
"""simple docstring"""

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	["pixel_values"]




      def __init__(							self   ,       lowercase_ = True   ,       lowercase_ = 1 / 2_5_5   ,       lowercase_ = True   ,       lowercase_ = 8   ,       **lowercase_   ,       )						->					int:
          super().__init__(**lowercase_							)

          UpperCAmelCase									=					do_rescale
          UpperCAmelCase									=					rescale_factor
          UpperCAmelCase									=					do_pad
          UpperCAmelCase									=					pad_size




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = None   ,       **lowercase_							)						->					int:
          return rescale(lowercase_   ,       scale=lowercase_   ,       data_format=lowercase_   ,       **lowercase_							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = None							)						->					str:
          UpperCAmelCase									=					get_image_size(lowercase_							)
          UpperCAmelCase									=					(old_height // size + 1) * size - old_height
          UpperCAmelCase									=					(old_width // size + 1) * size - old_width

          return pad(lowercase_   ,       ((0, pad_height), (0, pad_width))   ,       mode='symmetric'   ,       data_format=lowercase_							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = ChannelDimension.FIRST   ,       **lowercase_   ,       )						->					int:
          UpperCAmelCase									=					do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase									=					rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase									=					do_pad if do_pad is not None else self.do_pad
          UpperCAmelCase									=					pad_size if pad_size is not None else self.pad_size

          UpperCAmelCase									=					make_list_of_images(lowercase_							)

          if not valid_images(lowercase_							):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.'							)

          if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.'							)

          # All transformations expect numpy arrays.
          UpperCAmelCase									=					[to_numpy_array(lowercase_							) for image in images]

          if do_rescale:
              UpperCAmelCase									=					[self.rescale(image=lowercase_   ,       scale=lowercase_							) for image in images]

          if do_pad:
              UpperCAmelCase									=					[self.pad(lowercase_   ,       size=lowercase_							) for image in images]

          UpperCAmelCase									=					[to_channel_dimension_format(lowercase_   ,       lowercase_							) for image in images]

          UpperCAmelCase									=					{'pixel_values': images}
          return BatchFeature(data=lowercase_   ,       tensor_type=lowercase_							)





"""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							)							)



