
"""simple docstring"""

import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
      from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
      from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
   from PIL import Image




def     lowercase__						(					lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					List[str]="shi-labs/oneformer_demo" ) ->      Union[str, Any]:






    """simple docstring"""




    with open(hf_hub_download(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ,				'r' ) as f:
        UpperCAmelCase									=					json.load(lowerCAmelCase )
    UpperCAmelCase									=					{}
    UpperCAmelCase									=					[]
    UpperCAmelCase									=					[]
    for key, info in class_info.items():
        UpperCAmelCase									=					info['name']
        class_names.append(info['name'] )
        if info["isthing"]:
            thing_ids.append(int(lowerCAmelCase ) )
    UpperCAmelCase									=					thing_ids
    UpperCAmelCase									=					class_names
    return metadata
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=7   ,       lowercase_=3   ,       lowercase_=3_0   ,       lowercase_=4_0_0   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=1_0   ,       lowercase_=False   ,       lowercase_=2_5_5   ,       lowercase_="shi-labs/oneformer_demo"   ,       lowercase_="ade20k_panoptic.json"   ,       lowercase_=1_0   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					min_resolution
          UpperCAmelCase									=					max_resolution
          UpperCAmelCase									=					do_resize
          UpperCAmelCase									=					{'shortest_edge': 3_2, 'longest_edge': 1_3_3_3} if size is None else size
          UpperCAmelCase									=					do_normalize
          UpperCAmelCase									=					image_mean
          UpperCAmelCase									=					image_std
          UpperCAmelCase									=					class_info_file
          UpperCAmelCase									=					prepare_metadata(lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					num_text
          UpperCAmelCase									=					repo_path

          # for the post_process_functions
          UpperCAmelCase									=					2
          UpperCAmelCase									=					1_0
          UpperCAmelCase									=					1_0
          UpperCAmelCase									=					3
          UpperCAmelCase									=					4
          UpperCAmelCase									=					num_labels
          UpperCAmelCase									=					do_reduce_labels
          UpperCAmelCase									=					ignore_index




      def     a_      (							self							)						->					Any:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "num_labels": self.num_labels,
              "do_reduce_labels": self.do_reduce_labels,
              "ignore_index": self.ignore_index,
              "class_info_file": self.class_info_file,
              "metadata": self.metadata,
              "num_text": self.num_text,
          }




      def     a_      (							self   ,       lowercase_   ,       lowercase_=False							)						->					Dict:
          if not batched:
              UpperCAmelCase									=					image_inputs[0]
              if isinstance(lowercase_   ,       Image.Image							):
                  UpperCAmelCase			,							UpperCAmelCase									=					image.size
              else:
                  UpperCAmelCase			,							UpperCAmelCase									=					image.shape[1], image.shape[2]
              if w < h:
                  UpperCAmelCase									=					int(self.size['shortest_edge'] * h / w							)
                  UpperCAmelCase									=					self.size['shortest_edge']
              elif w > h:
                  UpperCAmelCase									=					self.size['shortest_edge']
                  UpperCAmelCase									=					int(self.size['shortest_edge'] * w / h							)
              else:
                  UpperCAmelCase									=					self.size['shortest_edge']
                  UpperCAmelCase									=					self.size['shortest_edge']

          else:
              UpperCAmelCase									=					[]
              for image in image_inputs:
                  UpperCAmelCase			,							UpperCAmelCase									=					self.get_expected_values([image]							)
                  expected_values.append((expected_height, expected_width)							)
              UpperCAmelCase									=					max(lowercase_   ,       key=lambda lowercase_							: item[0]							)[0]
              UpperCAmelCase									=					max(lowercase_   ,       key=lambda lowercase_							: item[1]							)[1]

          return expected_height, expected_width




      def     a_      (							self							)						->					List[Any]:
          return OneFormerForUniversalSegmentationOutput(
              # +1 for null class
              class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)							)   ,       masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)							)   ,       )

@require_torch
@require_vision
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Any							      =	OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	image_processing_class




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					OneFormerImageProcessorTester(self							)




      @property
      def     a_      (							self							)						->					Tuple:
          return self.image_processing_tester.prepare_image_processor_dict()




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(lowercase_   ,       'image_mean'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'image_std'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_normalize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_resize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'size'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'ignore_index'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'class_info_file'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'num_text'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'repo_path'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'metadata'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_reduce_labels'							)							)




      def     a_      (							self							)						->					Dict:
          pass




      def     a_      (							self							)						->					List[Any]:
          # Initialize image_processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       Image.Image							)

          # Test not batched input
          UpperCAmelCase									=					image_processor(image_inputs[0]   ,       ['semantic']   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape   ,       (
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,       )




      def     a_      (							self							)						->					Dict:
          # Initialize image_processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_   ,       numpify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       np.ndarray							)

