





import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
	from transformers import LlamaTokenizerFast
except ImportError as e:
	warnings.warn(e)
	warnings.warn(
	    '''The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'''
	)
	lowerCAmelCase_    =				None
lowerCAmelCase_    =				{
    '''7B''': 1_1_0_0_8,
    '''13B''': 1_3_8_2_4,
    '''30B''': 1_7_9_2_0,
    '''65B''': 2_2_0_1_6,
    '''70B''': 2_8_6_7_2,
}
lowerCAmelCase_    =				{
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase=1 ,				_UpperCamelCase=256 )		->						List[str]:






				"""simple docstring"""


				return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)






def      lowerCamelCase_						(				_UpperCamelCase )		->						Dict:






				"""simple docstring"""


				with open(_UpperCamelCase ,				'''r''' ) as f:
								return json.load(_UpperCamelCase )






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


				with open(_UpperCamelCase ,				'''w''' ) as f:
								json.dump(_UpperCamelCase ,				_UpperCamelCase )






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=True )		->						List[Any]:






				"""simple docstring"""


				os.makedirs(_UpperCamelCase ,				exist_ok=_UpperCamelCase )
				snake_case_						:    Tuple				=  os.path.join(_UpperCamelCase ,				'''tmp''' )
				os.makedirs(_UpperCamelCase ,				exist_ok=_UpperCamelCase )

				snake_case_						:    Dict				=  read_json(os.path.join(_UpperCamelCase ,				'''params.json''' ) )
				snake_case_						:    List[Any]				=  NUM_SHARDS[model_size]
				snake_case_						:    Union[str, Any]				=  params['''n_layers''']
				snake_case_						:    List[Any]				=  params['''n_heads''']
				snake_case_						:    int				=  n_heads // num_shards
				snake_case_						:    str				=  params['''dim''']
				snake_case_						:    str				=  dim // n_heads
				snake_case_						:    int				=  10_000.0
				snake_case_						:    List[str]				=  1.0 / (base ** (torch.arange(0 ,				_UpperCamelCase ,				2 ).float() / dims_per_head))

				if "n_kv_heads" in params:
								snake_case_						:    Union[str, Any]				=  params['''n_kv_heads''']  # for GQA / MQA
								snake_case_						:    List[Any]				=  n_heads_per_shard // num_key_value_heads
								snake_case_						:    List[Any]				=  dim // num_key_value_heads
				else:  # compatibility with other checkpoints
								snake_case_						:    List[Any]				=  n_heads
								snake_case_						:    Dict				=  n_heads_per_shard
								snake_case_						:    str				=  dim

				# permute for sliced rotary
				def permute(_UpperCamelCase ,				_UpperCamelCase=n_heads ,				_UpperCamelCase=dim ,				_UpperCamelCase=dim ):
								return w.view(_UpperCamelCase ,				dima // n_heads // 2 ,				2 ,				_UpperCamelCase ).transpose(1 ,				2 ).reshape(_UpperCamelCase ,				_UpperCamelCase )

				print(f'''Fetching all parameters from the checkpoint at {input_base_path}.''' )
				# Load weights
				if model_size == "7B":
								# Not sharded
								# (The sharded implementation would also work, but this is simpler.)
								snake_case_						:    str				=  torch.load(os.path.join(_UpperCamelCase ,				'''consolidated.00.pth''' ) ,				map_location='''cpu''' )
				else:
								# Sharded
								snake_case_						:    List[Any]				=  [
								    torch.load(os.path.join(_UpperCamelCase ,				f'''consolidated.{i:02d}.pth''' ) ,				map_location='''cpu''' )
								    for i in range(_UpperCamelCase )
								]
				snake_case_						:    List[Any]				=  0
				snake_case_						:    str				=  {'''weight_map''': {}}
				for layer_i in range(_UpperCamelCase ):
								snake_case_						:    List[str]				=  f'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
								if model_size == "7B":
												# Unsharded
												snake_case_						:    Optional[Any]				=  {
												    f'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
												        loaded[f'''layers.{layer_i}.attention.wq.weight'''] ),
												    f'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
												        loaded[f'''layers.{layer_i}.attention.wk.weight'''] ),
												    f'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[f'''layers.{layer_i}.attention.wv.weight'''],
												    f'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[f'''layers.{layer_i}.attention.wo.weight'''],
												    f'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w1.weight'''],
												    f'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w2.weight'''],
												    f'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w3.weight'''],
												    f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[f'''layers.{layer_i}.attention_norm.weight'''],
												    f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[f'''layers.{layer_i}.ffn_norm.weight'''],
												}
								else:
												# Sharded
												# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
												# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
												# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

