



'''simple docstring'''





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      :	Union[str, Any]			,	_a      :	str			,	_a      :	Any=7			,	_a      :	Optional[Any]=3			,	_a      :	Any=30			,	_a      :	str=400			,	_a      :	str=True			,	_a      :	Any=None			,	_a      :	List[str]=True			,	_a      :	Tuple=[0.5, 0.5, 0.5]			,	_a      :	Union[str, Any]=[0.5, 0.5, 0.5]			,	_a      :	Optional[int]=True			,	_a      :	List[Any]=1 / 255			,	_a      :	Any=True			,	)					->					List[str]:
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    __lowerCamelCase       :       Union[str, Any]           = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
    __lowerCamelCase       :       Optional[int]           = parent
    __lowerCamelCase       :       int           = batch_size
    __lowerCamelCase       :       str           = num_channels
    __lowerCamelCase       :       int           = min_resolution
    __lowerCamelCase       :       str           = max_resolution
    __lowerCamelCase       :       Optional[int]           = do_resize
    __lowerCamelCase       :       Tuple           = size
    __lowerCamelCase       :       Optional[Any]           = do_normalize
    __lowerCamelCase       :       int           = image_mean
    __lowerCamelCase       :       Tuple           = image_std
    __lowerCamelCase       :       str           = do_rescale
    __lowerCamelCase       :       Union[str, Any]           = rescale_factor
    __lowerCamelCase       :       List[Any]           = do_pad




  def 						_lowercase     ( self      :	Any   )					->					int:
    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 						_lowercase     ( self      :	List[str]			,	_a      :	int			,	_a      :	Any=False   )					->					Optional[int]:
    if not batched:
      __lowerCamelCase       :       Union[str, Any]           = image_inputs[0]
      if isinstance(_a			,	Image.Image   ):
        __lowerCamelCase       ,__lowerCamelCase       :       Any           = image.size
      else:
        __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = image.shape[1], image.shape[2]
      if w < h:
        __lowerCamelCase       :       int           = int(self.size['shortest_edge'] * h / w   )
        __lowerCamelCase       :       List[str]           = self.size['shortest_edge']
      elif w > h:
        __lowerCamelCase       :       Optional[Any]           = self.size['shortest_edge']
        __lowerCamelCase       :       Optional[int]           = int(self.size['shortest_edge'] * w / h   )
      else:
        __lowerCamelCase       :       Dict           = self.size['shortest_edge']
        __lowerCamelCase       :       Union[str, Any]           = self.size['shortest_edge']

    else:
      __lowerCamelCase       :       Dict           = []
      for image in image_inputs:
        __lowerCamelCase       ,__lowerCamelCase       :       Optional[int]           = self.get_expected_values([image]   )
        expected_values.append((expected_height, expected_width)   )
      __lowerCamelCase       :       Dict           = max(_a			,	key=lambda _a   : item[0]   )[0]
      __lowerCamelCase       :       Any           = max(_a			,	key=lambda _a   : item[1]   )[1]

    return expected_height, expected_width






@require_torch
@require_vision
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=ConditionalDetrImageProcessor if is_vision_available() else None




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Dict:
    __lowerCamelCase       :       List[str]           = ConditionalDetrImageProcessingTester(self   )




  @property
  def 						_lowercase     ( self      :	str   )					->					Dict:
    return self.image_processor_tester.prepare_image_processor_dict()




  def 						_lowercase     ( self      :	Tuple   )					->					str:
    __lowerCamelCase       :       List[str]           = self.image_processing_class(**self.image_processor_dict   )
    self.assertTrue(hasattr(_a			,	'image_mean'   )   )
    self.assertTrue(hasattr(_a			,	'image_std'   )   )
    self.assertTrue(hasattr(_a			,	'do_normalize'   )   )
    self.assertTrue(hasattr(_a			,	'do_resize'   )   )
    self.assertTrue(hasattr(_a			,	'size'   )   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Any           = self.image_processing_class.from_dict(self.image_processor_dict   )
    self.assertEqual(image_processor.size			,	{'shortest_edge': 18, 'longest_edge': 1333}   )
    self.assertEqual(image_processor.do_pad			,	_a   )

