
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


UpperCAmelCase	      = logging.get_logger(__name__)







class        A_   (			lowerCAmelCase_   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[int]							 =       ["""input_features"""]





	def __init__(       self		,    snake_case=80		,    snake_case=1_6000		,    snake_case=160		,    snake_case=30		,    snake_case=400		,    snake_case=0.0		,    snake_case=False		,    **snake_case		,    ):
		super().__init__(
		    feature_size=snake_case_		,    sampling_rate=snake_case_		,    padding_value=snake_case_		,    return_attention_mask=snake_case_		,    **snake_case_		,    )
		lowercase       =    n_fft
		lowercase       =    hop_length
		lowercase       =    chunk_length
		lowercase       =    chunk_length * sampling_rate
		lowercase       =    self.n_samples // hop_length
		lowercase       =    sampling_rate
		lowercase       =    mel_filter_bank(
		    num_frequency_bins=1 + n_fft // 2		,    num_mel_filters=snake_case_		,    min_frequency=0.0		,    max_frequency=8000.0		,    sampling_rate=snake_case_		,    norm='slaney'		,    mel_scale='slaney'		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    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'		,    )
		lowercase       =    log_spec[:, :-1]
		lowercase       =    np.maximum(snake_case_		,    log_spec.max() - 8.0       )
		lowercase       =    (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__						(       snake_case		,    snake_case		,    snake_case = 0.0       ):
		if attention_mask is not None:
			lowercase       =    np.array(snake_case_		,    np.intaa       )
			lowercase       =    []

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

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

		return normed_input_values





	def __call__(       self		,    snake_case		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = "max_length"		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    **snake_case		,    ):

		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.'       )

		lowercase       =    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}'''       )
		lowercase       =    is_batched_numpy or (
		    isinstance(snake_case_		,    (list, tuple)       ) and (isinstance(raw_speech[0]		,    (np.ndarray, tuple, list)       ))
		)

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

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

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

		# convert into correct format for padding

		lowercase       =    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:
			lowercase       =    self.zero_mean_unit_var_norm(
			    padded_inputs['input_features']		,    attention_mask=padded_inputs['attention_mask']		,    padding_value=self.padding_value		,    )
			lowercase       =    np.stack(padded_inputs['input_features']		,    axis=0       )

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

		lowercase       =    [self._np_extract_fbank_features(snake_case_       ) for waveform in input_features[0]]

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

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

		if return_tensors is not None:
			lowercase       =    padded_inputs.convert_to_tensors(snake_case_       )

		return padded_inputs





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








'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"			,     [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      int       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path_factory.mktemp("dset_infos_dir"       )
     if "full:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write("---\ndataset_info:\n  dataset_size: 42\n---"       )
     if "empty:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write(""       )
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / "dataset_infos.json"			,     "w"       ) as f:
               f.write("{\"default\": {\"dataset_size\": 42}}"       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"			,     [
        DatasetInfo(),
        DatasetInfo(
            description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      DatasetInfo       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_info.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfo.from_directory(__lowercase       )
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(__lowercase			,     "dataset_info.json"       )       )
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo(
         description="foo"			,     citation="bar"			,     homepage="https://foo.bar"			,     license="CC0"			,     features=Features({"a": Value("int32"       )}       )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train", "num_examples": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert sorted(__lowercase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str)       )
     _UpperCAmelCase     							=   yaml.safe_dump(__lowercase       )
     _UpperCAmelCase     							=   yaml.safe_load(__lowercase       )
     assert dataset_info_yaml_dict == reloaded
def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo()
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}       ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     )
            }       ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42       ),
                "v2": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      DatasetInfosDict       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_infos_dict.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _UpperCAmelCase     							=   config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _UpperCAmelCase     							=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(__lowercase			,     "README.md"       )       )