												snake_case_						:    int				=  {
												    f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
												        f'''layers.{layer_i}.attention_norm.weight'''
												    ].clone(),
												    f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
												        f'''layers.{layer_i}.ffn_norm.weight'''
												    ].clone(),
												}
												snake_case_						:    List[Any]				=  permute(
												    torch.cat(
												        [
												            loaded[i][f'''layers.{layer_i}.attention.wq.weight'''].view(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )
												            for i in range(_UpperCamelCase )
												        ] ,				dim=0 ,				).reshape(_UpperCamelCase ,				_UpperCamelCase ) )
												snake_case_						:    Optional[int]				=  permute(
												    torch.cat(
												        [
												            loaded[i][f'''layers.{layer_i}.attention.wk.weight'''].view(
												                _UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )
												            for i in range(_UpperCamelCase )
												        ] ,				dim=0 ,				).reshape(_UpperCamelCase ,				_UpperCamelCase ) ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				)
												snake_case_						:    Any				=  torch.cat(
												    [
												        loaded[i][f'''layers.{layer_i}.attention.wv.weight'''].view(
												            _UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )
												        for i in range(_UpperCamelCase )
												    ] ,				dim=0 ,				).reshape(_UpperCamelCase ,				_UpperCamelCase )

												snake_case_						:    Dict				=  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.attention.wo.weight'''] for i in range(_UpperCamelCase )] ,				dim=1 )
												snake_case_						:    str				=  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(_UpperCamelCase )] ,				dim=0 )
												snake_case_						:    Optional[Any]				=  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(_UpperCamelCase )] ,				dim=1 )
												snake_case_						:    Optional[int]				=  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(_UpperCamelCase )] ,				dim=0 )

								snake_case_						:    Dict				=  inv_freq
								for k, v in state_dict.items():
												snake_case_						:    Optional[int]				=  filename
												param_count += v.numel()
								torch.save(_UpperCamelCase ,				os.path.join(_UpperCamelCase ,				_UpperCamelCase ) )

				snake_case_						:    str				=  f'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
				if model_size == "7B":
								# Unsharded
								snake_case_						:    List[str]				=  {
								    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
								    '''model.norm.weight''': loaded['''norm.weight'''],
								    '''lm_head.weight''': loaded['''output.weight'''],
								}
				else:
								snake_case_						:    Optional[int]				=  {
								    '''model.norm.weight''': loaded[0]['''norm.weight'''],
								    '''model.embed_tokens.weight''': torch.cat(
								        [loaded[i]['''tok_embeddings.weight'''] for i in range(_UpperCamelCase )] ,				dim=1 ),
								    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_UpperCamelCase )] ,				dim=0 ),
								}

				for k, v in state_dict.items():
								snake_case_						:    int				=  filename
								param_count += v.numel()
				torch.save(_UpperCamelCase ,				os.path.join(_UpperCamelCase ,				_UpperCamelCase ) )

				# Write configs
				snake_case_						:    Optional[int]				=  {'''total_size''': param_count * 2}
				write_json(_UpperCamelCase ,				os.path.join(_UpperCamelCase ,				'''pytorch_model.bin.index.json''' ) )
				snake_case_						:    Any				=  params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
				snake_case_						:    Any				=  params['''multiple_of'''] if '''multiple_of''' in params else 256
				snake_case_						:    Dict				=  LlamaConfig(
				    hidden_size=_UpperCamelCase ,				intermediate_size=compute_intermediate_size(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ) ,				num_attention_heads=params['''n_heads'''] ,				num_hidden_layers=params['''n_layers'''] ,				rms_norm_eps=params['''norm_eps'''] ,				num_key_value_heads=_UpperCamelCase ,				)
				config.save_pretrained(_UpperCamelCase )

				# Make space so we can load the model properly now.
				del state_dict
				del loaded
				gc.collect()

				print('''Loading the checkpoint in a Llama model.''' )
				snake_case_						:    Optional[Any]				=  LlamaForCausalLM.from_pretrained(_UpperCamelCase ,				torch_dtype=torch.floataa ,				low_cpu_mem_usage=_UpperCamelCase )
				# Avoid saving this as part of the config.
				del model.config._name_or_path

				print('''Saving in the Transformers format.''' )
				model.save_pretrained(_UpperCamelCase ,				safe_serialization=_UpperCamelCase )
				shutil.rmtree(_UpperCamelCase )






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
				print(f'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.''' )
				snake_case_						:    Union[str, Any]				=  tokenizer_class(_UpperCamelCase )
				tokenizer.save_pretrained(_UpperCamelCase )






def      lowerCamelCase_						(				)		->						Tuple:






