
'''simple docstring'''



import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np





def        _A						(							lowercase__						,						lowercase__		):
						lowercase__							       =				f'''{sampling_rate}'''
						lowercase__							       =				'1'
						lowercase__							       =				'f32le'
						lowercase__							       =				[
						    'ffmpeg',
						    '-i',
						    'pipe:0',
						    '-ac',
						    ac,
						    '-ar',
						    ar,
						    '-f',
						    format_for_conversion,
						    '-hide_banner',
						    '-loglevel',
						    'quiet',
						    'pipe:1',
						]

						try:
												with subprocess.Popen(_SCREAMING_SNAKE_CASE						,						stdin=subprocess.PIPE						,						stdout=subprocess.PIPE		) as ffmpeg_process:
																		lowercase__							       =				ffmpeg_process.communicate(_SCREAMING_SNAKE_CASE		)
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""		) from error
						lowercase__							       =				output_stream[0]
						lowercase__							       =				np.frombuffer(_SCREAMING_SNAKE_CASE						,						np.floataa		)
						if audio.shape[0] == 0:
												raise ValueError("""Malformed soundfile"""		)
						return audio





def        _A						(							lowercase__						,						lowercase__						,						lowercase__ = "f32le"						,						):
						lowercase__							       =				f'''{sampling_rate}'''
						lowercase__							       =				'1'
						if format_for_conversion == "s16le":
												lowercase__							       =				2
						elif format_for_conversion == "f32le":
												lowercase__							       =				4
						else:
												raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

						lowercase__							       =				platform.system()
						if system == "Linux":
												lowercase__							       =				'alsa'
												lowercase__							       =				'default'
						elif system == "Darwin":
												lowercase__							       =				'avfoundation'
												lowercase__							       =				':0'
						elif system == "Windows":
												lowercase__							       =				'dshow'
												lowercase__							       =				'default'

						lowercase__							       =				[
						    'ffmpeg',
						    '-f',
						    format_,
						    '-i',
						    input_,
						    '-ac',
						    ac,
						    '-ar',
						    ar,
						    '-f',
						    format_for_conversion,
						    '-fflags',
						    'nobuffer',
						    '-hide_banner',
						    '-loglevel',
						    'quiet',
						    'pipe:1',
						]
						lowercase__							       =				int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
						lowercase__							       =				_ffmpeg_stream(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE		)
						for item in iterator:
												yield item





def        _A						(							lowercase__						,						lowercase__						,						lowercase__ = None						,						lowercase__ = None						,						lowercase__ = "f32le"						,						):
						if stream_chunk_s is not None:
												lowercase__							       =				stream_chunk_s
						else:
												lowercase__							       =				chunk_length_s

						lowercase__							       =				ffmpeg_microphone(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						format_for_conversion=_SCREAMING_SNAKE_CASE		)
						if format_for_conversion == "s16le":
												lowercase__							       =				np.intaa
												lowercase__							       =				2
						elif format_for_conversion == "f32le":
												lowercase__							       =				np.floataa
												lowercase__							       =				4
						else:
												raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

						if stride_length_s is None:
												lowercase__							       =				chunk_length_s / 6
						lowercase__							       =				int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
						if isinstance(_SCREAMING_SNAKE_CASE						,						(int, float)		):
												lowercase__							       =				[stride_length_s, stride_length_s]

						lowercase__							       =				int(round(sampling_rate * stride_length_s[0]		)		) * size_of_sample
						lowercase__							       =				int(round(sampling_rate * stride_length_s[1]		)		) * size_of_sample
						lowercase__							       =				datetime.datetime.now()
						lowercase__							       =				datetime.timedelta(seconds=_SCREAMING_SNAKE_CASE		)
						for item in chunk_bytes_iter(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						stride=(stride_left, stride_right)						,						stream=_SCREAMING_SNAKE_CASE		):
												# Put everything back in numpy scale
												lowercase__							       =				np.frombuffer(item["""raw"""]						,						dtype=_SCREAMING_SNAKE_CASE		)
												lowercase__							       =				(
												    item['stride'][0] // size_of_sample,
												    item['stride'][1] // size_of_sample,
												)
												lowercase__							       =				sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 10 * delta:
																		# We're late !! SKIP
																		continue
												yield item





def        _A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__ = False		):
						lowercase__							       =				b''
						lowercase__							       =				stride
						if stride_left + stride_right >= chunk_len:
												raise ValueError(
												    f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''		)
						lowercase__							       =				0
						for raw in iterator:
												acc += raw
												if stream and len(_SCREAMING_SNAKE_CASE		) < chunk_len:
																		lowercase__							       =				(_stride_left, 0)
																		yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
												else:
																		while len(_SCREAMING_SNAKE_CASE		) >= chunk_len:
																								# We are flushing the accumulator
																								lowercase__							       =				(_stride_left, stride_right)
																								lowercase__							       =				{'raw': acc[:chunk_len], 'stride': stride}
																								if stream:
																														lowercase__							       =				False
																								yield item
																								lowercase__							       =				stride_left
																								lowercase__							       =				acc[chunk_len - stride_left - stride_right :]
												# Last chunk
						if len(_SCREAMING_SNAKE_CASE		) > stride_left:
												lowercase__							       =				{'raw': acc, 'stride': (_stride_left, 0)}
												if stream:
																		lowercase__							       =				False
												yield item







def        _A						(							lowercase__						,						lowercase__		):
						lowercase__							       =				2**24  # 16Mo
						try:
												with subprocess.Popen(_SCREAMING_SNAKE_CASE						,						stdout=subprocess.PIPE						,						bufsize=_SCREAMING_SNAKE_CASE		) as ffmpeg_process:
																		while True:
																								lowercase__							       =				ffmpeg_process.stdout.read(_SCREAMING_SNAKE_CASE		)
																								if raw == b"":
																														break
																								yield raw
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""		) from error







