
"""simple docstring"""





import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								A_				 =						pa.array(TypedSequence([1, 2, 3]	)	)
								self.assertEqual(arr.type       ,   pa.intaa()	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								with self.assertRaises(_snake_case	):
													A_				 =						pa.array(TypedSequence([1, 2, 3]	)       ,   type=pa.intaa()	)




			def      lowerCamelCase__     (				self						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								with self.assertRaises(_snake_case	):
													A_				 =						pa.array(TypedSequence([1, 2, 3]       ,   try_type=Value("bool"	)       ,   type=Value("int64"	)	)	)




			def      lowerCamelCase__     (				self						:					int	)      -> int:







								"""simple docstring"""
								A_				 =						pa.array(TypedSequence([1, 2, 3]       ,   type=Value("int32"	)	)	)
								self.assertEqual(arr.type       ,   pa.intaa()	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
													A_				 =						pa.array(TypedSequence(["foo", "bar"]       ,   type=Value("int64"	)	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Any:







								"""simple docstring"""
								A_				 =						pa.array(TypedSequence([1, 2, 3]       ,   try_type=Value("int32"	)	)	)
								self.assertEqual(arr.type       ,   pa.intaa()	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						pa.array(TypedSequence(["foo", "bar"]       ,   try_type=Value("int64"	)	)	)
								self.assertEqual(arr.type       ,   pa.string()	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







								"""simple docstring"""
								A_				 =						pa.array(TypedSequence([[[1, 2, 3]]]       ,   type=ArrayaD((1, 3)       ,   "int64"	)	)	)
								self.assertEqual(arr.type       ,   ArrayaDExtensionType((1, 3)       ,   "int64"	)	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
													A_				 =						pa.array(TypedSequence(["foo", "bar"]       ,   type=ArrayaD((1, 3)       ,   "int64"	)	)	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> str:







								"""simple docstring"""
								A_				 =						pa.array(TypedSequence([[[1, 2, 3]]]       ,   try_type=ArrayaD((1, 3)       ,   "int64"	)	)	)
								self.assertEqual(arr.type       ,   ArrayaDExtensionType((1, 3)       ,   "int64"	)	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						pa.array(TypedSequence(["foo", "bar"]       ,   try_type=ArrayaD((1, 3)       ,   "int64"	)	)	)
								self.assertEqual(arr.type       ,   pa.string()	)






			@require_pil
			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[Any]:







								"""simple docstring"""
								import PIL.Image

								A_				 =						PIL.Image.fromarray(np.arange(10       ,   dtype=np.uinta	).reshape(2       ,   5	)	)
								with patch(
								    "datasets.arrow_writer.cast_to_python_objects"       ,   side_effect=_snake_case	) as mock_cast_to_python_objects:
													A_				 =						pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]       ,   type=Image()	)	)
													A_ ,    A_				 =						mock_cast_to_python_objects.call_args_list[-1]
													self.assertIn("optimize_list_casting"       ,   _snake_case	)
													self.assertFalse(kwargs["optimize_list_casting"]	)





def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						pa.BufferReader(__a				) if isinstance(__a				,   pa.Buffer				) else pa.memory_map(__a				)
					A_				 =						pa.ipc.open_stream(__a				)
					A_				 =						f.read_all()
					assert len(pa_table.to_batches()				) == expected_num_chunks
					assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
					del pa_table
@pytest.mark.parametrize("writer_batch_size"				,   [None, 1, 10]				)
@pytest.mark.parametrize(
    "fields"				,   [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]				)
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					A_				 =						pa.schema(__a				) if fields else None
					with ArrowWriter(stream=__a				,   schema=__a				,   writer_batch_size=__a				) as writer:
										writer.write({"col_1": "foo", "col_2": 1}				)
										writer.write({"col_1": "bar", "col_2": 2}				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										A_				 =						{"col_1": pa.string(), "col_2": pa.intaa()}
					assert writer._schema == pa.schema(__a				,   metadata=writer._schema.metadata				)
					_check_output(output.getvalue()				,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)
def 							A_     ():