    __lowerCamelCase       :       Dict           = self.image_processing_class.from_dict(
        self.image_processor_dict			,	size=42			,	max_size=84			,	pad_and_return_pixel_mask=_a   )
    self.assertEqual(image_processor.size			,	{'shortest_edge': 42, 'longest_edge': 84}   )
    self.assertEqual(image_processor.do_pad			,	_a   )




  def 						_lowercase     ( self      :	Tuple   )					->					List[Any]:
    pass




  def 						_lowercase     ( self      :	str   )					->					Any:
    # Initialize image_processing
    __lowerCamelCase       :       Dict           = self.image_processing_class(**self.image_processor_dict   )
    # create random PIL images
    __lowerCamelCase       :       Optional[Any]           = prepare_image_inputs(self.image_processor_tester			,	equal_resolution=_a   )
    for image in image_inputs:
      self.assertIsInstance(_a			,	Image.Image   )

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

    __lowerCamelCase       ,__lowerCamelCase       :       int           = self.image_processor_tester.get_expected_values(_a   )

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

    # Test batched
    __lowerCamelCase       ,__lowerCamelCase       :       str           = self.image_processor_tester.get_expected_values(_a			,	batched=_a   )

    __lowerCamelCase       :       Any           = image_processing(_a			,	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 						_lowercase     ( self      :	List[str]   )					->					Dict:
    # Initialize image_processing
    __lowerCamelCase       :       str           = self.image_processing_class(**self.image_processor_dict   )
    # create random numpy tensors
    __lowerCamelCase       :       Any           = prepare_image_inputs(self.image_processor_tester			,	equal_resolution=_a			,	numpify=_a   )
    for image in image_inputs:
      self.assertIsInstance(_a			,	np.ndarray   )

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

    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = self.image_processor_tester.get_expected_values(_a   )

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

    # Test batched
    __lowerCamelCase       :       Optional[Any]           = image_processing(_a			,	return_tensors='pt'   ).pixel_values

    __lowerCamelCase       ,__lowerCamelCase       :       str           = self.image_processor_tester.get_expected_values(_a			,	batched=_a   )

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




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    # Initialize image_processing
    __lowerCamelCase       :       Tuple           = self.image_processing_class(**self.image_processor_dict   )
    # create random PyTorch tensors
    __lowerCamelCase       :       Any           = prepare_image_inputs(self.image_processor_tester			,	equal_resolution=_a			,	torchify=_a   )
    for image in image_inputs:
      self.assertIsInstance(_a			,	torch.Tensor   )

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

    __lowerCamelCase       ,__lowerCamelCase       :       Tuple           = self.image_processor_tester.get_expected_values(_a   )

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

    # Test batched
    __lowerCamelCase       :       Dict           = image_processing(_a			,	return_tensors='pt'   ).pixel_values

    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.image_processor_tester.get_expected_values(_a			,	batched=_a   )

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




  @slow
  def 						_lowercase     ( self      :	Optional[Any]   )					->					Optional[Any]:
    # prepare image and target
    __lowerCamelCase       :       Optional[int]           = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
    with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,	'r'   ) as f:
      __lowerCamelCase       :       int           = json.loads(f.read()   )

    __lowerCamelCase       :       Tuple           = {'image_id': 3_9769, 'annotations': target}

    # encode them
    __lowerCamelCase       :       int           = ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'   )
    __lowerCamelCase       :       Dict           = image_processing(images=_a			,	annotations=_a			,	return_tensors='pt'   )

    # verify pixel values
    __lowerCamelCase       :       Dict           = torch.Size([1, 3, 800, 1066]   )
    self.assertEqual(encoding['pixel_values'].shape			,	_a   )

    __lowerCamelCase       :       List[str]           = torch.tensor([0.2796, 0.3138, 0.3481]   )
    self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,	_a			,	atol=1e-4   )   )