"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase__					       =	random.Random()







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=1.0		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None   )	->Optional[int]:
  if rng is None:
    a__:     Any										=	global_rng

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

  return values




class    __snake_case       (     unittest.TestCase				):



      def __init__(						self    ,			lowercase    ,			lowercase=7    ,			lowercase=4_00    ,			lowercase=20_00    ,			lowercase=1    ,			lowercase=0.0    ,			lowercase=1_60_00    ,			lowercase=True    ,			lowercase=True    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	parent
        a__:     Optional[int]										=	batch_size
        a__:     Optional[Any]										=	min_seq_length
        a__:     Optional[int]										=	max_seq_length
        a__:     Tuple										=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__:     Dict										=	feature_size
        a__:     Any										=	padding_value
        a__:     Optional[Any]										=	sampling_rate
        a__:     Optional[Any]										=	return_attention_mask
        a__:     str										=	do_normalize



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return {
            "feature_size": self.feature_size,
            "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    ,			lowercase=False    ,			lowercase=False)	->			Tuple:





        '''simple docstring'''





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

        if equal_length:
          a__:     Dict										=	floats_list((self.batch_size, self.max_seq_length))
        else:
          # make sure that inputs increase in size
          a__:     List[Any]										=	[
              _flatten(floats_list((x, self.feature_size)))
              for x in range(self.min_seq_length    ,			self.max_seq_length    ,			self.seq_length_diff)
          ]

        if numpify:
          a__:     str										=	[np.asarray(lowercase) for x in speech_inputs]

        return speech_inputs







class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		WavaVecaFeatureExtractor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[int]										=	WavaVecaFeatureExtractionTester(self)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        self.assertTrue(np.all(np.mean(lowercase    ,			axis=0) < 1e-3))
        self.assertTrue(np.all(np.abs(np.var(lowercase    ,			axis=0) - 1) < 1e-3))



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





        '''simple docstring'''





        a__:     List[Any]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:     Optional[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     List[str]										=	[np.asarray(lowercase) for speech_input in speech_inputs]

        # Test not batched input
        a__:     Optional[Any]										=	feat_extract(speech_inputs[0]    ,			return_tensors='np').input_values
        a__:     Dict										=	feat_extract(np_speech_inputs[0]    ,			return_tensors='np').input_values
        self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))

        # Test batched
        a__:     Dict										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     int										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(lowercase    ,			lowercase):
          self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))

        # Test 2-D numpy arrays are batched.
        a__:     int										=	[floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
        a__:     Union[str, Any]										=	np.asarray(lowercase)
        a__:     int										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     Any										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(lowercase    ,			lowercase):
          self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     List[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]

        a__:     Optional[int]										=	['longest', 'max_length', 'do_not_pad']
        a__:     List[Any]										=	[None, 16_00, None]
        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     Dict										=	feat_extract(lowercase    ,			padding=lowercase    ,			max_length=lowercase    ,			return_tensors='np')
          a__:     Union[str, Any]										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



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





        '''simple docstring'''





        a__:     str										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Optional[int]										=	range(8_00    ,			14_00    ,			2_00)
        a__:     List[str]										=	[floats_list((1, x))[0] for x in lengths]

        a__:     Tuple										=	['longest', 'max_length', 'do_not_pad']
        a__:     Dict										=	[None, 16_00, None]

        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     int										=	feat_extract(lowercase    ,			max_length=lowercase    ,			padding=lowercase)
          a__:     Any										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



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





        '''simple docstring'''





        a__:     Any										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Any										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Dict										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='max_length'    ,			return_tensors='np')
        a__:     int										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1])
        self._check_zero_mean_unit_variance(input_values[2])



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     int										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     str										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     Any										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 10_00))

        a__:     Dict										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Tuple										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=20_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     str										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 12_00))



      @require_torch
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        import torch

        a__:     Optional[int]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Tuple										=	np.random.rand(1_00).astype(np.floataa)
        a__:     Tuple										=	np_speech_inputs.tolist()

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



      @slow
      @require_torch
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          a__:     str										=	WavaVecaConfig.from_pretrained(lowercase)
          a__:     str										=	WavaVecaFeatureExtractor.from_pretrained(lowercase)

          # only "layer" feature extraction norm should make use of
          # attention_mask
          self.assertEqual(feat_extract.return_attention_mask    ,			config.feat_extract_norm == 'layer')