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					A_				 =						Features({"labels": ClassLabel(names=["neg", "pos"]				)}				)
					with ArrowWriter(stream=__a				,   features=__a				) as writer:
										writer.write({"labels": 0}				)
										writer.write({"labels": 1}				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert writer._schema == features.arrow_schema
					assert writer._schema.metadata == features.arrow_schema.metadata
					A_				 =						pa.BufferReader(output.getvalue()				)
					A_				 =						pa.ipc.open_stream(__a				)
					A_				 =						f.read_all()
					A_				 =						pa_table.schema
					assert pa_table.num_rows == 2
					assert schema == features.arrow_schema
					assert schema.metadata == features.arrow_schema.metadata
					assert features == Features.from_arrow_schema(__a				)
@pytest.mark.parametrize("writer_batch_size"				,   [None, 1, 10]				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__a				,   writer_batch_size=__a				,   hash_salt="split_name"				,   check_duplicates=__a				,   ) as writer:
										with pytest.raises(__a				):
															writer.write({"col_1": "foo", "col_2": 1}				,   key=[1, 2]				)
															A_ ,    A_				 =						writer.finalize()
@pytest.mark.parametrize("writer_batch_size"				,   [None, 2, 10]				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__a				,   writer_batch_size=__a				,   hash_salt="split_name"				,   check_duplicates=__a				,   ) as writer:
										with pytest.raises(__a				):
															writer.write({"col_1": "foo", "col_2": 1}				,   key=10				)
															writer.write({"col_1": "bar", "col_2": 2}				,   key=10				)
															A_ ,    A_				 =						writer.finalize()
@pytest.mark.parametrize("writer_batch_size"				,   [None, 2, 10]				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__a				,   writer_batch_size=__a				,   hash_salt="split_name"				,   check_duplicates=__a				,   ) as writer:
										writer.write({"col_1": "foo", "col_2": 1}				,   key=1				)
										writer.write({"col_1": "bar", "col_2": 2}				,   key=2				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					_check_output(output.getvalue()				,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)
@pytest.mark.parametrize("writer_batch_size"				,   [None, 1, 10]				)
@pytest.mark.parametrize(
    "fields"				,   [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]				)
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					A_				 =						pa.schema(__a				) if fields else None
					with ArrowWriter(stream=__a				,   schema=__a				,   writer_batch_size=__a				) as writer:
										writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}				)
										writer.write_batch({"col_1": [], "col_2": []}				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										A_				 =						{"col_1": pa.string(), "col_2": pa.intaa()}
					assert writer._schema == pa.schema(__a				,   metadata=writer._schema.metadata				)
					_check_output(output.getvalue()				,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)
@pytest.mark.parametrize("writer_batch_size"				,   [None, 1, 10]				)
@pytest.mark.parametrize(
    "fields"				,   [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]				)
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					A_				 =						pa.schema(__a				) if fields else None
					with ArrowWriter(stream=__a				,   schema=__a				,   writer_batch_size=__a				) as writer:
										writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}				)				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										A_				 =						{"col_1": pa.string(), "col_2": pa.intaa()}
					assert writer._schema == pa.schema(__a				,   metadata=writer._schema.metadata				)
					_check_output(output.getvalue()				,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)
@pytest.mark.parametrize("writer_batch_size"				,   [None, 1, 10]				)
@pytest.mark.parametrize(
    "fields"				,   [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]				)
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					A_				 =						pa.schema(__a				) if fields else None
					with ArrowWriter(stream=__a				,   schema=__a				,   writer_batch_size=__a				) as writer:
										writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}				)				)
										writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}				)				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										A_				 =						{"col_1": pa.string(), "col_2": pa.intaa()}
					assert writer._schema == pa.schema(__a				,   metadata=writer._schema.metadata				)
					_check_output(output.getvalue()				,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)
def 							A_     ():

					'''simple docstring'''





					with tempfile.TemporaryDirectory() as tmp_dir:
										A_				 =						{"col_1": pa.string(), "col_2": pa.intaa()}
										A_				 =						os.path.join(__a				,   "test.arrow"				)
										with ArrowWriter(path=__a				,   schema=pa.schema(__a				)				) as writer:
															writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}				)
															A_ ,    A_				 =						writer.finalize()
										assert num_examples == 2
										assert num_bytes > 0
										assert writer._schema == pa.schema(__a				,   metadata=writer._schema.metadata				)
										_check_output(__a				,   1				)
def 							A_     (__a				):

					'''simple docstring'''





					if pa.types.is_list(__a				):
										return get_base_dtype(arr_type.value_type				)
					else:
										return arr_type
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					if isinstance(lst[0]				,   __a				):
										change_first_primitive_element_in_list(lst[0]				,   __a				)
					else:
										A_				 =						value
@pytest.mark.parametrize("optimized_int_type, expected_dtype"				,   [(None, pa.intaa()), (Value("int32"				), pa.intaa())]				)
@pytest.mark.parametrize("sequence"				,   [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						pa.array(TypedSequence(__a				,   optimized_int_type=__a				)				)
					assert get_base_dtype(arr.type				) == expected_dtype
@pytest.mark.parametrize(
    "col, expected_dtype"				,   [
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]				,   )
@pytest.mark.parametrize("sequence"				,   [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						pa.array(OptimizedTypedSequence(__a				,   col=__a				)				)
					assert get_base_dtype(arr.type				) == expected_dtype

