input_mel[0]    ,				_SCREAMING_SNAKE_CASE	):
						UpperCamelCase          =					[np.asarray(_SCREAMING_SNAKE_CASE    ,				dtype=np.floataa	) for feature in input_mel]

				# is_longer is a list of bool
				UpperCamelCase          =					[[longer] for longer in is_longer]

				UpperCamelCase          =					{'input_features': input_mel, 'is_longer': is_longer}
				UpperCamelCase          =					BatchFeature(_SCREAMING_SNAKE_CASE	)

				if return_tensors is not None:
						UpperCamelCase          =					input_features.convert_to_tensors(_SCREAMING_SNAKE_CASE	)

				return input_features


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     lowercase :



		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_=512    ,				A_=16    ,				A_=2    ,				A_=0.02    ,				A_=3    ,				A_=4    ,				A_=None    ,				)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					seq_length
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_input_mask
				UpperCamelCase          =					use_token_type_ids
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					type_vocab_size
				UpperCamelCase          =					type_sequence_label_size
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					num_choices
				UpperCamelCase          =					scope



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size	)

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

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

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

				UpperCamelCase          =					self.get_config()

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



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""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=A_    ,				initializer_range=self.initializer_range    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					LlamaModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_	)
				UpperCamelCase          =					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_    ,				)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					True
				UpperCamelCase          =					LlamaModel(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				)
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				)
				UpperCamelCase          =					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_    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					LlamaForCausalLM(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					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_    ,				)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					True
				UpperCamelCase          =					True
				UpperCamelCase          =					LlamaForCausalLM(config=A_	)
				model.to(A_	)
				model.eval()

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

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

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

				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				output_hidden_states=A_    ,				)['hidden_states'][0]
				UpperCamelCase          =					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
				UpperCamelCase          =					ids_tensor((1,)    ,				output_from_past.shape[-1]	).item()
				UpperCamelCase          =					output_from_no_past[:, -3:, random_slice_idx].detach()
				UpperCamelCase          =					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	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  
				)          =					config_and_inputs
				UpperCamelCase          =					{'input_ids': input_ids, 'attention_mask': input_mask}
				return config, inputs_dict






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



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					LlamaModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						UpperCamelCase          =					type
						self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					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	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					'single_label_classification'
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					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[str]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					'multi_label_classification'
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]    ,				self.model_tester.type_sequence_label_size	).to(torch.float	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					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	)  -> Union[str, Any]:
				"""simple docstring"""
				pass



		@parameterized.expand([('linear',), ('dynamic',)]	)
		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					ids_tensor([1, 10]    ,				config.vocab_size	)
				UpperCamelCase          =					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          =					LlamaModel(A_	)
				original_model.to(A_	)
				original_model.eval()
				UpperCamelCase          =					original_model(A_	).last_hidden_state
				UpperCamelCase          =					original_model(A_	).last_hidden_state

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



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor([input_ids]	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					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	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					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]    ,				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	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					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	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					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]    ,				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	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					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	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					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	)    ,				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]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)

				UpperCamelCase          =					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	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# fmt: off
				UpperCamelCase          =					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]    ,				A_    ,				atol=1e-5    ,				rtol=1e-5	)



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

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