

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 			lowerCamelCase__			(       _a				,      _a=1.0				,      _a=None				,      _a=None):
	if rng is None:
		SCREAMING_SNAKE_CASE :   List[str]					  =							global_rng

	SCREAMING_SNAKE_CASE :   Optional[int]					  =							[]
	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 ):
			'''simple docstring'''







			def __init__(      self				:			str   ,   a				:			Any   ,   a				:			Union[str, Any]=7   ,   a				:			List[Any]=400   ,   a				:			str=2000   ,   a				:			Dict=2048   ,   a				:			List[Any]=128   ,   a				:			Tuple=1   ,   a				:			Union[str, Any]=512   ,   a				:			List[str]=30   ,   a				:			Tuple=4_4100   ,   )				->							Dict:

				"""simple docstring"""

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

				"""simple docstring"""

				def _flatten(a				:			Any						):
					return list(itertools.chain(*a						)						)

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






@require_torch
@require_torchaudio
class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =TvltFeatureExtractor
			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							TvltFeatureExtractionTester(self						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							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				:			List[str]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							feat_extract_first.save_pretrained(a						)[0]
					check_json_file_has_correct_format(a						)
					SCREAMING_SNAKE_CASE :   Any					  =							self.feature_extraction_class.from_pretrained(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   int					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							dict_first.pop("mel_filters"						)
				SCREAMING_SNAKE_CASE :   str					  =							dict_second.pop("mel_filters"						)
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

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

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							os.path.join(a   ,   "feat_extract.json"						)
					feat_extract_first.to_json_file(a						)
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.feature_extraction_class.from_json_file(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   Tuple					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							dict_first.pop("mel_filters"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							dict_second.pop("mel_filters"						)
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.feature_extraction_class(**self.feat_extract_dict						)

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

				# Test not batched input
				SCREAMING_SNAKE_CASE :   str					  =							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
				SCREAMING_SNAKE_CASE :   Tuple					  =							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
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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.
				SCREAMING_SNAKE_CASE :   Any					  =							[floats_list((1, x)						)[0] for x in (800, 800, 800)]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							np.asarray(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							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				:			List[Any]   ,   a				:			str						)				->							str:

				"""simple docstring"""

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

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






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self._load_datasamples(1						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							TvltFeatureExtractor()
				SCREAMING_SNAKE_CASE :   Any					  =							feature_extractor(a   ,   return_tensors="pt"						).audio_values

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

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]						)
				self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,   a   ,   atol=1e-4						)						)




from __future__ import annotations

import numpy as np

def        __a			(   __lowerCAmelCase   )     ->     Optional[Any]:
					return np.maximum(0      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]