				"""simple docstring"""


				snake_case_						:    List[Any]				=  argparse.ArgumentParser()
				parser.add_argument(
				    '''--input_dir''' ,				help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' ,				)
				parser.add_argument(
				    '''--model_size''' ,				choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] ,				)
				parser.add_argument(
				    '''--output_dir''' ,				help='''Location to write HF model and tokenizer''' ,				)
				parser.add_argument('''--safe_serialization''' ,				type=_UpperCamelCase ,				help='''Whether or not to save using `safetensors`.''' )
				snake_case_						:    Dict				=  parser.parse_args()
				if args.model_size != "tokenizer_only":
								write_model(
								    model_path=args.output_dir ,				input_base_path=os.path.join(args.input_dir ,				args.model_size ) ,				model_size=args.model_size ,				safe_serialization=args.safe_serialization ,				)
				snake_case_						:    str				=  os.path.join(args.input_dir ,				'''tokenizer.model''' )
				write_tokenizer(args.output_dir ,				_UpperCamelCase )


if __name__ == "__main__":
	main()







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import WhisperFeatureExtractor

if is_torch_available():
	import torch

lowerCAmelCase_    =				random.Random()






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase=1.0 ,				_UpperCamelCase=None ,				_UpperCamelCase=None )		->						List[Any]:






				"""simple docstring"""


				if rng is None:
								snake_case_						:    str				=  global_rng

				snake_case_						:    Any				=  []
				for batch_idx in range(shape[0] ):
								values.append([] )
								for _ in range(shape[1] ):
												values[-1].append(rng.random() * scale )

				return values



@require_torch
@require_torchaudio
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=400					,							__magic_name__=2000					,							__magic_name__=10					,							__magic_name__=160					,							__magic_name__=8					,							__magic_name__=0.0					,							__magic_name__=4000					,							__magic_name__=False					,							__magic_name__=True					,							)     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    str				=  batch_size
							snake_case_						:    Union[str, Any]				=  min_seq_length
							snake_case_						:    Tuple				=  max_seq_length
							snake_case_						:    Optional[Any]				=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							snake_case_						:    Optional[int]				=  padding_value
							snake_case_						:    Union[str, Any]				=  sampling_rate
							snake_case_						:    Optional[int]				=  return_attention_mask
							snake_case_						:    str				=  do_normalize
							snake_case_						:    str				=  feature_size
							snake_case_						:    Optional[Any]				=  chunk_length
							snake_case_						:    Union[str, Any]				=  hop_length






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


							'''simple docstring'''



							return {
							    "feature_size": self.feature_size,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}






			def 			lowerCamelCase  (self					,							__magic_name__=False					,							__magic_name__=False    )     ->   Optional[Any]:


							'''simple docstring'''



							def _flatten(__magic_name__    ):
											return list(itertools.chain(*__magic_name__    )    )

							if equal_length:
											snake_case_						:    int				=  [floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
							else:
											# make sure that inputs increase in size
											snake_case_						:    int				=  [
											    floats_list((x, self.feature_size)    )
											    for x in range(self.min_seq_length					,							self.max_seq_length					,							self.seq_length_diff    )
											]
							if numpify:
											snake_case_						:    str				=  [np.asarray(__magic_name__    ) for x in speech_inputs]
							return speech_inputs




@require_torch
@require_torchaudio
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      WhisperFeatureExtractor if is_speech_available() else None






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


							'''simple docstring'''



							snake_case_						:    List[str]				=  WhisperFeatureExtractionTester(self    )






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


							'''simple docstring'''



							snake_case_						:    str				=  self.feature_extraction_class(**self.feat_extract_dict    )

							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_						:    Union[str, Any]				=  feat_extract_first.save_pretrained(__magic_name__    )[0]
											check_json_file_has_correct_format(__magic_name__    )
											snake_case_						:    List[Any]				=  self.feature_extraction_class.from_pretrained(__magic_name__    )

							snake_case_						:    Optional[int]				=  feat_extract_first.to_dict()
							snake_case_						:    Dict				=  feat_extract_second.to_dict()
							snake_case_						:    List[str]				=  feat_extract_first.mel_filters
							snake_case_						:    Union[str, Any]				=  feat_extract_second.mel_filters
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__    )    )
							self.assertEqual(__magic_name__					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.feature_extraction_class(**self.feat_extract_dict    )

