


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


snake_case							:       Dict     			=      logging.get_logger(__name__)







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[str]          =			['''input_features''']
					def __init__(      self   :Any			,__snake_case   :Any=80			,__snake_case   :List[str]=1_60_00			,__snake_case   :List[Any]=1_60			,__snake_case   :str=30			,__snake_case   :List[str]=4_00			,__snake_case   :Union[str, Any]=0.0			,__snake_case   :Union[str, Any]=False			,**__snake_case   :Tuple			,)					->    Dict:
												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=80_00.0			,sampling_rate=__snake_case			,norm='slaney'			,mel_scale='slaney'			,)
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.array      )					->    np.ndarray:
												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  lowerCamelCase__(      __snake_case   :List[np.ndarray]			,__snake_case   :List[np.ndarray]			,__snake_case   :float = 0.0      )					->    List[np.ndarray]:
												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   :Tuple			,__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   :str			,)					->    BatchFeature:

												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  lowerCamelCase__(      self   :Optional[Any]      )					->    Dict[str, Any]:
												a__             =     copy.deepcopy(self.__dict__      )
												a__             =     self.__class__.__name__
												if "mel_filters" in output:
																			del output["mel_filters"]
												return output








import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
						from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]      )					->    Union[str, Any]:
												for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['bs']			,model_result['ss']      ):
																										a__             =     model_result['result'][batch_size][sequence_length]
																										self.assertIsNotNone(__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Union[str, Any]:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,only_pretrain_model=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,torchscript=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					@unittest.skipIf(torch_device == 'cpu'			,'Cant do half precision'      )
					def  lowerCamelCase__(      self   :int      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,fpaa=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												# set architectures equal to `None`
												a__             =     None
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Dict      )					->    int:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					@unittest.skipIf(torch_device == 'cpu'			,'Can\'t do half precision'      )
					def  lowerCamelCase__(      self   :int      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,fpaa=__snake_case			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :str      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Any:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,save_to_csv=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,inference_time_csv_file=os.path.join(__snake_case			,'inf_time.csv'      )			,train_memory_csv_file=os.path.join(__snake_case			,'train_mem.csv'      )			,inference_memory_csv_file=os.path.join(__snake_case			,'inf_mem.csv'      )			,train_time_csv_file=os.path.join(__snake_case			,'train_time.csv'      )			,env_info_csv_file=os.path.join(__snake_case			,'env.csv'      )			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'env.csv'      )      ).exists()      )



					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'

												def _check_summary_is_not_empty(__snake_case   :List[str]      ):
																			self.assertTrue(hasattr(__snake_case			,'sequential'      )      )
																			self.assertTrue(hasattr(__snake_case			,'cumulative'      )      )
																			self.assertTrue(hasattr(__snake_case			,'current'      )      )
																			self.assertTrue(hasattr(__snake_case			,'total'      )      )

												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,log_filename=os.path.join(__snake_case			,'log.txt'      )			,log_print=__snake_case			,trace_memory_line_by_line=__snake_case			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			a__             =     benchmark.run()
																			_check_summary_is_not_empty(result.inference_summary      )
																			_check_summary_is_not_empty(result.train_summary      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'log.txt'      )      ).exists()      )