					# not in range
					if col != "other":
										# avoids errors due to in-place modifications
										A_				 =						copy.deepcopy(__a				)
										A_				 =						np.iinfo(expected_dtype.to_pandas_dtype()				).max + 1
										change_first_primitive_element_in_list(__a				,   __a				)
										A_				 =						pa.array(OptimizedTypedSequence(__a				,   col=__a				)				)
										assert get_base_dtype(arr.type				) == pa.intaa()
@pytest.mark.parametrize("raise_exception"				,   [False, True]				)
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						str(tmp_path / "dataset-train.arrow"				)
					try:
										with ArrowWriter(path=__a				) as writer:
															if raise_exception:
																				raise pa.lib.ArrowInvalid()
															else:
																				writer.stream.close()
					except pa.lib.ArrowInvalid:
										pass
					finally:
										assert writer.stream.closed
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						"mock://dataset-train.arrow"
					with ArrowWriter(path=__a				,   storage_options=mockfs.storage_options				) as writer:
										assert isinstance(writer._fs				,   type(__a				)				)
										assert writer._fs.storage_options == mockfs.storage_options
										writer.write({"col_1": "foo", "col_2": 1}				)
										writer.write({"col_1": "bar", "col_2": 2}				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert mockfs.exists(__a				)
def 							A_     ():

					'''simple docstring'''





					A_				 =						pa.BufferOutputStream()
					with ParquetWriter(stream=__a				) as writer:
										writer.write({"col_1": "foo", "col_2": 1}				)
										writer.write({"col_1": "bar", "col_2": 2}				)
										A_ ,    A_				 =						writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					A_				 =						pa.BufferReader(output.getvalue()				)
					A_				 =						pq.read_table(__a				)
					assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize("embed_local_files"				,   [False, True]				)
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					import PIL.Image

					A_				 =						str(tmp_path / "test_image_rgb.jpg"				)
					PIL.Image.fromarray(np.zeros((5, 5)				,   dtype=np.uinta				)				).save(__a				,   format="png"				)
					A_				 =						pa.BufferOutputStream()
					with ParquetWriter(
					    stream=__a				,   features=Features({"image": Image()}				)				,   embed_local_files=__a				) as writer:
										writer.write({"image": image_path}				)
										writer.finalize()
					A_				 =						pa.BufferReader(output.getvalue()				)
					A_				 =						pq.read_table(__a				)
					A_				 =						pa_table.to_pydict()
					if embed_local_files:
										assert isinstance(out["image"][0]["path"]				,   __a				)
										with open(__a				,   "rb"				) as f:
															assert out["image"][0]["bytes"] == f.read()
					else:
										assert out["image"][0]["path"] == image_path
										assert out["image"][0]["bytes"] is None




def 							A_     ():

					'''simple docstring'''





					A_				 =						pa.schema([pa.field("col_1"				,   pa.string()				,   nullable=__a				)]				)
					A_				 =						pa.BufferOutputStream()
					with ArrowWriter(stream=__a				) as writer:
										writer._build_writer(inferred_schema=__a				)
										assert writer._schema == pa.schema([pa.field("col_1"				,   pa.string()				)]				)






"""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

UpperCamelCase_					:						Union[str, Any]				= random.Random()
def 							A_     (__a				,   __a=1.0				,   __a=None				,   __a=None				):

					'''simple docstring'''





					if rng is None:
										A_				 =						global_rng

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

					return values



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Union[str, Any]=7       ,   _snake_case						:					Any=400       ,   _snake_case						:					Tuple=2_000       ,   _snake_case						:					str=2_048       ,   _snake_case						:					List[Any]=128       ,   _snake_case						:					List[Any]=1       ,   _snake_case						:					Union[str, Any]=512       ,   _snake_case						:					List[str]=30       ,   _snake_case						:					List[str]=44_100       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						min_seq_length
								A_				 =						max_seq_length
								A_				 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A_				 =						spectrogram_length
								A_				 =						feature_size
								A_				 =						num_audio_channels
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						sampling_rate




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""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      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[Any]=False	)      -> List[str]:







								"""simple docstring"""
								def _flatten(_snake_case						:					List[Any]	):
													return list(itertools.chain(*_snake_case	)	)

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





@require_torch
@require_torchaudio
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TvltFeatureExtractor




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TvltFeatureExtractionTester(self	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "spectrogram_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "feature_size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "num_audio_channels"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "hop_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "chunk_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "sampling_rate"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

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

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




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

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

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




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								# Initialize feature_extractor
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								A_				 =						[floats_list((1, x)	)[0] for x in range(800       ,   1_400       ,   200	)]
								A_				 =						[np.asarray(_snake_case	) for speech_input in speech_inputs]

								# Test not batched input
								A_				 =						feature_extractor(np_speech_inputs[0]       ,   return_tensors="np"       ,   sampling_rate=44_100	).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
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).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
								A_				 =						feature_extractor(
								    _snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100       ,   mask_audio=_snake_case	).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.
								A_				 =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								A_				 =						np.asarray(_snake_case	)
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).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						:					Dict       ,   _snake_case						:					int	)      -> List[str]:







								"""simple docstring"""
								A_				 =						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,   "clean"       ,   split="validation"	)
								# automatic decoding with librispeech
								A_				 =						ds.sort("id"	).select(range(_snake_case	)	)[:num_samples]["audio"]

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






			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						self._load_datasamples(1	)
								A_				 =						TvltFeatureExtractor()
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="pt"	).audio_values

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

								A_				 =						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]       ,   _snake_case       ,   atol=1e-4	)	)




