






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor

__UpperCamelCase   :   Tuple      =    random.Random()





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase=1.0					,	lowerCamelCase=None					,	lowerCamelCase=None       ):




	'''simple docstring'''







	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


@require_torch
@require_torchaudio
class 							__UpperCamelCase     (  unittest.TestCase ):







	def __init__(	self   :				List[str]							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				Any=7							,					_lowerCAmelCase   :				Dict=400							,					_lowerCAmelCase   :				Any=2000							,					_lowerCAmelCase   :				Dict=24							,					_lowerCAmelCase   :				Optional[int]=24							,					_lowerCAmelCase   :				Any=0.0							,					_lowerCAmelCase   :				int=1_6000							,					_lowerCAmelCase   :				Any=True							,					_lowerCAmelCase   :				List[Any]=True							,					)		->			str:
		"""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   							=	feature_size
		__lowercase   							=	num_mel_bins
		__lowercase   							=	padding_value
		__lowercase   							=	sampling_rate
		__lowercase   							=	return_attention_mask
		__lowercase   							=	do_normalize







	def 			_a (	self   :				List[str]      )		->			str:
		"""simple docstring"""







		return {
		    "feature_size": self.feature_size,
		    "num_mel_bins": self.num_mel_bins,
		    "padding_value": self.padding_value,
		    "sampling_rate": self.sampling_rate,
		    "return_attention_mask": self.return_attention_mask,
		    "do_normalize": self.do_normalize,
		}







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Tuple=False							,					_lowerCAmelCase   :				str=False      )		->			Union[str, Any]:
		"""simple docstring"""







		def _flatten(_lowerCAmelCase   :				Optional[Any]      ):
			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 							__UpperCamelCase     (  _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[Any] =							SpeechaTextFeatureExtractor if is_speech_available() else None







	def 			_a (	self   :				Union[str, Any]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	SpeechaTextFeatureExtractionTester(self      )







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				List[Any]      )		->			Tuple:
		"""simple docstring"""







		self.assertTrue(np.all(np.mean(_lowerCAmelCase							,					axis=0      ) < 1e-3      )      )
		self.assertTrue(np.all(np.abs(np.var(_lowerCAmelCase							,					axis=0      ) - 1      ) < 1e-3      )      )







	def 			_a (	self   :				int      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
		# create three inputs of length 800, 1000, and 1200
		__lowercase   							=	[floats_list((1, x)      )[0] for x in range(800							,					1400							,					200      )]
		__lowercase   							=	[np.asarray(_lowerCAmelCase      ) for speech_input in speech_inputs]

		# Test feature size
		__lowercase   							=	feature_extractor(_lowerCAmelCase							,					padding=_lowerCAmelCase							,					return_tensors="""np"""      ).input_features
		self.assertTrue(input_features.ndim == 3      )
		self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size      )

		# Test not batched input
		__lowercase   							=	feature_extractor(speech_inputs[0]							,					return_tensors="""np"""      ).input_features
		__lowercase   							=	feature_extractor(np_speech_inputs[0]							,					return_tensors="""np"""      ).input_features
		self.assertTrue(np.allclose(_lowerCAmelCase							,					_lowerCAmelCase							,					atol=1e-3      )      )

		# Test batched
		__lowercase   							=	feature_extractor(_lowerCAmelCase							,					return_tensors="""np"""      ).input_features
		__lowercase   							=	feature_extractor(_lowerCAmelCase							,					return_tensors="""np"""      ).input_features
		for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase							,					_lowerCAmelCase      ):
			self.assertTrue(np.allclose(_lowerCAmelCase							,					_lowerCAmelCase							,					atol=1e-3      )      )

		# 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"""      ).input_features
		__lowercase   							=	feature_extractor(_lowerCAmelCase							,					return_tensors="""np"""      ).input_features
		for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase							,					_lowerCAmelCase      ):
			self.assertTrue(np.allclose(_lowerCAmelCase							,					_lowerCAmelCase							,					atol=1e-3      )      )







	def 			_a (	self   :				Union[str, Any]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
		__lowercase   							=	[floats_list((1, x)      )[0] for x in range(800							,					1400							,					200      )]

		__lowercase   							=	["""longest""", """max_length""", """do_not_pad"""]
		__lowercase   							=	[None, 16, None]
		for max_length, padding in zip(_lowerCAmelCase							,					_lowerCAmelCase      ):
			__lowercase   							=	feature_extractor(
			    _lowerCAmelCase							,					padding=_lowerCAmelCase							,					max_length=_lowerCAmelCase							,					return_attention_mask=_lowerCAmelCase      )
			__lowercase   							=	inputs.input_features
			__lowercase   							=	inputs.attention_mask
			__lowercase   							=	[np.sum(_lowerCAmelCase      ) for x in attention_mask]

			self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]      )
			self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]      )
			self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]      )







	def 			_a (	self   :				Any      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
		__lowercase   							=	[floats_list((1, x)      )[0] for x in range(800							,					1400							,					200      )]

