





"""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

_snake_case          =						random.Random()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[Any]=1.0     ,  SCREAMING_SNAKE_CASE:					Tuple=None     ,  SCREAMING_SNAKE_CASE:					Any=None						):






							"""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        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



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







											"""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 				__lowerCamelCase	(					self	:			Any  )		->  str:







											"""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	:			Optional[int]				,     UpperCAmelCase_	:			Any=False				,     UpperCAmelCase_	:			str=False  )		->  str:







											"""simple docstring"""




											def _flatten(UpperCAmelCase_	:			Optional[int]  ):
																		return list(itertools.chain(*UpperCAmelCase_  )  )

											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(UpperCAmelCase_  ) for x in speech_inputs]
											return speech_inputs






@require_torch
@require_torchaudio
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					TvltFeatureExtractor


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







											"""simple docstring"""




											_lowerCAmelCase  	=			TvltFeatureExtractionTester(self  )


				def 				__lowerCamelCase	(					self	:			Any  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.feature_extraction_class(**self.feat_extract_dict  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'spectrogram_length'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'feature_size'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'num_audio_channels'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'hop_length'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'chunk_length'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'sampling_rate'  )  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Optional[int]:







											"""simple docstring"""




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

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

											_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(UpperCAmelCase_				,     UpperCAmelCase_  )  )
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  List[str]:







											"""simple docstring"""




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

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

											_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(UpperCAmelCase_				,     UpperCAmelCase_  )  )
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


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







											"""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				,     1_400				,     200  )]
											_lowerCAmelCase  	=			[np.asarray(UpperCAmelCase_  ) for speech_input in speech_inputs]

											# Test not batched input
											_lowerCAmelCase  	=			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
											_lowerCAmelCase  	=			feature_extractor(UpperCAmelCase_				,     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
											_lowerCAmelCase  	=			feature_extractor(
											    UpperCAmelCase_				,     return_tensors='np'				,     sampling_rate=44_100				,     mask_audio=UpperCAmelCase_  ).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(UpperCAmelCase_  )
											_lowerCAmelCase  	=			feature_extractor(UpperCAmelCase_				,     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	:			Any				,     UpperCAmelCase_	:			str  )		->  Dict:







											"""simple docstring"""




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

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







				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self._load_datasamples(1  )
											_lowerCAmelCase  	=			TvltFeatureExtractor()
											_lowerCAmelCase  	=			feature_extractor(UpperCAmelCase_				,     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]				,     UpperCAmelCase_				,     atol=1E-4  )  )








"""simple docstring"""



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

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					field(default="image-classification"      ,				metadata={"include_in_asdict_even_if_is_default": True}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"image": Image()}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"labels": ClassLabel}							)
				SCREAMING_SNAKE_CASE_:							str							=					"image"
				SCREAMING_SNAKE_CASE_:							str							=					"labels"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											if self.label_column not in features:
																		raise ValueError(F"""Column {self.label_column} is not present in features."""  )
											if not isinstance(features[self.label_column]				,     UpperCAmelCase_  ):
																		raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
											_lowerCAmelCase  	=			copy.deepcopy(self  )
											_lowerCAmelCase  	=			self.label_schema.copy()
											_lowerCAmelCase  	=			features[self.label_column]
											_lowerCAmelCase  	=			label_schema
											return task_template







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict[str, str]:







											"""simple docstring"""




											return {
											    self.image_column: "image",
											    self.label_column: "labels",
											}






