'''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

__lowercase        =    random.Random()






def 							snake_case__				(     _A:   Any ,  _A:   int=1.0 ,  _A:   List[str]=None ,  _A:   str=None					)       ->      Dict:

		'''simple docstring'''


		if rng is None:
				lowerCAmelCase     =    global_rng

		lowerCAmelCase     =    []
		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      ):


			'''simple docstring'''






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




					"""simple docstring"""


					lowerCAmelCase     =    parent
					lowerCAmelCase     =    batch_size
					lowerCAmelCase     =    min_seq_length
					lowerCAmelCase     =    max_seq_length
					lowerCAmelCase     =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					lowerCAmelCase     =    spectrogram_length
					lowerCAmelCase     =    feature_size
					lowerCAmelCase     =    num_audio_channels
					lowerCAmelCase     =    hop_length
					lowerCAmelCase     =    chunk_length
					lowerCAmelCase     =    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			,       __lowerCAmelCase=False			,       __lowerCAmelCase=False):




					"""simple docstring"""


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

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







@require_torch
@require_torchaudio
class 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			TvltFeatureExtractor



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    TvltFeatureExtractionTester(self)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.feature_extraction_class(**self.feat_extract_dict)
					self.assertTrue(hasattr(__lowerCAmelCase			,       """spectrogram_length"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """feature_size"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """num_audio_channels"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """hop_length"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """chunk_length"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """sampling_rate"""))



			def 				a_			(  self):




					"""simple docstring"""


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

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

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



			def 				a_			(  self):




					"""simple docstring"""


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

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

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



			def 				a_			(  self):




					"""simple docstring"""


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

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

					# Test not batched input
					lowerCAmelCase     =    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
					lowerCAmelCase     =    feature_extractor(__lowerCAmelCase			,       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
					lowerCAmelCase     =    feature_extractor(
					    __lowerCAmelCase			,       return_tensors="""np"""			,       sampling_rate=44100			,       mask_audio=__lowerCAmelCase).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.
					lowerCAmelCase     =    [floats_list((1, x))[0] for x in (800, 800, 800)]
					lowerCAmelCase     =    np.asarray(__lowerCAmelCase)
					lowerCAmelCase     =    feature_extractor(__lowerCAmelCase			,       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			,       __lowerCAmelCase):




					"""simple docstring"""


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

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







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self._load_datasamples(1)
					lowerCAmelCase     =    TvltFeatureExtractor()
					lowerCAmelCase     =    feature_extractor(__lowerCAmelCase			,       return_tensors="""pt""").audio_values

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

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





'''simple docstring'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate






@dataclass(frozen=lowerCAmelCase__      )
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			field(default='''automatic-speech-recognition'''					,		metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''audio''': Audio()}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''transcription''': Value('''string'''      )}      )
			UpperCAmelCase_     :	str						=			"audio"
			UpperCAmelCase_     :	str						=			"transcription"



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if self.audio_column not in features:
							raise ValueError(f"Column {self.audio_column} is not present in features.")
					if not isinstance(features[self.audio_column]			,       __lowerCAmelCase):
							raise ValueError(f"Column {self.audio_column} is not an Audio type.")
					lowerCAmelCase     =    copy.deepcopy(self)
					lowerCAmelCase     =    self.input_schema.copy()
					lowerCAmelCase     =    features[self.audio_column]
					lowerCAmelCase     =    input_schema
					return task_template







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {self.audio_column: "audio", self.transcription_column: "transcription"}




