




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 YolosImageProcessor




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					int=7       ,					_lowerCAmelCase:					List[str]=3       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					List[Any]=4_00       ,					_lowerCAmelCase:					Optional[int]=True       ,					_lowerCAmelCase:					Optional[Any]=None       ,					_lowerCAmelCase:					List[str]=True       ,					_lowerCAmelCase:					Union[str, Any]=[0.5, 0.5, 0.5]       ,					_lowerCAmelCase:					int=[0.5, 0.5, 0.5]       ,					_lowerCAmelCase:					int=True       ,					_lowerCAmelCase:					str=1 / 2_55       ,					_lowerCAmelCase:					Union[str, Any]=True       ,					):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         lowercase    :List[str]      	=						size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
         lowercase    :Dict      	=						parent
         lowercase    :List[str]      	=						batch_size
         lowercase    :List[Any]      	=						num_channels
         lowercase    :Optional[Any]      	=						min_resolution
         lowercase    :Any      	=						max_resolution
         lowercase    :Optional[int]      	=						do_resize
         lowercase    :Any      	=						size
         lowercase    :Optional[int]      	=						do_normalize
         lowercase    :Optional[Any]      	=						image_mean
         lowercase    :Optional[Any]      	=						image_std
         lowercase    :str      	=						do_rescale
         lowercase    :Union[str, Any]      	=						rescale_factor
         lowercase    :List[Any]      	=						do_pad






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[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 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					Tuple=False				):
         if not batched:
           lowercase    :Optional[Any]      	=						image_inputs[0]
           if isinstance(_lowerCAmelCase       ,					Image.Image				):
             lowercase    :str      	=						image.size
           else:
             lowercase    :int      	=						image.shape[1], image.shape[2]
           if w < h:
             lowercase    :List[str]      	=						int(self.size["shortest_edge"] * h / w				)
             lowercase    :Optional[Any]      	=						self.size["shortest_edge"]
           elif w > h:
             lowercase    :Any      	=						self.size["shortest_edge"]
             lowercase    :Dict      	=						int(self.size["shortest_edge"] * w / h				)
           else:
             lowercase    :str      	=						self.size["shortest_edge"]
             lowercase    :List[str]      	=						self.size["shortest_edge"]

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

         return expected_height, expected_width







@require_torch
@require_vision
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						YolosImageProcessor if is_vision_available() else None






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :List[Any]      	=						YolosImageProcessingTester(self				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         return self.image_processor_tester.prepare_image_processor_dict()






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :str      	=						self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"image_mean"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"image_std"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_normalize"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_resize"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"size"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :List[str]      	=						self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 18, "longest_edge": 13_33}				)
         self.assertEqual(image_processor.do_pad       ,					_lowerCAmelCase				)

         lowercase    :List[Any]      	=						self.image_processing_class.from_dict(
             self.image_processor_dict       ,					size=42       ,					max_size=84       ,					pad_and_return_pixel_mask=_lowerCAmelCase				)
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 42, "longest_edge": 84}				)
         self.assertEqual(image_processor.do_pad       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         # Initialize image_processing
         lowercase    :str      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :Union[str, Any]      	=						prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					Image.Image				)

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

         lowercase    :int      	=						self.image_processor_tester.get_expected_values(_lowerCAmelCase				)

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

         # Test batched
         lowercase    :Union[str, Any]      	=						self.image_processor_tester.get_expected_values(_lowerCAmelCase       ,					batched=_lowerCAmelCase				)

         lowercase    :str      	=						image_processing(_lowerCAmelCase       ,					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 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         # Initialize image_processing
         lowercase    :Optional[Any]      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         lowercase    :Optional[int]      	=						prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCAmelCase       ,					numpify=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					np.ndarray				)

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

         lowercase    :Union[str, Any]      	=						self.image_processor_tester.get_expected_values(_lowerCAmelCase				)

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

         # Test batched
         lowercase    :Tuple      	=						image_processing(_lowerCAmelCase       ,					return_tensors="pt"				).pixel_values

         lowercase    :Union[str, Any]      	=						self.image_processor_tester.get_expected_values(_lowerCAmelCase       ,					batched=_lowerCAmelCase				)

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






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         # Initialize image_processing
         lowercase    :str      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         lowercase    :str      	=						prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCAmelCase       ,					torchify=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					torch.Tensor				)

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

         lowercase    :Union[str, Any]      	=						self.image_processor_tester.get_expected_values(_lowerCAmelCase				)

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

         # Test batched
         lowercase    :Any      	=						image_processing(_lowerCAmelCase       ,					return_tensors="pt"				).pixel_values

         lowercase    :Tuple      	=						self.image_processor_tester.get_expected_values(_lowerCAmelCase       ,					batched=_lowerCAmelCase				)

