



"""simple docstring"""
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


A:   int								=		logging.get_logger(__name__)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   str						=						['input_features']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=80      ,     _SCREAMING_SNAKE_CASE=16000      ,     _SCREAMING_SNAKE_CASE=160      ,     _SCREAMING_SNAKE_CASE=30      ,     _SCREAMING_SNAKE_CASE=400      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=False      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''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	:					int	=	n_fft
    UpperCAmelCase	:					str	=	hop_length
    UpperCAmelCase	:					Tuple	=	chunk_length
    UpperCAmelCase	:					Optional[Any]	=	chunk_length * sampling_rate
    UpperCAmelCase	:					str	=	self.n_samples // hop_length
    UpperCAmelCase	:					Any	=	sampling_rate
    UpperCAmelCase	:					List[Any]	=	mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2      ,     num_mel_filters=_SCREAMING_SNAKE_CASE      ,     min_frequency=0.0      ,     max_frequency=8000.0      ,     sampling_rate=_SCREAMING_SNAKE_CASE      ,     norm="""slaney"""      ,     mel_scale="""slaney"""      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      np.ndarray:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	spectrogram(
        _SCREAMING_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"""      ,     )
    UpperCAmelCase	:					List[str]	=	log_spec[:, :-1]
    UpperCAmelCase	:					Any	=	np.maximum(_SCREAMING_SNAKE_CASE      ,     log_spec.max() - 8.0						)
    UpperCAmelCase	:					List[Any]	=	(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 	SCREAMING_SNAKE_CASE     (   _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = 0.0						)  ->      List[np.ndarray]:





    '''simple docstring'''




    if attention_mask is not None:
     UpperCAmelCase	:					Optional[Any]	=	np.array(_SCREAMING_SNAKE_CASE      ,     np.intaa						)
     UpperCAmelCase	:					List[str]	=	[]

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

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

    return normed_input_values

   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = True      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "max_length"      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      BatchFeature:





    '''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."""						)

    UpperCAmelCase	:					Tuple	=	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	:					Optional[Any]	=	is_batched_numpy or (
        isinstance(_SCREAMING_SNAKE_CASE      ,     (list, tuple)						) and (isinstance(raw_speech[0]      ,     (np.ndarray, tuple, list)						))
    )

    if is_batched:
     UpperCAmelCase	:					Any	=	[np.asarray([speech]      ,     dtype=np.floataa						).T for speech in raw_speech]
    elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE      ,     np.ndarray						):
     UpperCAmelCase	:					List[str]	=	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	:					List[Any]	=	raw_speech.astype(np.floataa						)

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

    UpperCAmelCase	:					Union[str, Any]	=	BatchFeature({"""input_features""": raw_speech}						)

    # convert into correct format for padding

    UpperCAmelCase	:					Optional[int]	=	self.pad(
        _SCREAMING_SNAKE_CASE      ,     padding=_SCREAMING_SNAKE_CASE      ,     max_length=max_length if max_length else self.n_samples      ,     truncation=_SCREAMING_SNAKE_CASE      ,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,     return_attention_mask=return_attention_mask or do_normalize      ,     )

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

    # make sure list is in array format
    UpperCAmelCase	:					Optional[int]	=	padded_inputs.get("""input_features"""						).transpose(2      ,     0      ,     1						)

    UpperCAmelCase	:					int	=	[self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

    if isinstance(input_features[0]      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Any	=	[np.asarray(_SCREAMING_SNAKE_CASE      ,     dtype=np.floataa						) for feature in input_features]
    else:
     UpperCAmelCase	:					Dict	=	input_features

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

    if return_tensors is not None:
     UpperCAmelCase	:					str	=	padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE						)

    return padded_inputs




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	copy.deepcopy(self.__dict__						)
    UpperCAmelCase	:					Optional[Any]	=	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_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
      import tensorflow as tf

      from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments







@require_tf
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any							,				A :    Union[str, Any]		):
         for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["""bs"""]							,				model_result["""ss"""]		):
             __snake_case:						Dict   =						model_result["""result"""][batch_size][sequence_length]
             self.assertIsNotNone(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Dict   =						TensorFlowBenchmark(A		)
         __snake_case:						List[str]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Optional[int]   =						"""sgugger/tiny-distilbert-classification"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				only_pretrain_model=A							,				)
         __snake_case:						Any   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						int   =						TensorFlowBenchmark(A		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						int   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Optional[int]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Tuple   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						str   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Tuple   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[int]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						List[str]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Dict   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						List[Any]   =						"""patrickvonplaten/t5-tiny-random"""
         __snake_case:						List[str]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						List[str]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Optional[int]   =						TensorFlowBenchmark(A							,				configs=[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""		)		) == 0							,				"""Cannot do xla on CPU."""		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				use_xla=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[str]   =						"""sshleifer/tiny-gpt2"""
         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						int   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				save_to_csv=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				inference_time_csv_file=os.path.join(A							,				"""inf_time.csv"""		)							,				inference_memory_csv_file=os.path.join(A							,				"""inf_mem.csv"""		)							,				env_info_csv_file=os.path.join(A							,				"""env.csv"""		)							,				multi_process=A							,				)
           __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
           benchmark.run()
           self.assertTrue(Path(os.path.join(A							,				"""inf_time.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""inf_mem.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""env.csv"""		)		).exists()		)






       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						"""sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(A :    Union[str, Any]		):
           self.assertTrue(hasattr(A							,				"""sequential"""		)		)
           self.assertTrue(hasattr(A							,				"""cumulative"""		)		)
           self.assertTrue(hasattr(A							,				"""current"""		)		)
           self.assertTrue(hasattr(A							,				"""total"""		)		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				log_filename=os.path.join(A							,				"""log.txt"""		)							,				log_print=A							,				trace_memory_line_by_line=A							,				eager_mode=A							,				multi_process=A							,				)
           __snake_case:						Dict   =						TensorFlowBenchmark(A		)
           __snake_case:						List[str]   =						benchmark.run()
           _check_summary_is_not_empty(result.inference_summary		)
           self.assertTrue(Path(os.path.join(A							,				"""log.txt"""		)		).exists()		)