          # Test not batched input
          UpperCAmelCase									=					image_processor(image_inputs[0]   ,       ['semantic']   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape   ,       (
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,       )




      def     a_      (							self							)						->					Union[str, Any]:
          # Initialize image_processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_   ,       torchify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       torch.Tensor							)

          # Test not batched input
          UpperCAmelCase									=					image_processor(image_inputs[0]   ,       ['semantic']   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape   ,       (
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,       )




      def     a_      (							self   ,       lowercase_=False   ,       lowercase_=False   ,       lowercase_="np"							)						->					int:
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # prepare image and target
          UpperCAmelCase									=					self.image_processing_tester.num_labels
          UpperCAmelCase									=					None
          UpperCAmelCase									=					None
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_							)
          if with_segmentation_maps:
              UpperCAmelCase									=					num_labels
              if is_instance_map:
                  UpperCAmelCase									=					list(range(lowercase_							)							) * 2
                  UpperCAmelCase									=					dict(enumerate(lowercase_							)							)
              UpperCAmelCase									=					[
                  np.random.randint(0   ,       high * 2   ,       (img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
              ]
              if segmentation_type == "pil":
                  UpperCAmelCase									=					[Image.fromarray(lowercase_							) for annotation in annotations]

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       lowercase_   ,       return_tensors='pt'   ,       instance_id_to_semantic_id=lowercase_   ,       pad_and_return_pixel_mask=lowercase_   ,       )

          return inputs




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




      def     a_      (							self							)						->					int:
          def common(lowercase_=False   ,       lowercase_=None							):
              UpperCAmelCase									=					self.comm_get_image_processor_inputs(
                  with_segmentation_maps=lowercase_   ,       is_instance_map=lowercase_   ,       segmentation_type=lowercase_							)

              UpperCAmelCase									=					inputs['mask_labels']
              UpperCAmelCase									=					inputs['class_labels']
              UpperCAmelCase									=					inputs['pixel_values']
              UpperCAmelCase									=					inputs['text_inputs']

              # check the batch_size
              for mask_label, class_label, text_input in zip(lowercase_   ,       lowercase_   ,       lowercase_							):
                  self.assertEqual(mask_label.shape[0]   ,       class_label.shape[0]							)
                  # this ensure padding has happened
                  self.assertEqual(mask_label.shape[1:]   ,       pixel_values.shape[2:]							)
                  self.assertEqual(len(lowercase_							)   ,       self.image_processing_tester.num_text							)

          common()
          common(is_instance_map=lowercase_							)
          common(is_instance_map=lowercase_   ,       segmentation_type='pil'							)
          common(is_instance_map=lowercase_   ,       segmentation_type='pil'							)




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					np.zeros((2_0, 5_0)							)
          UpperCAmelCase									=					1
          UpperCAmelCase									=					1
          UpperCAmelCase									=					1

          UpperCAmelCase									=					binary_mask_to_rle(lowercase_							)
          self.assertEqual(len(lowercase_							)   ,       4							)
          self.assertEqual(rle[0]   ,       2_1							)
          self.assertEqual(rle[1]   ,       4_5							)




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes   ,       max_seq_length=7_7   ,       task_seq_length=7_7   ,       class_info_file='ade20k_panoptic.json'   ,       num_text=self.image_processing_tester.num_text   ,       repo_path='shi-labs/oneformer_demo'   ,       )
          UpperCAmelCase									=					self.image_processing_tester.get_fake_oneformer_outputs()

          UpperCAmelCase									=					fature_extractor.post_process_semantic_segmentation(lowercase_							)

          self.assertEqual(len(lowercase_							)   ,       self.image_processing_tester.batch_size							)
          self.assertEqual(
              segmentation[0].shape   ,       (
                  self.image_processing_tester.height,
                  self.image_processing_tester.width,
              )   ,       )

          UpperCAmelCase									=					[(1, 4) for i in range(self.image_processing_tester.batch_size							)]
          UpperCAmelCase									=					fature_extractor.post_process_semantic_segmentation(lowercase_   ,       target_sizes=lowercase_							)

          self.assertEqual(segmentation[0].shape   ,       target_sizes[0]							)




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes   ,       max_seq_length=7_7   ,       task_seq_length=7_7   ,       class_info_file='ade20k_panoptic.json'   ,       num_text=self.image_processing_tester.num_text   ,       repo_path='shi-labs/oneformer_demo'   ,       )
          UpperCAmelCase									=					self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase									=					image_processor.post_process_instance_segmentation(lowercase_   ,       threshold=0							)

          self.assertTrue(len(lowercase_							) == self.image_processing_tester.batch_size							)
          for el in segmentation:
              self.assertTrue('segmentation' in el							)
              self.assertTrue('segments_info' in el							)
              self.assertEqual(type(el['segments_info']							)   ,       lowercase_							)
              self.assertEqual(
                  el['segmentation'].shape   ,       (self.image_processing_tester.height, self.image_processing_tester.width)							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes   ,       max_seq_length=7_7   ,       task_seq_length=7_7   ,       class_info_file='ade20k_panoptic.json'   ,       num_text=self.image_processing_tester.num_text   ,       repo_path='shi-labs/oneformer_demo'   ,       )
          UpperCAmelCase									=					self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase									=					image_processor.post_process_panoptic_segmentation(lowercase_   ,       threshold=0							)

          self.assertTrue(len(lowercase_							) == self.image_processing_tester.batch_size							)
          for el in segmentation:
              self.assertTrue('segmentation' in el							)
              self.assertTrue('segments_info' in el							)
              self.assertEqual(type(el['segments_info']							)   ,       lowercase_							)
              self.assertEqual(
                  el['segmentation'].shape   ,       (self.image_processing_tester.height, self.image_processing_tester.width)							)