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         # Initialize image_processings
         lowercase    :List[str]      	=						self.image_processing_class(**self.image_processor_dict				)
         lowercase    :Any      	=						self.image_processing_class(do_resize=_lowerCAmelCase       ,					do_normalize=_lowerCAmelCase       ,					do_rescale=_lowerCAmelCase				)
         # create random PyTorch tensors
         lowercase    :Union[str, Any]      	=						prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCAmelCase       ,					torchify=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					torch.Tensor				)

         # Test whether the method "pad" and calling the image processor return the same tensors
         lowercase    :Tuple      	=						image_processing_a.pad(_lowerCAmelCase       ,					return_tensors="pt"				)
         lowercase    :Dict      	=						image_processing_a(_lowerCAmelCase       ,					return_tensors="pt"				)

         self.assertTrue(
             torch.allclose(encoded_images_with_method["pixel_values"]       ,					encoded_images["pixel_values"]       ,					atol=1e-4				)				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         # prepare image and target
         lowercase    :List[str]      	=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,					"r"				) as f:
           lowercase    :List[Any]      	=						json.loads(f.read()				)

         lowercase    :List[Any]      	=						{"image_id": 3_97_69, "annotations": target}

         # encode them
         lowercase    :Dict      	=						YolosImageProcessor.from_pretrained("hustvl/yolos-small"				)
         lowercase    :Union[str, Any]      	=						image_processing(images=_lowerCAmelCase       ,					annotations=_lowerCAmelCase       ,					return_tensors="pt"				)

         # verify pixel values
         lowercase    :Any      	=						torch.Size([1, 3, 8_00, 10_66]				)
         self.assertEqual(encoding["pixel_values"].shape       ,					_lowerCAmelCase				)

         lowercase    :str      	=						torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,					_lowerCAmelCase       ,					atol=1e-4				)				)

         # verify area
         lowercase    :Optional[int]      	=						torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,					_lowerCAmelCase				)				)
         # verify boxes
         lowercase    :List[str]      	=						torch.Size([6, 4]				)
         self.assertEqual(encoding["labels"][0]["boxes"].shape       ,					_lowerCAmelCase				)
         lowercase    :Tuple      	=						torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,					_lowerCAmelCase       ,					atol=1e-3				)				)
         # verify image_id
         lowercase    :int      	=						torch.tensor([3_97_69]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,					_lowerCAmelCase				)				)
         # verify is_crowd
         lowercase    :List[str]      	=						torch.tensor([0, 0, 0, 0, 0, 0]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,					_lowerCAmelCase				)				)
         # verify class_labels
         lowercase    :Optional[int]      	=						torch.tensor([75, 75, 63, 65, 17, 17]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,					_lowerCAmelCase				)				)
         # verify orig_size
         lowercase    :int      	=						torch.tensor([4_80, 6_40]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,					_lowerCAmelCase				)				)
         # verify size
         lowercase    :List[Any]      	=						torch.tensor([8_00, 10_66]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,					_lowerCAmelCase				)				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         # prepare image, target and masks_path
         lowercase    :Any      	=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,					"r"				) as f:
           lowercase    :List[Any]      	=						json.loads(f.read()				)

         lowercase    :List[str]      	=						{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

         lowercase    :int      	=						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"				)

         # encode them
         lowercase    :Dict      	=						YolosImageProcessor(format="coco_panoptic"				)
         lowercase    :int      	=						image_processing(images=_lowerCAmelCase       ,					annotations=_lowerCAmelCase       ,					masks_path=_lowerCAmelCase       ,					return_tensors="pt"				)

         # verify pixel values
         lowercase    :Optional[Any]      	=						torch.Size([1, 3, 8_00, 10_66]				)
         self.assertEqual(encoding["pixel_values"].shape       ,					_lowerCAmelCase				)

         lowercase    :str      	=						torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,					_lowerCAmelCase       ,					atol=1e-4				)				)

         # verify area
         lowercase    :Optional[int]      	=						torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,					_lowerCAmelCase				)				)
         # verify boxes
         lowercase    :str      	=						torch.Size([6, 4]				)
         self.assertEqual(encoding["labels"][0]["boxes"].shape       ,					_lowerCAmelCase				)
         lowercase    :str      	=						torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,					_lowerCAmelCase       ,					atol=1e-3				)				)
         # verify image_id
         lowercase    :str      	=						torch.tensor([3_97_69]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,					_lowerCAmelCase				)				)
         # verify is_crowd
         lowercase    :Any      	=						torch.tensor([0, 0, 0, 0, 0, 0]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,					_lowerCAmelCase				)				)
         # verify class_labels
         lowercase    :Any      	=						torch.tensor([17, 17, 63, 75, 75, 93]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,					_lowerCAmelCase				)				)
         # verify masks
         lowercase    :List[str]      	=						82_28_73
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,					_lowerCAmelCase				)
         # verify orig_size
         lowercase    :Tuple      	=						torch.tensor([4_80, 6_40]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,					_lowerCAmelCase				)				)
         # verify size
         lowercase    :List[str]      	=						torch.tensor([8_00, 10_66]				)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,					_lowerCAmelCase				)				)









import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset

_UpperCAmelCase	:						List[str]			=     random.Random()



