



'''simple docstring'''





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_UpperCamelCase											=		logging.get_logger(__name__)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =["""audio_values""", """audio_mask"""]




  def __init__(					self :      Dict     ,      _a :      List[Any]=2048     ,      _a :      List[str]=1     ,      _a :      Any=[16, 16]     ,      _a :      str=128     ,      _a :      Union[str, Any]=4_4100     ,      _a :      int=86     ,      _a :      List[str]=2048     ,      _a :      Optional[Any]=0.0     ,      **_a :      Optional[int]     ,      )   ->   Dict:
    super().__init__(
        feature_size=_a     ,      sampling_rate=_a     ,      padding_value=_a     ,      **_a     ,      )

    __lowerCamelCase			:			Dict       			=					spectrogram_length
    __lowerCamelCase			:			str       			=					num_channels
    __lowerCamelCase			:			Union[str, Any]       			=					patch_size
    __lowerCamelCase			:			Tuple       			=					feature_size // self.patch_size[1]
    __lowerCamelCase			:			Optional[int]       			=					n_fft
    __lowerCamelCase			:			int       			=					sampling_rate // hop_length_to_sampling_rate
    __lowerCamelCase			:			Tuple       			=					sampling_rate
    __lowerCamelCase			:			Tuple       			=					padding_value
    __lowerCamelCase			:			Optional[Any]       			=					mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2     ,      num_mel_filters=_a     ,      min_frequency=0.0     ,      max_frequency=22050.0     ,      sampling_rate=_a     ,      norm='slaney'     ,      mel_scale='slaney'     ,      ).T




  def 		_lowercase	(					self :      List[Any]     ,      _a :      np.array  )   ->   np.ndarray:
    __lowerCamelCase			:			Optional[Any]       			=					spectrogram(
        _a     ,      window_function(self.n_fft     ,      'hann'  )     ,      frame_length=self.n_fft     ,      hop_length=self.hop_length     ,      power=2.0     ,      mel_filters=self.mel_filters.T     ,      log_mel='dB'     ,      db_range=80.0     ,      )
    __lowerCamelCase			:			Tuple       			=					log_spec[:, :-1]
    __lowerCamelCase			:			List[str]       			=					log_spec - 20.0
    __lowerCamelCase			:			int       			=					np.clip(log_spec / 40.0     ,      -2.0     ,      0.0  ) + 1.0
    return log_spec




  def __call__(					self :      str     ,      _a :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,      _a :      Optional[Union[str, TensorType]] = None     ,      _a :      Optional[bool] = True     ,      _a :      Optional[int] = None     ,      _a :      bool = False     ,      _a :      bool = False     ,      **_a :      str     ,      )   ->   BatchFeature:

    if sampling_rate is not None:
      if sampling_rate != self.sampling_rate:
        raise ValueError(
            'This feature extractor is set to support sampling rate'
            f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
            f' 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.'  )

    __lowerCamelCase			:			List[str]       			=					isinstance(_a     ,      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}'  )
    __lowerCamelCase			:			List[str]       			=					is_batched_numpy or (
        isinstance(_a     ,      (list, tuple)  ) and (isinstance(raw_speech[0]     ,      (np.ndarray, tuple, list)  ))
    )
    if is_batched:
      __lowerCamelCase			:			Dict       			=					[np.asarray([speech]     ,      dtype=np.floataa  ).T for speech in raw_speech]
    elif not is_batched and not isinstance(_a     ,      np.ndarray  ):
      __lowerCamelCase			:			int       			=					np.asarray(_a     ,      dtype=np.floataa  )
    elif isinstance(_a     ,      np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
      __lowerCamelCase			:			Any       			=					raw_speech.astype(np.floataa  )
    # always return batch
    if not is_batched:
      __lowerCamelCase			:			int       			=					[np.asarray([raw_speech]  ).T]

    # Convert audio signals to log mel spectrograms, truncate by time axis
    __lowerCamelCase			:			List[str]       			=					[
        self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
    ]
    if isinstance(audio_features[0]     ,      _a  ):
      __lowerCamelCase			:			Any       			=					[np.asarray(_a     ,      dtype=np.floataa  ) for feature in audio_features]

    # Create audio attention mask
    __lowerCamelCase			:			Union[str, Any]       			=					max(
        [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
    if return_attention_mask:
      __lowerCamelCase			:			Any       			=					[
          (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
          + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
          for feature in audio_features
      ]
      __lowerCamelCase			:			Optional[int]       			=					np.array(_a  ).astype(np.floataa  )

    # convert into correct format for padding
    __lowerCamelCase			:			str       			=					max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
    __lowerCamelCase			:			Union[str, Any]       			=					np.ones([len(_a  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
    __lowerCamelCase			:			Optional[Any]       			=					padded_audio_features * self.padding_value
    for i in range(len(_a  )  ):
      __lowerCamelCase			:			List[str]       			=					audio_features[i]
      __lowerCamelCase			:			Any       			=					feature

