

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

__A      		=     random.Random()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=1.0			,	_UpperCamelCase=None			,	_UpperCamelCase=None					)							->     Optional[int]:



  """simple docstring"""






  if rng is None:
    _snake_case						=							global_rng

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

  return values



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    List[Any]      , A__				:    List[Any]      , A__				:    int=7      , A__				:    Tuple=400      , A__				:    int=2000      , A__				:    Any=2048      , A__				:    List[Any]=128      , A__				:    Optional[int]=1      , A__				:    Optional[Any]=512      , A__				:    Any=30      , A__				:    Any=44100      , )   ->					int:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							min_seq_length
        _snake_case						=							max_seq_length
        _snake_case						=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _snake_case						=							spectrogram_length
        _snake_case						=							feature_size
        _snake_case						=							num_audio_channels
        _snake_case						=							hop_length
        _snake_case						=							chunk_length
        _snake_case						=							sampling_rate



      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
        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 						UpperCamelCase_		(      self				:    Any      , A__				:    Any=False      , A__				:    List[str]=False		)   ->					Tuple:
        def _flatten(A__				:    List[str]		):
          return list(itertools.chain(*A__		)		)

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



@require_torch
@require_torchaudio
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[int]										=    TvltFeatureExtractor



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[str]:
        _snake_case						=							TvltFeatureExtractionTester(self		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)
        self.assertTrue(hasattr(A__      , '''spectrogram_length'''		)		)
        self.assertTrue(hasattr(A__      , '''feature_size'''		)		)
        self.assertTrue(hasattr(A__      , '''num_audio_channels'''		)		)
        self.assertTrue(hasattr(A__      , '''hop_length'''		)		)
        self.assertTrue(hasattr(A__      , '''chunk_length'''		)		)
        self.assertTrue(hasattr(A__      , '''sampling_rate'''		)		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

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

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



      def 						UpperCamelCase_		(      self				:    int		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

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

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



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Any:
        # Initialize feature_extractor
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        # create three inputs of length 800, 1000, and 1200
        _snake_case						=							[floats_list((1, x)		)[0] for x in range(800      , 1400      , 200		)]
        _snake_case						=							[np.asarray(A__		) for speech_input in speech_inputs]

        # Test not batched input
        _snake_case						=							feature_extractor(np_speech_inputs[0]      , return_tensors='''np'''      , sampling_rate=44100		).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
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).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
        _snake_case						=							feature_extractor(
            A__      , return_tensors='''np'''      , sampling_rate=44100      , mask_audio=A__		).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.
        _snake_case						=							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
        _snake_case						=							np.asarray(A__		)
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).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 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Any		)   ->					Optional[int]:
        _snake_case						=							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      , '''clean'''      , split='''validation'''		)
        # automatic decoding with librispeech
        _snake_case						=							ds.sort('''id'''		).select(range(A__		)		)[:num_samples]['''audio''']

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







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        _snake_case						=							self._load_datasamples(1		)
        _snake_case						=							TvltFeatureExtractor()
        _snake_case						=							feature_extractor(A__      , return_tensors='''pt'''		).audio_values

        self.assertEquals(audio_values.shape      , (1, 1, 192, 128)		)

        _snake_case						=							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      , A__      , atol=1e-4		)		)




__A      		=     {
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}

