






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ConditionalDetrImageProcessor




class     _lowerCAmelCase(	unittest.TestCase ):





							"""simple docstring"""







							def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=7  ,      _lowerCamelCase=3  ,      _lowerCamelCase=3_0  ,      _lowerCamelCase=4_0_0  ,      _lowerCamelCase=True  ,      _lowerCamelCase=None  ,      _lowerCamelCase=True  ,      _lowerCamelCase=[0.5, 0.5, 0.5]  ,      _lowerCamelCase=[0.5, 0.5, 0.5]  ,      _lowerCamelCase=True  ,      _lowerCamelCase=1 / 2_5_5  ,      _lowerCamelCase=True  ,      ):
									UpperCamelCase_: List[Any]   				=    size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
									UpperCamelCase_: List[Any]   				=    parent
									UpperCamelCase_: Optional[int]   				=    batch_size
									UpperCamelCase_: List[str]   				=    num_channels
									UpperCamelCase_: Dict   				=    min_resolution
									UpperCamelCase_: List[Any]   				=    max_resolution
									UpperCamelCase_: Dict   				=    do_resize
									UpperCamelCase_: str   				=    size
									UpperCamelCase_: Any   				=    do_normalize
									UpperCamelCase_: Optional[int]   				=    image_mean
									UpperCamelCase_: Tuple   				=    image_std
									UpperCamelCase_: List[Any]   				=    do_rescale
									UpperCamelCase_: List[str]   				=    rescale_factor
									UpperCamelCase_: Union[str, Any]   				=    do_pad





							def       _a   ( self							):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}





							def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase=False							):



									if not batched:
											UpperCamelCase_: Optional[Any]   				=    image_inputs[0]
											if isinstance(UpperCamelCase__  ,      Image.Image							):
													UpperCamelCase_				,UpperCamelCase_: Tuple   				=    image.size
											else:
													UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    image.shape[1], image.shape[2]
											if w < h:
													UpperCamelCase_: Optional[Any]   				=    int(self.size['shortest_edge'] * h / w							)
													UpperCamelCase_: Union[str, Any]   				=    self.size['shortest_edge']
											elif w > h:
													UpperCamelCase_: Optional[Any]   				=    self.size['shortest_edge']
													UpperCamelCase_: Optional[Any]   				=    int(self.size['shortest_edge'] * w / h							)
											else:
													UpperCamelCase_: str   				=    self.size['shortest_edge']
													UpperCamelCase_: Tuple   				=    self.size['shortest_edge']

									else:
											UpperCamelCase_: Any   				=    []
											for image in image_inputs:
													UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_expected_values([image]							)
													expected_values.append((expected_height, expected_width)							)
											UpperCamelCase_: Optional[Any]   				=    max(UpperCamelCase__  ,      key=lambda _lowerCamelCase							: item[0]							)[0]
											UpperCamelCase_: List[Any]   				=    max(UpperCamelCase__  ,      key=lambda _lowerCamelCase							: item[1]							)[1]

									return expected_height, expected_width







@require_torch
@require_vision
class     _lowerCAmelCase(	__a							,     unittest.TestCase ):





							"""simple docstring"""







							a    :		Dict									=ConditionalDetrImageProcessor if is_vision_available() else None





							def       _a   ( self							):
									UpperCamelCase_: Optional[Any]   				=    ConditionalDetrImageProcessingTester(self							)





							@property
							def       _a   ( self							):
									return self.image_processor_tester.prepare_image_processor_dict()





							def       _a   ( self							):
									UpperCamelCase_: Tuple   				=    self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(UpperCamelCase__  ,      'image_mean'							)							)
									self.assertTrue(hasattr(UpperCamelCase__  ,      'image_std'							)							)
									self.assertTrue(hasattr(UpperCamelCase__  ,      'do_normalize'							)							)
									self.assertTrue(hasattr(UpperCamelCase__  ,      'do_resize'							)							)
									self.assertTrue(hasattr(UpperCamelCase__  ,      'size'							)							)





							def       _a   ( self							):
									UpperCamelCase_: Tuple   				=    self.image_processing_class.from_dict(self.image_processor_dict							)
									self.assertEqual(image_processor.size  ,      {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}							)
									self.assertEqual(image_processor.do_pad  ,      UpperCamelCase__							)