    # return as BatchFeature
    if return_attention_mask:
      __lowerCamelCase			:			List[Any]       			=					{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
    else:
      __lowerCamelCase			:			Optional[Any]       			=					{'audio_values': padded_audio_features}

    __lowerCamelCase			:			Optional[Any]       			=					BatchFeature(data=_a     ,      tensor_type=_a  )
    return encoded_inputs











'''simple docstring'''





import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image
else:






   class    lowerCamelCase_       :
     """simple docstring"""




     @staticmethod
     def 		_lowercase	(					*_a :      Optional[Any]     ,      **_a :      Optional[Any]  )   ->   Tuple:
       pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  a_				   =MODEL_FOR_OBJECT_DETECTION_MAPPING




  def 		_lowercase	(					self :      List[Any]     ,      _a :      int     ,      _a :      str     ,      _a :      Tuple  )   ->   List[Any]:
    __lowerCamelCase			:			List[str]       			=					ObjectDetectionPipeline(model=_a     ,      image_processor=_a  )
    return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      int     ,      _a :      Optional[Any]  )   ->   Any:
    __lowerCamelCase			:			int       			=					object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'     ,      threshold=0.0  )

    self.assertGreater(len(_a  )     ,      0  )
    for detected_object in outputs:
      self.assertEqual(
          _a     ,      {
              'score': ANY(_a  ),
              'label': ANY(_a  ),
              'box': {'xmin': ANY(_a  ), 'ymin': ANY(_a  ), 'xmax': ANY(_a  ), 'ymax': ANY(_a  )},
          }     ,      )

    import datasets

    __lowerCamelCase			:			Optional[int]       			=					datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,      'image'     ,      split='test'  )

    __lowerCamelCase			:			List[str]       			=					[
        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  ),
        'http://images.cocodataset.org/val2017/000000039769.jpg',
        # RGBA
        dataset[0]['file'],
        # LA
        dataset[1]['file'],
        # L
        dataset[2]['file'],
    ]
    __lowerCamelCase			:			Dict       			=					object_detector(_a     ,      threshold=0.0  )

    self.assertEqual(len(_a  )     ,      len(_a  )  )
    for outputs in batch_outputs:
      self.assertGreater(len(_a  )     ,      0  )
      for detected_object in outputs:
        self.assertEqual(
            _a     ,      {
                'score': ANY(_a  ),
                'label': ANY(_a  ),
                'box': {'xmin': ANY(_a  ), 'ymin': ANY(_a  ), 'xmax': ANY(_a  ), 'ymax': ANY(_a  )},
            }     ,      )




  @require_tf
  @unittest.skip('Object detection not implemented in TF'  )
  def 		_lowercase	(					self :      Optional[Any]  )   ->   Dict:
    pass




  @require_torch
  def 		_lowercase	(					self :      int  )   ->   Optional[int]:
    __lowerCamelCase			:			Dict       			=					'hf-internal-testing/tiny-detr-mobilenetsv3'

    __lowerCamelCase			:			str       			=					AutoModelForObjectDetection.from_pretrained(_a  )
    __lowerCamelCase			:			Optional[Any]       			=					AutoFeatureExtractor.from_pretrained(_a  )
    __lowerCamelCase			:			str       			=					ObjectDetectionPipeline(model=_a     ,      feature_extractor=_a  )

    __lowerCamelCase			:			Tuple       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     ,      threshold=0.0  )

    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
        ]     ,      )

    __lowerCamelCase			:			Optional[int]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]     ,      threshold=0.0     ,      )

    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            ],
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Dict:
    __lowerCamelCase			:			Tuple       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			Optional[int]       			=					AutoModelForObjectDetection.from_pretrained(_a  )
    __lowerCamelCase			:			List[str]       			=					AutoFeatureExtractor.from_pretrained(_a  )
    __lowerCamelCase			:			str       			=					ObjectDetectionPipeline(model=_a     ,      feature_extractor=_a  )

    __lowerCamelCase			:			int       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )

    __lowerCamelCase			:			List[Any]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      Tuple  )   ->   Optional[Any]:
    __lowerCamelCase			:			Any       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			List[str]       			=					pipeline('object-detection'     ,      model=_a  )

    __lowerCamelCase			:			int       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )

    __lowerCamelCase			:			List[Any]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      int  )   ->   Tuple:
    __lowerCamelCase			:			Optional[Any]       			=					0.9985
    __lowerCamelCase			:			Optional[Any]       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			Optional[int]       			=					pipeline('object-detection'     ,      model=_a  )

    __lowerCamelCase			:			Dict       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     ,      threshold=_a  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )




  @require_torch
  @require_pytesseract
  @slow
  def 		_lowercase	(					self :      int  )   ->   Any:
    __lowerCamelCase			:			Optional[int]       			=					'Narsil/layoutlmv3-finetuned-funsd'
    __lowerCamelCase			:			Tuple       			=					0.9993

    __lowerCamelCase			:			List[str]       			=					pipeline('object-detection'     ,      model=_a     ,      threshold=_a  )

    __lowerCamelCase			:			Tuple       			=					object_detector(
        'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
        ]     ,      )