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=1.0,			lowerCamelCase=None,			lowerCamelCase=None   ):
  if rng is None:
    lowercase    :Union[str, Any]      	=						global_rng

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

  return values




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Union[str, Any]=7       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					List[str]=20_00       ,					_lowerCAmelCase:					Dict=20_48       ,					_lowerCAmelCase:					Any=1_28       ,					_lowerCAmelCase:					Any=1       ,					_lowerCAmelCase:					List[Any]=5_12       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					List[Any]=4_41_00       ,					):
         lowercase    :Any      	=						parent
         lowercase    :Any      	=						batch_size
         lowercase    :Optional[int]      	=						min_seq_length
         lowercase    :Optional[Any]      	=						max_seq_length
         lowercase    :Dict      	=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase    :List[str]      	=						spectrogram_length
         lowercase    :int      	=						feature_size
         lowercase    :Union[str, Any]      	=						num_audio_channels
         lowercase    :Optional[int]      	=						hop_length
         lowercase    :str      	=						chunk_length
         lowercase    :List[str]      	=						sampling_rate






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					int=False       ,					_lowerCAmelCase:					int=False				):
         def _flatten(_lowerCAmelCase:					Optional[Any]				):
           return list(itertools.chain(*_lowerCAmelCase				)				)

         if equal_length:
           lowercase    :Any      	=						[floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
         else:
           # make sure that inputs increase in size
           lowercase    :Tuple      	=						[
               floats_list((x, self.feature_size)				)
               for x in range(self.min_seq_length       ,					self.max_seq_length       ,					self.seq_length_diff				)
           ]
         if numpify:
           lowercase    :Optional[int]      	=						[np.asarray(_lowerCAmelCase				) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						TvltFeatureExtractor






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Dict      	=						TvltFeatureExtractionTester(self				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"spectrogram_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"feature_size"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"num_audio_channels"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"hop_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"chunk_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"sampling_rate"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Any      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Tuple      	=						feat_extract_first.save_pretrained(_lowerCAmelCase				)[0]
           check_json_file_has_correct_format(_lowerCAmelCase				)
           lowercase    :Tuple      	=						self.feature_extraction_class.from_pretrained(_lowerCAmelCase				)

         lowercase    :Optional[Any]      	=						feat_extract_first.to_dict()
         lowercase    :List[str]      	=						feat_extract_second.to_dict()
         lowercase    :Any      	=						dict_first.pop("mel_filters"				)
         lowercase    :Any      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Union[str, Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Any      	=						os.path.join(_lowerCAmelCase       ,					"feat_extract.json"				)
           feat_extract_first.to_json_file(_lowerCAmelCase				)
           lowercase    :List[Any]      	=						self.feature_extraction_class.from_json_file(_lowerCAmelCase				)

         lowercase    :Tuple      	=						feat_extract_first.to_dict()
         lowercase    :int      	=						feat_extract_second.to_dict()
         lowercase    :Tuple      	=						dict_first.pop("mel_filters"				)
         lowercase    :Tuple      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize feature_extractor
         lowercase    :int      	=						self.feature_extraction_class(**self.feat_extract_dict				)

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

         # Test not batched input
         lowercase    :Any      	=						feature_extractor(np_speech_inputs[0]       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test batched
         lowercase    :Optional[int]      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test audio masking
         lowercase    :Dict      	=						feature_extractor(
             _lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00       ,					mask_audio=_lowerCAmelCase				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test 2-D numpy arrays are batched.
         lowercase    :Optional[int]      	=						[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
         lowercase    :Union[str, Any]      	=						np.asarray(_lowerCAmelCase				)
         lowercase    :Dict      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values
         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :str      	=						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,					"clean"       ,					split="validation"				)
         # automatic decoding with librispeech
         lowercase    :Any      	=						ds.sort("id"				).select(range(_lowerCAmelCase				)				)[:num_samples]["audio"]

         return [x["array"] for x in speech_samples]






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Optional[int]      	=						self._load_datasamples(1				)
         lowercase    :Union[str, Any]      	=						TvltFeatureExtractor()
         lowercase    :str      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="pt"				).audio_values

         self.assertEquals(audio_values.shape       ,					(1, 1, 1_92, 1_28)				)

         lowercase    :Optional[int]      	=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,					_lowerCAmelCase       ,					atol=1e-4				)				)



