'''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

__lowerCAmelCase	:       Optional[int]						       =	random.Random()





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Union[str, Any]=1.0    ,					UpperCamelCase__				:   str=None    ,					UpperCamelCase__				:   Optional[Any]=None       ):


	"""simple docstring"""
	if rng is None:
		__UpperCAmelCase							=			global_rng

	__UpperCAmelCase							=			[]
	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   ):





							def __init__(      self				:							int     ,		__a				:							int     ,		__a				:							Tuple=7     ,		__a				:							Dict=4_0_0     ,		__a				:							str=2_0_0_0     ,		__a				:							Optional[Any]=2_0_4_8     ,		__a				:							Optional[int]=1_2_8     ,		__a				:							Optional[int]=1     ,		__a				:							int=5_1_2     ,		__a				:							Union[str, Any]=3_0     ,		__a				:							Any=4_4_1_0_0     ,		)							->	Tuple:
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			batch_size
								__UpperCAmelCase							=			min_seq_length
								__UpperCAmelCase							=			max_seq_length
								__UpperCAmelCase							=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__UpperCAmelCase							=			spectrogram_length
								__UpperCAmelCase							=			feature_size
								__UpperCAmelCase							=			num_audio_channels
								__UpperCAmelCase							=			hop_length
								__UpperCAmelCase							=			chunk_length
								__UpperCAmelCase							=			sampling_rate





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Dict:
								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       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							Optional[Any]=False     ,		__a				:							int=False	)							->	Union[str, Any]:
								def _flatten(__a				:							int	):
									return list(itertools.chain(*__a	)	)

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






@require_torch
@require_torchaudio
class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      TvltFeatureExtractor





							def       snake_case__						(      self				:							Any	)							->	Optional[Any]:
								__UpperCAmelCase							=			TvltFeatureExtractionTester(self	)





							def       snake_case__						(      self				:							List[str]	)							->	str:
								__UpperCAmelCase							=			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       snake_case__						(      self				:							Tuple	)							->	List[str]:
								__UpperCAmelCase							=			self.feature_extraction_class(**self.feat_extract_dict	)

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

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





							def       snake_case__						(      self				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			self.feature_extraction_class(**self.feat_extract_dict	)

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

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





							def       snake_case__						(      self				:							str	)							->	List[Any]:
								# Initialize feature_extractor
								__UpperCAmelCase							=			self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								__UpperCAmelCase							=			[floats_list((1, x)	)[0] for x in range(8_0_0     ,		1_4_0_0     ,		2_0_0	)]
								__UpperCAmelCase							=			[np.asarray(__a	) for speech_input in speech_inputs]

								# Test not batched input
								__UpperCAmelCase							=			feature_extractor(np_speech_inputs[0]     ,		return_tensors='''np'''     ,		sampling_rate=4_4_1_0_0	).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
								__UpperCAmelCase							=			feature_extractor(__a     ,		return_tensors='''np'''     ,		sampling_rate=4_4_1_0_0	).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
								__UpperCAmelCase							=			feature_extractor(
								    __a     ,		return_tensors='''np'''     ,		sampling_rate=4_4_1_0_0     ,		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.
								__UpperCAmelCase							=			[floats_list((1, x)	)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								__UpperCAmelCase							=			np.asarray(__a	)
								__UpperCAmelCase							=			feature_extractor(__a     ,		return_tensors='''np'''     ,		sampling_rate=4_4_1_0_0	).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       snake_case__						(      self				:							Tuple     ,		__a				:							List[Any]	)							->	int:
								__UpperCAmelCase							=			load_dataset('''hf-internal-testing/librispeech_asr_dummy'''     ,		'''clean'''     ,		split='''validation'''	)
								# automatic decoding with librispeech
								__UpperCAmelCase							=			ds.sort('''id'''	).select(range(__a	)	)[:num_samples]['''audio''']

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






							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			self._load_datasamples(1	)
								__UpperCAmelCase							=			TvltFeatureExtractor()
								__UpperCAmelCase							=			feature_extractor(__a     ,		return_tensors='''pt'''	).audio_values

								self.assertEquals(audio_values.shape     ,		(1, 1, 1_9_2, 1_2_8)	)

								__UpperCAmelCase							=			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]     ,		__a     ,		atol=1e-4	)	)

'''simple docstring'''


from ...configuration_utils import PretrainedConfig






class 						A   (   UpperCAmelCase   ):
							a_        =      '''bert-generation'''





							def __init__(      self				:							str     ,		__a				:							str=5_0_3_5_8     ,		__a				:							int=1_0_2_4     ,		__a				:							Optional[Any]=2_4     ,		__a				:							Any=1_6     ,		__a				:							int=4_0_9_6     ,		__a				:							Any="gelu"     ,		__a				:							Union[str, Any]=0.1     ,		__a				:							Any=0.1     ,		__a				:							Union[str, Any]=5_1_2     ,		__a				:							int=0.0_2     ,		__a				:							str=1e-12     ,		__a				:							List[str]=0     ,		__a				:							Optional[int]=2     ,		__a				:							Tuple=1     ,		__a				:							str="absolute"     ,		__a				:							Optional[Any]=True     ,		**__a				:							Tuple     ,		)							->	Any:
								super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			position_embedding_type
								__UpperCAmelCase							=			use_cache