									UpperCamelCase_: List[Any]   				=    self.image_processing_class.from_dict(
									    self.image_processor_dict  ,      size=4_2  ,      max_size=8_4  ,      pad_and_return_pixel_mask=UpperCamelCase__							)
									self.assertEqual(image_processor.size  ,      {'shortest_edge': 4_2, 'longest_edge': 8_4}							)
									self.assertEqual(image_processor.do_pad  ,      UpperCamelCase__							)





							def       _a   ( self							):
									pass





							def       _a   ( self							):
									UpperCamelCase_: Any   				=    self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									UpperCamelCase_: Tuple   				=    prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=UpperCamelCase__							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase__  ,      Image.Image							)

									# Test not batched input
									UpperCamelCase_: str   				=    image_processing(image_inputs[0]  ,      return_tensors='pt'							).pixel_values

									UpperCamelCase_				,UpperCamelCase_: List[Any]   				=    self.image_processor_tester.get_expected_values(UpperCamelCase__							)

									self.assertEqual(
									    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

									# Test batched
									UpperCamelCase_				,UpperCamelCase_: Dict   				=    self.image_processor_tester.get_expected_values(UpperCamelCase__  ,      batched=UpperCamelCase__							)

									UpperCamelCase_: Optional[int]   				=    image_processing(UpperCamelCase__  ,      return_tensors='pt'							).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )  ,      )





							def       _a   ( self							):
									UpperCamelCase_: List[Any]   				=    self.image_processing_class(**self.image_processor_dict							)
									# create random numpy tensors
									UpperCamelCase_: Any   				=    prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=UpperCamelCase__  ,      numpify=UpperCamelCase__							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase__  ,      np.ndarray							)

									# Test not batched input
									UpperCamelCase_: Tuple   				=    image_processing(image_inputs[0]  ,      return_tensors='pt'							).pixel_values

									UpperCamelCase_				,UpperCamelCase_: str   				=    self.image_processor_tester.get_expected_values(UpperCamelCase__							)

									self.assertEqual(
									    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

									# Test batched
									UpperCamelCase_: List[str]   				=    image_processing(UpperCamelCase__  ,      return_tensors='pt'							).pixel_values

									UpperCamelCase_				,UpperCamelCase_: Any   				=    self.image_processor_tester.get_expected_values(UpperCamelCase__  ,      batched=UpperCamelCase__							)

									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )  ,      )





							def       _a   ( self							):
									UpperCamelCase_: Optional[Any]   				=    self.image_processing_class(**self.image_processor_dict							)
									# create random PyTorch tensors
									UpperCamelCase_: Any   				=    prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=UpperCamelCase__  ,      torchify=UpperCamelCase__							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase__  ,      torch.Tensor							)

									# Test not batched input
									UpperCamelCase_: Optional[int]   				=    image_processing(image_inputs[0]  ,      return_tensors='pt'							).pixel_values

									UpperCamelCase_				,UpperCamelCase_: List[Any]   				=    self.image_processor_tester.get_expected_values(UpperCamelCase__							)

									self.assertEqual(
									    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

									# Test batched
									UpperCamelCase_: List[Any]   				=    image_processing(UpperCamelCase__  ,      return_tensors='pt'							).pixel_values

									UpperCamelCase_				,UpperCamelCase_: int   				=    self.image_processor_tester.get_expected_values(UpperCamelCase__  ,      batched=UpperCamelCase__							)

									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )  ,      )





							@slow
							def       _a   ( self							):
									UpperCamelCase_: List[Any]   				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'  ,      'r'							) as f:
											UpperCamelCase_: Optional[Any]   				=    json.loads(f.read()							)