"""simple docstring"""

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

SCREAMING_SNAKE_CASE_    					=						random.Random()




def     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Tuple=1.0 ,				lowerCAmelCase				:					Tuple=None ,				lowerCAmelCase				:					List[str]=None ) ->      Dict:






    """simple docstring"""




    if rng is None:
        UpperCAmelCase									=					global_rng

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

    return values
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=7   ,       lowercase_=4_0_0   ,       lowercase_=2_0_0_0   ,       lowercase_=2_0_4_8   ,       lowercase_=1_2_8   ,       lowercase_=1   ,       lowercase_=5_1_2   ,       lowercase_=3_0   ,       lowercase_=4_4_1_0_0   ,       )						->					str:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					min_seq_length
          UpperCAmelCase									=					max_seq_length
          UpperCAmelCase									=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          UpperCAmelCase									=					spectrogram_length
          UpperCAmelCase									=					feature_size
          UpperCAmelCase									=					num_audio_channels
          UpperCAmelCase									=					hop_length
          UpperCAmelCase									=					chunk_length
          UpperCAmelCase									=					sampling_rate




      def     a_      (							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     a_      (							self   ,       lowercase_=False   ,       lowercase_=False							)						->					Optional[Any]:
          def _flatten(lowercase_							):
              return list(itertools.chain(*lowercase_							)							)

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

@require_torch
@require_torchaudio
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	TvltFeatureExtractor




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					TvltFeatureExtractionTester(self							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)
          self.assertTrue(hasattr(lowercase_   ,       'spectrogram_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'feature_size'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'num_audio_channels'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'hop_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'chunk_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'sampling_rate'							)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase									=					feat_extract_first.save_pretrained(lowercase_							)[0]
              check_json_file_has_correct_format(lowercase_							)
              UpperCAmelCase									=					self.feature_extraction_class.from_pretrained(lowercase_							)

          UpperCAmelCase									=					feat_extract_first.to_dict()
          UpperCAmelCase									=					feat_extract_second.to_dict()
          UpperCAmelCase									=					dict_first.pop('mel_filters'							)
          UpperCAmelCase									=					dict_second.pop('mel_filters'							)
          self.assertTrue(np.allclose(lowercase_   ,       lowercase_							)							)
          self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase									=					os.path.join(lowercase_   ,       'feat_extract.json'							)
              feat_extract_first.to_json_file(lowercase_							)
              UpperCAmelCase									=					self.feature_extraction_class.from_json_file(lowercase_							)

          UpperCAmelCase									=					feat_extract_first.to_dict()
          UpperCAmelCase									=					feat_extract_second.to_dict()
          UpperCAmelCase									=					dict_first.pop('mel_filters'							)
          UpperCAmelCase									=					dict_second.pop('mel_filters'							)
          self.assertTrue(np.allclose(lowercase_   ,       lowercase_							)							)
          self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          # Initialize feature_extractor
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

          # create three inputs of length 800, 1000, and 1200
          UpperCAmelCase									=					[floats_list((1, x)							)[0] for x in range(8_0_0   ,       1_4_0_0   ,       2_0_0							)]
          UpperCAmelCase									=					[np.asarray(lowercase_							) for speech_input in speech_inputs]

          # Test not batched input
          UpperCAmelCase									=					feature_extractor(np_speech_inputs[0]   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).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
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).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
          UpperCAmelCase									=					feature_extractor(
              lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0   ,       mask_audio=lowercase_							).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.
          UpperCAmelCase									=					[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
          UpperCAmelCase									=					np.asarray(lowercase_							)
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).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     a_      (							self   ,       lowercase_							)						->					Optional[Any]:
          UpperCAmelCase									=					load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,       'clean'   ,       split='validation'							)
          # automatic decoding with librispeech
          UpperCAmelCase									=					ds.sort('id'							).select(range(lowercase_							)							)[:num_samples]['audio']

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




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					self._load_datasamples(1							)
          UpperCAmelCase									=					TvltFeatureExtractor()
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='pt'							).audio_values

          self.assertEquals(audio_values.shape   ,       (1, 1, 1_9_2, 1_2_8)							)

          UpperCAmelCase									=					torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]							)
          self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,       lowercase_   ,       atol=1E-4							)							)