    # verify area
    __lowerCamelCase       :       str           = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,	_a   )   )
    # verify boxes
    __lowerCamelCase       :       Optional[int]           = torch.Size([6, 4]   )
    self.assertEqual(encoding['labels'][0]['boxes'].shape			,	_a   )
    __lowerCamelCase       :       List[Any]           = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,	_a			,	atol=1e-3   )   )
    # verify image_id
    __lowerCamelCase       :       Dict           = torch.tensor([3_9769]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,	_a   )   )
    # verify is_crowd
    __lowerCamelCase       :       Tuple           = torch.tensor([0, 0, 0, 0, 0, 0]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,	_a   )   )
    # verify class_labels
    __lowerCamelCase       :       Union[str, Any]           = torch.tensor([75, 75, 63, 65, 17, 17]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,	_a   )   )
    # verify orig_size
    __lowerCamelCase       :       str           = torch.tensor([480, 640]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,	_a   )   )
    # verify size
    __lowerCamelCase       :       int           = torch.tensor([800, 1066]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,	_a   )   )




  @slow
  def 						_lowercase     ( self      :	Optional[int]   )					->					Optional[Any]:
    # prepare image, target and masks_path
    __lowerCamelCase       :       str           = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
    with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'			,	'r'   ) as f:
      __lowerCamelCase       :       int           = json.loads(f.read()   )

    __lowerCamelCase       :       int           = {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

    __lowerCamelCase       :       str           = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

    # encode them
    __lowerCamelCase       :       List[str]           = ConditionalDetrImageProcessor(format='coco_panoptic'   )
    __lowerCamelCase       :       Dict           = image_processing(images=_a			,	annotations=_a			,	masks_path=_a			,	return_tensors='pt'   )

    # verify pixel values
    __lowerCamelCase       :       Any           = torch.Size([1, 3, 800, 1066]   )
    self.assertEqual(encoding['pixel_values'].shape			,	_a   )

    __lowerCamelCase       :       Dict           = torch.tensor([0.2796, 0.3138, 0.3481]   )
    self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,	_a			,	atol=1e-4   )   )

    # verify area
    __lowerCamelCase       :       int           = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,	_a   )   )
    # verify boxes
    __lowerCamelCase       :       List[str]           = torch.Size([6, 4]   )
    self.assertEqual(encoding['labels'][0]['boxes'].shape			,	_a   )
    __lowerCamelCase       :       List[Any]           = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,	_a			,	atol=1e-3   )   )
    # verify image_id
    __lowerCamelCase       :       Optional[Any]           = torch.tensor([3_9769]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,	_a   )   )
    # verify is_crowd
    __lowerCamelCase       :       Dict           = torch.tensor([0, 0, 0, 0, 0, 0]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,	_a   )   )
    # verify class_labels
    __lowerCamelCase       :       int           = torch.tensor([17, 17, 63, 75, 75, 93]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,	_a   )   )
    # verify masks
    __lowerCamelCase       :       Any           = 82_2873
    self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,	_a   )
    # verify orig_size
    __lowerCamelCase       :       Optional[Any]           = torch.tensor([480, 640]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,	_a   )   )
    # verify size
    __lowerCamelCase       :       List[str]           = torch.tensor([800, 1066]   )
    self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,	_a   )   )











'''simple docstring'''





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

import numpy as np

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      np.array:
  __lowerCamelCase       :       Any           = F'{sampling_rate}'
  __lowerCamelCase       :       List[str]           = '1'
  __lowerCamelCase       :       int           = 'f32le'
  __lowerCamelCase       :       Dict           = [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = "f32le"       ,)      ->      Dict:
  __lowerCamelCase       :       Optional[Any]           = F'{sampling_rate}'
  __lowerCamelCase       :       Optional[int]           = '1'
  if format_for_conversion == "s16le":
    __lowerCamelCase       :       List[Any]           = 2
  elif format_for_conversion == "f32le":
    __lowerCamelCase       :       Tuple           = 4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

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

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = "f32le"       ,)      ->      List[str]:
  if stream_chunk_s is not None:
    __lowerCamelCase       :       int           = stream_chunk_s
  else:
    __lowerCamelCase       :       List[Any]           = chunk_length_s

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

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

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

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


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Tuple:
  __lowerCamelCase       :       int           = 2**24  # 16Mo
  try:
    with subprocess.Popen(_lowerCAmelCase       ,stdout=subprocess.PIPE       ,bufsize=_lowerCAmelCase       ) as ffmpeg_process:
      while True:
        __lowerCamelCase       :       Union[str, Any]           = ffmpeg_process.stdout.read(_lowerCAmelCase       )
        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






