






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

__magic_name__:					Tuple							=     random.Random()

def    UpperCamelCase						(	_A,   _A=1.0,   _A=None,   _A=None			):






						"""simple docstring"""







						if rng is None:
												__magic_name__      :       Optional[Any]       						= global_rng

						__magic_name__      :       Union[str, Any]       						= []
						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				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=7      ,       lowerCAmelCase__=4_00      ,       lowerCAmelCase__=20_00      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=1_28      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=30      ,       lowerCAmelCase__=4_41_00      ,       )							->				List[Any]:
							__magic_name__      :       List[str]       						= parent
							__magic_name__      :       List[Any]       						= batch_size
							__magic_name__      :       Optional[Any]       						= min_seq_length
							__magic_name__      :       Tuple       						= max_seq_length
							__magic_name__      :       Union[str, Any]       						= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__magic_name__      :       List[str]       						= spectrogram_length
							__magic_name__      :       List[Any]       						= feature_size
							__magic_name__      :       Tuple       						= num_audio_channels
							__magic_name__      :       str       						= hop_length
							__magic_name__      :       Optional[int]       						= chunk_length
							__magic_name__      :       str       						= sampling_rate
	def       __magic_name__    (    self			)							->				List[Any]:
							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       __magic_name__    (    self      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=False			)							->				Dict:
							def _flatten(lowerCAmelCase__			):
													return list(itertools.chain(*lowerCAmelCase__			)			)

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





@require_torch
@require_torchaudio
class 	snake_case__ (							_lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					Optional[Any]											=   TvltFeatureExtractor
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       List[Any]       						= TvltFeatureExtractionTester(self			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       str       						= 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       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Union[str, Any]       						= self.feature_extraction_class(**self.feat_extract_dict			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__magic_name__      :       Dict       						= feat_extract_first.save_pretrained(lowerCAmelCase__			)[0]
													check_json_file_has_correct_format(lowerCAmelCase__			)
													__magic_name__      :       Any       						= self.feature_extraction_class.from_pretrained(lowerCAmelCase__			)

							__magic_name__      :       int       						= feat_extract_first.to_dict()
							__magic_name__      :       Dict       						= feat_extract_second.to_dict()
							__magic_name__      :       str       						= dict_first.pop("""mel_filters"""			)
							__magic_name__      :       str       						= dict_second.pop("""mel_filters"""			)
							self.assertTrue(np.allclose(lowerCAmelCase__      ,       lowerCAmelCase__			)			)
							self.assertEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       int       						= self.feature_extraction_class(**self.feat_extract_dict			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__magic_name__      :       Union[str, Any]       						= os.path.join(lowerCAmelCase__      ,       """feat_extract.json"""			)
													feat_extract_first.to_json_file(lowerCAmelCase__			)
													__magic_name__      :       int       						= self.feature_extraction_class.from_json_file(lowerCAmelCase__			)

							__magic_name__      :       str       						= feat_extract_first.to_dict()
							__magic_name__      :       Optional[Any]       						= feat_extract_second.to_dict()
							__magic_name__      :       Dict       						= dict_first.pop("""mel_filters"""			)
							__magic_name__      :       Optional[Any]       						= dict_second.pop("""mel_filters"""			)
							self.assertTrue(np.allclose(lowerCAmelCase__      ,       lowerCAmelCase__			)			)
							self.assertEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							# Initialize feature_extractor
							__magic_name__      :       Optional[int]       						= self.feature_extraction_class(**self.feat_extract_dict			)

							# create three inputs of length 800, 1000, and 1200
							__magic_name__      :       Tuple       						= [floats_list((1, x)			)[0] for x in range(8_00      ,       14_00      ,       2_00			)]
							__magic_name__      :       Union[str, Any]       						= [np.asarray(lowerCAmelCase__			) for speech_input in speech_inputs]

							# Test not batched input
							__magic_name__      :       Union[str, Any]       						= feature_extractor(np_speech_inputs[0]      ,       return_tensors="""np"""      ,       sampling_rate=4_41_00			).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
							__magic_name__      :       str       						= feature_extractor(lowerCAmelCase__      ,       return_tensors="""np"""      ,       sampling_rate=4_41_00			).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
							__magic_name__      :       Dict       						= feature_extractor(
							    lowerCAmelCase__      ,       return_tensors="""np"""      ,       sampling_rate=4_41_00      ,       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.
							__magic_name__      :       int       						= [floats_list((1, x)			)[0] for x in (8_00, 8_00, 8_00)]
							__magic_name__      :       Optional[Any]       						= np.asarray(lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= feature_extractor(lowerCAmelCase__      ,       return_tensors="""np"""      ,       sampling_rate=4_41_00			).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       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Optional[Any]       						= load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,       """clean"""      ,       split="""validation"""			)
							# automatic decoding with librispeech
							__magic_name__      :       int       						= ds.sort("""id"""			).select(range(lowerCAmelCase__			)			)[:num_samples]["""audio"""]

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







	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       List[str]       						= self._load_datasamples(1			)
							__magic_name__      :       List[str]       						= TvltFeatureExtractor()
							__magic_name__      :       Tuple       						= feature_extractor(lowerCAmelCase__      ,       return_tensors="""pt"""			).audio_values

							self.assertEquals(audio_values.shape      ,       (1, 1, 1_92, 1_28)			)

							__magic_name__      :       List[str]       						= 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]      ,       lowerCAmelCase__      ,       atol=1e-4			)			)








import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

							__magic_name__      :       List[Any]       						= -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1e-4			)