							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_						:    List[Any]				=  os.path.join(__magic_name__					,							'''feat_extract.json'''    )
											feat_extract_first.to_json_file(__magic_name__    )
											snake_case_						:    Optional[int]				=  self.feature_extraction_class.from_json_file(__magic_name__    )

							snake_case_						:    int				=  feat_extract_first.to_dict()
							snake_case_						:    Optional[int]				=  feat_extract_second.to_dict()
							snake_case_						:    Union[str, Any]				=  feat_extract_first.mel_filters
							snake_case_						:    str				=  feat_extract_second.mel_filters
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__    )    )
							self.assertEqual(__magic_name__					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							# create three inputs of length 800, 1000, and 1200
							snake_case_						:    Any				=  [floats_list((1, x)    )[0] for x in range(800					,							1400					,							200    )]
							snake_case_						:    str				=  [np.asarray(__magic_name__    ) for speech_input in speech_inputs]

							# Test feature size
							snake_case_						:    str				=  feature_extractor(__magic_name__					,							padding='''max_length'''					,							return_tensors='''np'''    ).input_features
							self.assertTrue(input_features.ndim == 3    )
							self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames    )
							self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size    )

							# Test not batched input
							snake_case_						:    Dict				=  feature_extractor(speech_inputs[0]					,							return_tensors='''np'''    ).input_features
							snake_case_						:    Optional[int]				=  feature_extractor(np_speech_inputs[0]					,							return_tensors='''np'''    ).input_features
							self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )

							# Test batched
							snake_case_						:    int				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							snake_case_						:    Union[str, Any]				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							for enc_seq_a, enc_seq_a in zip(__magic_name__					,							__magic_name__    ):
											self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )

							# Test 2-D numpy arrays are batched.
							snake_case_						:    Union[str, Any]				=  [floats_list((1, x)    )[0] for x in (800, 800, 800)]
							snake_case_						:    List[str]				=  np.asarray(__magic_name__    )
							snake_case_						:    List[Any]				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							snake_case_						:    Dict				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							for enc_seq_a, enc_seq_a in zip(__magic_name__					,							__magic_name__    ):
											self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )

							# Test truncation required
							snake_case_						:    Any				=  [floats_list((1, x)    )[0] for x in range(200					,							(feature_extractor.n_samples + 500)					,							200    )]
							snake_case_						:    Union[str, Any]				=  [np.asarray(__magic_name__    ) for speech_input in speech_inputs]

							snake_case_						:    Tuple				=  [x[: feature_extractor.n_samples] for x in speech_inputs]
							snake_case_						:    Optional[Any]				=  [np.asarray(__magic_name__    ) for speech_input in speech_inputs_truncated]

							snake_case_						:    Any				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							snake_case_						:    List[Any]				=  feature_extractor(__magic_name__					,							return_tensors='''np'''    ).input_features
							for enc_seq_a, enc_seq_a in zip(__magic_name__					,							__magic_name__    ):
											self.assertTrue(np.allclose(__magic_name__					,							__magic_name__					,							atol=1e-3    )    )






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							import torch

							snake_case_						:    str				=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							snake_case_						:    Union[str, Any]				=  np.random.rand(100					,							32    ).astype(np.floataa    )
							snake_case_						:    Dict				=  np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											snake_case_						:    Optional[Any]				=  feature_extractor.pad([{'''input_features''': inputs}]					,							return_tensors='''np'''    )
											self.assertTrue(np_processed.input_features.dtype == np.floataa    )
											snake_case_						:    Optional[Any]				=  feature_extractor.pad([{'''input_features''': inputs}]					,							return_tensors='''pt'''    )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,							'''clean'''					,							split='''validation'''    )
							# automatic decoding with librispeech
							snake_case_						:    Optional[Any]				=  ds.sort('''id'''    ).select(range(__magic_name__    )    )[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    str				=  torch.tensor(
							    [
							        0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
							        0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
							        0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
							        -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
							    ]    )
							# fmt: on

							snake_case_						:    List[Any]				=  self._load_datasamples(1    )
							snake_case_						:    Union[str, Any]				=  WhisperFeatureExtractor()
							snake_case_						:    Union[str, Any]				=  feature_extractor(__magic_name__					,							return_tensors='''pt'''    ).input_features
							self.assertEqual(input_features.shape					,							(1, 80, 3000)    )
							self.assertTrue(torch.allclose(input_features[0, 0, :30]					,							__magic_name__					,							atol=1e-4    )    )






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
							snake_case_						:    Optional[int]				=  self._load_datasamples(1    )[0]
							snake_case_						:    List[str]				=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
							snake_case_						:    Optional[Any]				=  feat_extract.zero_mean_unit_var_norm([audio]					,							attention_mask=__magic_name__    )[0]

							self.assertTrue(np.all(np.mean(__magic_name__    ) < 1e-3    )    )
							self.assertTrue(np.all(np.abs(np.var(__magic_name__    ) - 1    ) < 1e-3    )    )
