




"""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

a_	   =    random.Random()







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase=1.0     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ):





			if rng is None:
						__lowercase       :   Optional[int]        =    global_rng

			__lowercase       :   Dict        =    []
			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							,		UpperCamelCase_							,		UpperCamelCase_=7							,		UpperCamelCase_=4_00							,		UpperCamelCase_=20_00							,		UpperCamelCase_=20_48							,		UpperCamelCase_=1_28							,		UpperCamelCase_=1							,		UpperCamelCase_=5_12							,		UpperCamelCase_=30							,		UpperCamelCase_=4_41_00							,		)		->    Dict:
					__lowercase       :   Optional[Any]        =    parent
					__lowercase       :   Optional[int]        =    batch_size
					__lowercase       :   Union[str, Any]        =    min_seq_length
					__lowercase       :   Dict        =    max_seq_length
					__lowercase       :   Any        =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__lowercase       :   Optional[int]        =    spectrogram_length
					__lowercase       :   int        =    feature_size
					__lowercase       :   int        =    num_audio_channels
					__lowercase       :   Optional[Any]        =    hop_length
					__lowercase       :   Any        =    chunk_length
					__lowercase       :   List[str]        =    sampling_rate
		def      _lowerCamelCase				(		self    )		->    Tuple:
					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      _lowerCamelCase				(		self							,		UpperCamelCase_=False							,		UpperCamelCase_=False    )		->    Union[str, Any]:




					def _flatten(UpperCamelCase_    ):
								return list(itertools.chain(*lowercase_    )    )

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


@require_torch
@require_torchaudio
class    UpperCAmelCase_  (       UpperCamelCase_					, unittest.TestCase						):
		UpperCamelCase						 =TvltFeatureExtractor
		def      _lowerCamelCase				(		self    )		->    List[str]:
					__lowercase       :   Any        =    TvltFeatureExtractionTester(self    )
		def      _lowerCamelCase				(		self    )		->    Tuple:
					__lowercase       :   Tuple        =    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      _lowerCamelCase				(		self    )		->    Tuple:
					__lowercase       :   Optional[int]        =    self.feature_extraction_class(**self.feat_extract_dict    )

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

					__lowercase       :   List[Any]        =    feat_extract_first.to_dict()
					__lowercase       :   Dict        =    feat_extract_second.to_dict()
					__lowercase       :   Optional[int]        =    dict_first.pop('''mel_filters'''    )
					__lowercase       :   List[str]        =    dict_second.pop('''mel_filters'''    )
					self.assertTrue(np.allclose(lowercase_							,		lowercase_    )    )
					self.assertEqual(lowercase_							,		lowercase_    )
		def      _lowerCamelCase				(		self    )		->    Optional[Any]:
					__lowercase       :   Optional[int]        =    self.feature_extraction_class(**self.feat_extract_dict    )

					with tempfile.TemporaryDirectory() as tmpdirname:
								__lowercase       :   str        =    os.path.join(lowercase_							,		'''feat_extract.json'''    )
								feat_extract_first.to_json_file(lowercase_    )
								__lowercase       :   Tuple        =    self.feature_extraction_class.from_json_file(lowercase_    )

					__lowercase       :   str        =    feat_extract_first.to_dict()
					__lowercase       :   Union[str, Any]        =    feat_extract_second.to_dict()
					__lowercase       :   Optional[int]        =    dict_first.pop('''mel_filters'''    )
					__lowercase       :   Dict        =    dict_second.pop('''mel_filters'''    )
					self.assertTrue(np.allclose(lowercase_							,		lowercase_    )    )
					self.assertEqual(lowercase_							,		lowercase_    )
		def      _lowerCamelCase				(		self    )		->    Tuple:
					__lowercase       :   Union[str, Any]        =    self.feature_extraction_class(**self.feat_extract_dict    )

					# create three inputs of length 800, 1000, and 1200
					__lowercase       :   Any        =    [floats_list((1, x)    )[0] for x in range(8_00							,		14_00							,		2_00    )]
					__lowercase       :   List[str]        =    [np.asarray(lowercase_    ) for speech_input in speech_inputs]

					# Test not batched input
					__lowercase       :   Tuple        =    feature_extractor(np_speech_inputs[0]							,		return_tensors='''np'''							,		sampling_rate=4_41_00    ).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
					__lowercase       :   Optional[int]        =    feature_extractor(lowercase_							,		return_tensors='''np'''							,		sampling_rate=4_41_00    ).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
					__lowercase       :   int        =    feature_extractor(
					    lowercase_							,		return_tensors='''np'''							,		sampling_rate=4_41_00							,		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.
					__lowercase       :   Any        =    [floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
					__lowercase       :   List[str]        =    np.asarray(lowercase_    )
					__lowercase       :   Optional[Any]        =    feature_extractor(lowercase_							,		return_tensors='''np'''							,		sampling_rate=4_41_00    ).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      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    str:
					__lowercase       :   Any        =    load_dataset('''hf-internal-testing/librispeech_asr_dummy'''							,		'''clean'''							,		split='''validation'''    )
					# automatic decoding with librispeech
					__lowercase       :   Tuple        =    ds.sort('''id'''    ).select(range(lowercase_    )    )[:num_samples]['audio']

					return [x["array"] for x in speech_samples]
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					__lowercase       :   str        =    self._load_datasamples(1    )
					__lowercase       :   Optional[int]        =    TvltFeatureExtractor()
					__lowercase       :   int        =    feature_extractor(lowercase_							,		return_tensors='''pt'''    ).audio_values

					self.assertEquals(audio_values.shape							,		(1, 1, 1_92, 1_28)    )

					__lowercase       :   str        =    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    )    )







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

lowerCamelCase											=       'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      str=None     ):
       '''simple docstring'''





       require_version(deps[pkg]		,			SCREAMING_SNAKE_CASE__     )