									UpperCamelCase_: int   				=    {'image_id': 3_9_7_6_9, 'annotations': target}

									# encode them
									UpperCamelCase_: List[str]   				=    ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'							)
									UpperCamelCase_: Optional[Any]   				=    image_processing(images=UpperCamelCase__  ,      annotations=UpperCamelCase__  ,      return_tensors='pt'							)

									# verify pixel values
									UpperCamelCase_: str   				=    torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
									self.assertEqual(encoding['pixel_values'].shape  ,      UpperCamelCase__							)

									UpperCamelCase_: Optional[int]   				=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  ,      UpperCamelCase__  ,      atol=1e-4							)							)

									# verify area
									UpperCamelCase_: Any   				=    torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']  ,      UpperCamelCase__							)							)
									# verify boxes
									UpperCamelCase_: Dict   				=    torch.Size([6, 4]							)
									self.assertEqual(encoding['labels'][0]['boxes'].shape  ,      UpperCamelCase__							)
									UpperCamelCase_: Optional[int]   				=    torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,      UpperCamelCase__  ,      atol=1e-3							)							)
									# verify image_id
									UpperCamelCase_: int   				=    torch.tensor([3_9_7_6_9]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,      UpperCamelCase__							)							)
									# verify is_crowd
									UpperCamelCase_: str   				=    torch.tensor([0, 0, 0, 0, 0, 0]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,      UpperCamelCase__							)							)
									# verify class_labels
									UpperCamelCase_: Union[str, Any]   				=    torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,      UpperCamelCase__							)							)
									# verify orig_size
									UpperCamelCase_: Union[str, Any]   				=    torch.tensor([4_8_0, 6_4_0]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,      UpperCamelCase__							)							)
									# verify size
									UpperCamelCase_: str   				=    torch.tensor([8_0_0, 1_0_6_6]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,      UpperCamelCase__							)							)





							@slow
							def       _a   ( self							):
									UpperCamelCase_: List[str]   				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'  ,      'r'							) as f:
											UpperCamelCase_: Any   				=    json.loads(f.read()							)

									UpperCamelCase_: Dict   				=    {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

									UpperCamelCase_: Any   				=    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

									# encode them
									UpperCamelCase_: Optional[int]   				=    ConditionalDetrImageProcessor(format='coco_panoptic'							)
									UpperCamelCase_: str   				=    image_processing(images=UpperCamelCase__  ,      annotations=UpperCamelCase__  ,      masks_path=UpperCamelCase__  ,      return_tensors='pt'							)

									# verify pixel values
									UpperCamelCase_: Union[str, Any]   				=    torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
									self.assertEqual(encoding['pixel_values'].shape  ,      UpperCamelCase__							)

									UpperCamelCase_: Union[str, Any]   				=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  ,      UpperCamelCase__  ,      atol=1e-4							)							)

									# verify area
									UpperCamelCase_: Union[str, Any]   				=    torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']  ,      UpperCamelCase__							)							)
									# verify boxes
									UpperCamelCase_: int   				=    torch.Size([6, 4]							)
									self.assertEqual(encoding['labels'][0]['boxes'].shape  ,      UpperCamelCase__							)
									UpperCamelCase_: Dict   				=    torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,      UpperCamelCase__  ,      atol=1e-3							)							)
									# verify image_id
									UpperCamelCase_: List[str]   				=    torch.tensor([3_9_7_6_9]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,      UpperCamelCase__							)							)
									# verify is_crowd
									UpperCamelCase_: str   				=    torch.tensor([0, 0, 0, 0, 0, 0]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,      UpperCamelCase__							)							)
									# verify class_labels
									UpperCamelCase_: Optional[Any]   				=    torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,      UpperCamelCase__							)							)
									# verify masks
									UpperCamelCase_: Any   				=    8_2_2_8_7_3
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()  ,      UpperCamelCase__							)
									# verify orig_size
									UpperCamelCase_: int   				=    torch.tensor([4_8_0, 6_4_0]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,      UpperCamelCase__							)							)
									# verify size
									UpperCamelCase_: Optional[int]   				=    torch.tensor([8_0_0, 1_0_6_6]							)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,      UpperCamelCase__							)							)







