





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

SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   random.Random()



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=1.0						,   snake_case=None						,   snake_case=None							)		->    int:
		if rng is None:
				__lowercase											=     global_rng

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

		return values

class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




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

			'''simple docstring'''




			__lowercase											=     parent
			__lowercase											=     batch_size
			__lowercase											=     min_seq_length
			__lowercase											=     max_seq_length
			__lowercase											=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			__lowercase											=     spectrogram_length
			__lowercase											=     feature_size
			__lowercase											=     num_audio_channels
			__lowercase											=     hop_length
			__lowercase											=     chunk_length
			__lowercase											=     sampling_rate




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

			'''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							:     List[str]					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Dict=False   )       -> Tuple:

			'''simple docstring'''




			def _flatten(__lowerCamelCase							:     List[str]   ):
					return list(itertools.chain(*__lowerCamelCase   )   )

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





@require_torch
@require_torchaudio
class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		TvltFeatureExtractor




	def    UpperCAmelCase			(						self							:     Any   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     TvltFeatureExtractionTester(self   )




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

			'''simple docstring'''




			__lowercase											=     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    UpperCAmelCase			(						self							:     int   )       -> List[str]:

			'''simple docstring'''




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

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

			__lowercase											=     feat_extract_first.to_dict()
			__lowercase											=     feat_extract_second.to_dict()
			__lowercase											=     dict_first.pop('mel_filters'   )
			__lowercase											=     dict_second.pop('mel_filters'   )
			self.assertTrue(np.allclose(__lowerCamelCase					,      __lowerCamelCase   )   )
			self.assertEqual(__lowerCamelCase					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple   )       -> str:

			'''simple docstring'''




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

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

			__lowercase											=     feat_extract_first.to_dict()
			__lowercase											=     feat_extract_second.to_dict()
			__lowercase											=     dict_first.pop('mel_filters'   )
			__lowercase											=     dict_second.pop('mel_filters'   )
			self.assertTrue(np.allclose(__lowerCamelCase					,      __lowerCamelCase   )   )
			self.assertEqual(__lowerCamelCase					,      __lowerCamelCase   )




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

			'''simple docstring'''




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

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

			# Test not batched input
			__lowercase											=     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
			__lowercase											=     feature_extractor(__lowerCamelCase					,      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
			__lowercase											=     feature_extractor(
			    __lowerCamelCase					,      return_tensors='np'					,      sampling_rate=44_100					,      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.
			__lowercase											=     [floats_list((1, x)   )[0] for x in (800, 800, 800)]
			__lowercase											=     np.asarray(__lowerCamelCase   )
			__lowercase											=     feature_extractor(__lowerCamelCase					,      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    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     str   )       -> Optional[Any]:

			'''simple docstring'''




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

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






	def    UpperCAmelCase			(						self							:     Dict   )       -> str:

			'''simple docstring'''




			__lowercase											=     self._load_datasamples(1   )
			__lowercase											=     TvltFeatureExtractor()
			__lowercase											=     feature_extractor(__lowerCamelCase					,      return_tensors='pt'   ).audio_values

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

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











import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[int]:
		return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     _TestCommandArgs(dataset=snake_case						,   all_configs=snake_case						,   save_infos=snake_case							)
		__lowercase											=     TestCommand(*snake_case							)
		test_command.run()
		__lowercase											=     os.path.join(snake_case						,   'README.md'							)
		assert os.path.exists(snake_case							)
		__lowercase											=     DatasetInfosDict.from_directory(snake_case							)
		__lowercase											=     DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'							)							),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']							)							),
		                    'langs': Sequence(Value('string'							)							),
		                    'spans': Sequence(Value('string'							)							),
		                }							)						,   splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_351_563,
		                    'num_examples': 10_000,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 238_418,
		                    'num_examples': 1_000,
		                },
		            ]						,   download_size=3_940_680						,   dataset_size=2_589_981						,   )
		    }							)
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				__lowercase						,      __lowercase											=     getattr(dataset_infos['default']						,   snake_case							), getattr(expected_dataset_infos['default']						,   snake_case							)
				if key == "num_bytes":
						assert is_apercent_close(snake_case						,   snake_case							)
				elif key == "splits":
						assert list(snake_case							) == list(snake_case							)
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,   expected[split].num_bytes							)
				else:
						result == expected




