



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

__lowerCamelCase         =			random.Random()







def    _snake_case (       __snake_case					,  __snake_case=1.0					,  __snake_case=None					,  __snake_case=None    )     ->     Optional[Any]:






      '''simple docstring'''







      if rng is None:
            UpperCAmelCase_  :	List[Any]				      =		global_rng

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

      return values


class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=7     ,lowercase=400     ,lowercase=2000     ,lowercase=2048     ,lowercase=128     ,lowercase=1     ,lowercase=512     ,lowercase=30     ,lowercase=44100     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		parent
             UpperCAmelCase_  :	List[Any]				      =		batch_size
             UpperCAmelCase_  :	Dict				      =		min_seq_length
             UpperCAmelCase_  :	Optional[Any]				      =		max_seq_length
             UpperCAmelCase_  :	Union[str, Any]				      =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             UpperCAmelCase_  :	str				      =		spectrogram_length
             UpperCAmelCase_  :	Optional[int]				      =		feature_size
             UpperCAmelCase_  :	Optional[int]				      =		num_audio_channels
             UpperCAmelCase_  :	List[str]				      =		hop_length
             UpperCAmelCase_  :	Tuple				      =		chunk_length
             UpperCAmelCase_  :	Any				      =		sampling_rate



       def  A_   (     self):

             """simple docstring"""





             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):

             """simple docstring"""





             def _flatten(lowercase):
                   return list(itertools.chain(*lowercase))

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


@require_torch
@require_torchaudio
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				TvltFeatureExtractor



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		TvltFeatureExtractionTester(self)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		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):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		self.feature_extraction_class(**self.feat_extract_dict)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   UpperCAmelCase_  :	List[str]				      =		feat_extract_first.save_pretrained(lowercase)[0]
                   check_json_file_has_correct_format(lowercase)
                   UpperCAmelCase_  :	int				      =		self.feature_extraction_class.from_pretrained(lowercase)

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.feature_extraction_class(**self.feat_extract_dict)

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

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.feature_extraction_class(**self.feat_extract_dict)

             # create three inputs of length 800, 1000, and 1200
             UpperCAmelCase_  :	Optional[Any]				      =		[floats_list((1, x))[0] for x in range(800     ,1400     ,200)]
             UpperCAmelCase_  :	str				      =		[np.asarray(lowercase) for speech_input in speech_inputs]

             # Test not batched input
             UpperCAmelCase_  :	List[str]				      =		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
             UpperCAmelCase_  :	List[Any]				      =		feature_extractor(lowercase     ,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
             UpperCAmelCase_  :	List[Any]				      =		feature_extractor(
                 lowercase     ,return_tensors="np"     ,sampling_rate=44100     ,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_  :	Dict				      =		[floats_list((1, x))[0] for x in (800, 800, 800)]
             UpperCAmelCase_  :	Dict				      =		np.asarray(lowercase)
             UpperCAmelCase_  :	Any				      =		feature_extractor(lowercase     ,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  A_   (     self     ,lowercase):

             """simple docstring"""





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

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self._load_datasamples(1)
             UpperCAmelCase_  :	List[str]				      =		TvltFeatureExtractor()
             UpperCAmelCase_  :	Optional[int]				      =		feature_extractor(lowercase     ,return_tensors="pt").audio_values

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

             UpperCAmelCase_  :	int				      =		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]])
             self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,lowercase     ,atol=1E-4))





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""ClapFeatureExtractor"""
       _lowerCamelCase			    =				("""RobertaTokenizer""", """RobertaTokenizerFast""")



       def __init__(     self     ,lowercase     ,lowercase):

             """simple docstring"""





             super().__init__(lowercase     ,lowercase)



       def __call__(     self     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.pop("sampling_rate"     ,lowercase)

             if text is None and audios is None:
                   raise ValueError("You have to specify either text or audios. Both cannot be none.")

             if text is not None:
                   UpperCAmelCase_  :	List[str]				      =		self.tokenizer(lowercase     ,return_tensors=lowercase     ,**lowercase)

             if audios is not None:
                   UpperCAmelCase_  :	str				      =		self.feature_extractor(
                       lowercase     ,sampling_rate=lowercase     ,return_tensors=lowercase     ,**lowercase)

             if text is not None and audios is not None:
                   UpperCAmelCase_  :	Optional[int]				      =		audio_features.input_features
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**lowercase)     ,tensor_type=lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.batch_decode(*lowercase     ,**lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.decode(*lowercase     ,**lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.tokenizer.model_input_names
             UpperCAmelCase_  :	str				      =		self.feature_extractor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