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

import numpy as np

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.array:
  UpperCamelCase_: Dict   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Any   				=    '1'
  UpperCamelCase_: Any   				=    'f32le'
  UpperCamelCase_: Union[str, Any]   				=    [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Tuple:
  UpperCamelCase_: Any   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Union[str, Any]   				=    '1'
  if format_for_conversion == "s16le":
    UpperCamelCase_: Optional[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: Any   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  UpperCamelCase_: int   				=    platform.system()
  if system == "Linux":
    UpperCamelCase_: Tuple   				=    'alsa'
    UpperCamelCase_: List[str]   				=    'default'
  elif system == "Darwin":
    UpperCamelCase_: int   				=    'avfoundation'
    UpperCamelCase_: Union[str, Any]   				=    ':0'
  elif system == "Windows":
    UpperCamelCase_: Tuple   				=    'dshow'
    UpperCamelCase_: Dict   				=    'default'

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Any:
  if stream_chunk_s is not None:
    UpperCamelCase_: List[Any]   				=    stream_chunk_s
  else:
    UpperCamelCase_: Dict   				=    chunk_length_s

  UpperCamelCase_: List[str]   				=    ffmpeg_microphone(UpperCAmelCase__     ,   UpperCAmelCase__     ,   format_for_conversion=UpperCAmelCase__  )
  if format_for_conversion == "s16le":
    UpperCamelCase_: Union[str, Any]   				=    np.intaa
    UpperCamelCase_: List[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: str   				=    np.floataa
    UpperCamelCase_: Tuple   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  if stride_length_s is None:
    UpperCamelCase_: int   				=    chunk_length_s / 6
  UpperCamelCase_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  if isinstance(UpperCAmelCase__     ,   (int, float)  ):
    UpperCamelCase_: Union[str, Any]   				=    [stride_length_s, stride_length_s]

  UpperCamelCase_: Any   				=    int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
  UpperCamelCase_: Dict   				=    int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    datetime.datetime.now()
  UpperCamelCase_: Optional[int]   				=    datetime.timedelta(seconds=UpperCAmelCase__  )
  for item in chunk_bytes_iter(UpperCAmelCase__     ,   UpperCAmelCase__     ,   stride=(stride_left, stride_right)     ,   stream=UpperCAmelCase__  ):
    # Put everything back in numpy scale
    UpperCamelCase_: Tuple   				=    np.frombuffer(item['raw']     ,   dtype=UpperCAmelCase__  )
    UpperCamelCase_: Optional[int]   				=    (
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    UpperCamelCase_: int   				=    sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 1_0 * delta:
      # We're late !! SKIP
      continue
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = False  )	->    int:
  UpperCamelCase_: str   				=    b''
  UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    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}'''  )
  UpperCamelCase_: List[str]   				=    0
  for raw in iterator:
    acc += raw
    if stream and len(UpperCAmelCase__  ) < chunk_len:
      UpperCamelCase_: Optional[Any]   				=    (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(UpperCAmelCase__  ) >= chunk_len:
        # We are flushing the accumulator
        UpperCamelCase_: int   				=    (_stride_left, stride_right)
        UpperCamelCase_: Optional[Any]   				=    {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          UpperCamelCase_: Any   				=    False
        yield item
        UpperCamelCase_: Optional[int]   				=    stride_left
        UpperCamelCase_: Optional[Any]   				=    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(UpperCAmelCase__  ) > stride_left:
    UpperCamelCase_: int   				=    {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      UpperCamelCase_: Optional[Any]   				=    False
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Any   				=    2**2_4  # 16Mo
  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdout=subprocess.PIPE     ,   bufsize=UpperCAmelCase__  ) as ffmpeg_process:
      while True:
        UpperCamelCase_: Any   				=    ffmpeg_process.stdout.read(UpperCAmelCase__  )
        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