





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

lowercase    : str    	=  random.Random()


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase=1.0    ,       lowerCAmelCase=None    ,       lowerCAmelCase=None					):
       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  __A(						unittest.TestCase ):

     def __init__(      self,       A,       A=7,       A=400,       A=2000,       A=2048,       A=128,       A=1,       A=512,       A=30,       A=4_4100,       ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      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 	_UpperCamelCase     (      self,       A=False,       A=False      ):





            """simple docstring"""

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

            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(A      ) for x in speech_inputs]
            return speech_inputs






@require_torch
@require_torchaudio
class  __A(						__UpperCAmelCase						,			unittest.TestCase ):
     __A	  =       TvltFeatureExtractor

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						TvltFeatureExtractionTester(self      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						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      ):





            """simple docstring"""

            _UpperCamelCase              =						self.feature_extraction_class(**self.feat_extract_dict      )

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

            _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(A,       A      )      )
            self.assertEqual(A,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.feature_extraction_class(**self.feat_extract_dict      )

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

            _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(A,       A      )      )
            self.assertEqual(A,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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(800,       1400,       200      )]
            _UpperCamelCase              =						[np.asarray(A      ) for speech_input in speech_inputs]

            # Test not batched input
            _UpperCamelCase              =						feature_extractor(np_speech_inputs[0],       return_tensors='''np''',       sampling_rate=4_4100      ).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(A,       return_tensors='''np''',       sampling_rate=4_4100      ).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(
                A,       return_tensors='''np''',       sampling_rate=4_4100,       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.
            _UpperCamelCase              =						[floats_list((1, x)      )[0] for x in (800, 800, 800)]
            _UpperCamelCase              =						np.asarray(A      )
            _UpperCamelCase              =						feature_extractor(A,       return_tensors='''np''',       sampling_rate=4_4100      ).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,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						load_dataset('''hf-internal-testing/librispeech_asr_dummy''',       '''clean''',       split='''validation'''      )
            # automatic decoding with librispeech
            _UpperCamelCase              =						ds.sort('''id'''      ).select(range(A      )      )[:num_samples]['''audio''']

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



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self._load_datasamples(1      )
            _UpperCamelCase              =						TvltFeatureExtractor()
            _UpperCamelCase              =						feature_extractor(A,       return_tensors='''pt'''      ).audio_values

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

            _UpperCamelCase              =						torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]      )
            self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],       A,       atol=1E-4      )      )













import os
import sys
import unittest


lowercase    : Any    	=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase    : int    	=  os.path.join(git_repo_path, """src""", """transformers""")

lowercase    : str    	=  """
{0} = None
"""

lowercase    : Union[str, Any]    	=  """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowercase    : Optional[Any]    	=  """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''      )
            self.assertIsNone(A      )

            _UpperCamelCase              =						find_backend('''    if not is_tokenizers_available():'''      )
            self.assertEqual(A,       '''tokenizers'''      )

            _UpperCamelCase              =						find_backend('''    if not is_tensorflow_text_available():'''      )
            self.assertEqual(A,       '''tensorflow_text'''      )

            _UpperCamelCase              =						find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tokenizers'''      )

            _UpperCamelCase              =						find_backend(
                '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tensorflow_text'''      )

            _UpperCamelCase              =						find_backend(
                '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tokenizers_and_vision'''      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						read_init()
            # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
            self.assertIn('''torch''',       A      )
            self.assertIn('''tensorflow_text''',       A      )
            self.assertIn('''sentencepiece_and_tokenizers''',       A      )

            # Likewise, we can't assert on the exact content of a key
            self.assertIn('''BertModel''',       objects['''torch''']      )
            self.assertIn('''TFBertModel''',       objects['''tf''']      )
            self.assertIn('''FlaxBertModel''',       objects['''flax''']      )
            self.assertIn('''BertModel''',       objects['''torch''']      )
            self.assertIn('''TFBertTokenizer''',       objects['''tensorflow_text''']      )
            self.assertIn('''convert_slow_tokenizer''',       objects['''sentencepiece_and_tokenizers''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						create_dummy_object('''CONSTANT''',       '''\'torch\''''      )
            self.assertEqual(A,       '''\nCONSTANT = None\n'''      )

            _UpperCamelCase              =						create_dummy_object('''function''',       '''\'torch\''''      )
            self.assertEqual(
                A,       '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''      )

            _UpperCamelCase              =						'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
            _UpperCamelCase              =						create_dummy_object('''FakeClass''',       '''\'torch\''''      )
            self.assertEqual(A,       A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
            _UpperCamelCase              =						create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}      )
            self.assertEqual(dummy_files['''torch'''],       A      )






