
"""simple docstring"""

from typing import List

import numpy as np




def     lowercase__						(					lowerCAmelCase				:					Any ) ->      int:






    """simple docstring"""




    UpperCAmelCase									=					{key: len(lowerCAmelCase ) for key, value in gen_kwargs.items() if isinstance(lowerCAmelCase ,				lowerCAmelCase )}
    if len(set(lists_lengths.values() ) ) > 1:
        raise RuntimeError(
            (
                'Sharding is ambiguous for this dataset: '
                + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                + '\n'.join(F"\t- key {key} has length {length}" for key, length in lists_lengths.items() )
                + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
            ) )
    UpperCAmelCase									=					max(lists_lengths.values() ,				default=0 )
    return max(1 ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Optional[int] ) ->      List[range]:






    """simple docstring"""




    UpperCAmelCase									=					[]
    for group_idx in range(lowerCAmelCase ):
        UpperCAmelCase									=					num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        UpperCAmelCase									=					shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        UpperCAmelCase									=					range(lowerCAmelCase ,				start + num_shards_to_add )
        shards_indices_per_group.append(lowerCAmelCase )
    return shards_indices_per_group




def     lowercase__						(					lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Tuple ) ->      List[dict]:






    """simple docstring"""




    UpperCAmelCase									=					_number_of_shards_in_gen_kwargs(lowerCAmelCase )
    if num_shards == 1:
        return [dict(lowerCAmelCase )]
    else:
        UpperCAmelCase									=					_distribute_shards(num_shards=lowerCAmelCase ,				max_num_jobs=lowerCAmelCase )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(lowerCAmelCase ,				lowerCAmelCase )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(lowerCAmelCase ) )
        ]




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      dict:






    """simple docstring"""




    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,				lowerCAmelCase )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					Any ) ->      dict:






    """simple docstring"""




    UpperCAmelCase									=					{len(lowerCAmelCase ) for value in gen_kwargs.values() if isinstance(lowerCAmelCase ,				lowerCAmelCase )}
    UpperCAmelCase									=					{}
    for size in list_sizes:
        UpperCAmelCase									=					list(range(lowerCAmelCase ) )
        rng.shuffle(indices_per_size[size] )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    UpperCAmelCase									=					dict(lowerCAmelCase )
    for key, value in shuffled_kwargs.items():
        if isinstance(lowerCAmelCase ,				lowerCAmelCase ):
            UpperCAmelCase									=					[value[i] for i in indices_per_size[len(lowerCAmelCase )]]
    return shuffled_kwargs





"""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							)							)