		__lowercase   							=	["""longest""", """max_length""", """do_not_pad"""]
		__lowercase   							=	[None, 16, None]
		for max_length, padding in zip(_lowerCAmelCase							,					_lowerCAmelCase      ):
			__lowercase   							=	feature_extractor(
			    _lowerCAmelCase							,					max_length=_lowerCAmelCase							,					padding=_lowerCAmelCase							,					return_tensors="""np"""							,					return_attention_mask=_lowerCAmelCase      )
			__lowercase   							=	inputs.input_features
			__lowercase   							=	inputs.attention_mask
			__lowercase   							=	[np.sum(_lowerCAmelCase      ) for x in attention_mask]

			self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]      )
			self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6      )
			self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]      )
			self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6      )
			self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]      )







	def 			_a (	self   :				Any      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
		__lowercase   							=	[floats_list((1, x)      )[0] for x in range(800							,					1400							,					200      )]
		__lowercase   							=	feature_extractor(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=4							,					truncation=_lowerCAmelCase							,					return_tensors="""np"""							,					return_attention_mask=_lowerCAmelCase							,					)
		__lowercase   							=	inputs.input_features
		__lowercase   							=	inputs.attention_mask
		__lowercase   							=	np.sum(attention_mask == 1							,					axis=1      )

		self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]      )
		self._check_zero_mean_unit_variance(input_features[1]      )
		self._check_zero_mean_unit_variance(input_features[2]      )







	def 			_a (	self   :				Optional[int]      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
		__lowercase   							=	[floats_list((1, x)      )[0] for x in range(800							,					1400							,					200      )]
		__lowercase   							=	feature_extractor(
		    _lowerCAmelCase							,					padding="""longest"""							,					max_length=4							,					truncation=_lowerCAmelCase							,					return_tensors="""np"""							,					return_attention_mask=_lowerCAmelCase							,					)
		__lowercase   							=	inputs.input_features
		__lowercase   							=	inputs.attention_mask
		__lowercase   							=	np.sum(attention_mask == 1							,					axis=1      )

		self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]      )
		self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]      )
		self._check_zero_mean_unit_variance(input_features[2]      )

		# make sure that if max_length < longest -> then pad to max_length
		self.assertEqual(input_features.shape							,					(3, 4, 24)      )

		__lowercase   							=	[floats_list((1, x)      )[0] for x in range(800							,					1400							,					200      )]
		__lowercase   							=	feature_extractor(
		    _lowerCAmelCase							,					padding="""longest"""							,					max_length=16							,					truncation=_lowerCAmelCase							,					return_tensors="""np"""							,					return_attention_mask=_lowerCAmelCase							,					)
		__lowercase   							=	inputs.input_features
		__lowercase   							=	inputs.attention_mask
		__lowercase   							=	np.sum(attention_mask == 1							,					axis=1      )

		self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]      )
		self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]      )
		self._check_zero_mean_unit_variance(input_features[2]      )

		# make sure that if max_length < longest -> then pad to max_length
		self.assertEqual(input_features.shape							,					(3, 6, 24)      )







	def 			_a (	self   :				Dict      )		->			Union[str, Any]:
		"""simple docstring"""







		import torch

		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
		__lowercase   							=	np.random.rand(100							,					32      ).astype(np.floataa      )
		__lowercase   							=	np_speech_inputs.tolist()

		for inputs in [py_speech_inputs, np_speech_inputs]:
			__lowercase   							=	feature_extractor.pad([{"""input_features""": inputs}]							,					return_tensors="""np"""      )
			self.assertTrue(np_processed.input_features.dtype == np.floataa      )
			__lowercase   							=	feature_extractor.pad([{"""input_features""": inputs}]							,					return_tensors="""pt"""      )
			self.assertTrue(pt_processed.input_features.dtype == torch.floataa      )







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				Optional[int]      )		->			Union[str, Any]:
		"""simple docstring"""







		from datasets import load_dataset

		__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 			_a (	self   :				int      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	np.array([
		    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
		    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
		    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
		]      )
		# fmt: on

		__lowercase   							=	self._load_datasamples(1      )
		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
		__lowercase   							=	feature_extractor(_lowerCAmelCase							,					return_tensors="""pt"""      ).input_features
		self.assertEquals(input_features.shape							,					(1, 584, 24)      )
		self.assertTrue(np.allclose(input_features[0, 0, :30]							,					_lowerCAmelCase							,					atol=1e-4      )      )



"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''SpeechT5Tokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SpeechT5ForSpeechToText''',
       '''SpeechT5ForSpeechToSpeech''',
       '''SpeechT5ForTextToSpeech''',
       '''SpeechT5Model''',
       '''SpeechT5PreTrainedModel''',
       '''SpeechT5HifiGan''',
   ]

if TYPE_CHECKING:
   from .configuration_speechta import (
       SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
       SpeechTaConfig,
       SpeechTaHifiGanConfig,
   )
   from .feature_extraction_speechta import SpeechTaFeatureExtractor
   from .processing_speechta import SpeechTaProcessor

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_speechta import SpeechTaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speechta import (
          SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechTaForSpeechToSpeech,
          SpeechTaForSpeechToText,
          SpeechTaForTextToSpeech,
          SpeechTaHifiGan,
          SpeechTaModel,
          SpeechTaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



