



"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


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

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       List[str]=1.0						,       _lowercase    :       Any=None						,       _lowercase    :       Optional[Any]=None)							->	Optional[Any]:







			"""simple docstring"""
			if rng is None:
						a__       :						Any							=  global_rng

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

			return values

@require_torch
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase=7 ,       __lowercase=4_0_0 ,       __lowercase=2_0_0_0 ,       __lowercase=1 ,       __lowercase=0.0 ,       __lowercase=1_6_0_0_0 ,       __lowercase=True ,       __lowercase=8_0 ,       __lowercase=1_6 ,       __lowercase=6_4 ,       __lowercase="hann_window" ,       __lowercase=8_0 ,       __lowercase=7_6_0_0 ,       __lowercase=1E-10 ,       __lowercase=True ,       ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[Any]							=  parent
									a__       :						Any							=  batch_size
									a__       :						List[Any]							=  min_seq_length
									a__       :						List[str]							=  max_seq_length
									a__       :						Union[str, Any]							=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a__       :						Tuple							=  feature_size
									a__       :						int							=  padding_value
									a__       :						Dict							=  sampling_rate
									a__       :						Tuple							=  do_normalize
									a__       :						List[Any]							=  num_mel_bins
									a__       :						Dict							=  hop_length
									a__       :						Optional[Any]							=  win_length
									a__       :						Union[str, Any]							=  win_function
									a__       :						Optional[int]							=  fmin
									a__       :						str							=  fmax
									a__       :						Optional[int]							=  mel_floor
									a__       :						Any							=  return_attention_mask

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "do_normalize": self.do_normalize,
									    "num_mel_bins": self.num_mel_bins,
									    "hop_length": self.hop_length,
									    "win_length": self.win_length,
									    "win_function": self.win_function,
									    "fmin": self.fmin,
									    "fmax": self.fmax,
									    "mel_floor": self.mel_floor,
									    "return_attention_mask": self.return_attention_mask,
									}

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=False ,       __lowercase=False  ) ->				Optional[Any]:
									"""simple docstring"""




									def _flatten(__lowercase  ):
												return list(itertools.chain(*SCREAMING_SNAKE_CASE_  )  )

									if equal_length:
												a__       :						Tuple							=  floats_list((self.batch_size, self.max_seq_length)  )
									else:
												# make sure that inputs increase in size
												a__       :						Any							=  [
												    _flatten(floats_list((x, self.feature_size)  )  )
												    for x in range(self.min_seq_length ,       self.max_seq_length ,       self.seq_length_diff  )
												]

									if numpify:
												a__       :						Tuple							=  [np.asarray(SCREAMING_SNAKE_CASE_  ) for x in speech_inputs]

									return speech_inputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=False ,       __lowercase=False  ) ->				Any:
									"""simple docstring"""




									if equal_length:
												a__       :						List[str]							=  [floats_list((self.max_seq_length, self.num_mel_bins)  ) for _ in range(self.batch_size  )]
									else:
												# make sure that inputs increase in size
												a__       :						List[Any]							=  [
												    floats_list((x, self.num_mel_bins)  )
												    for x in range(self.min_seq_length ,       self.max_seq_length ,       self.seq_length_diff  )
												]

									if numpify:
												a__       :						Tuple							=  [np.asarray(SCREAMING_SNAKE_CASE_  ) for x in speech_inputs]

									return speech_inputs




@require_torch
class        snake_case__    (_UpperCAmelCase ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:List[Any]      =					SpeechTaFeatureExtractor

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




									a__       :						Optional[Any]							=  SpeechTaFeatureExtractionTester(self  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




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

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




									a__       :						List[Any]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									# create three inputs of length 800, 1000, and 1200
									a__       :						Dict							=  [floats_list((1, x)  )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0  )]
									a__       :						Optional[int]							=  [np.asarray(SCREAMING_SNAKE_CASE_  ) for speech_input in speech_inputs]

									# Test not batched input
									a__       :						Optional[Any]							=  feat_extract(speech_inputs[0] ,       return_tensors="""np"""  ).input_values
									a__       :						Dict							=  feat_extract(np_speech_inputs[0] ,       return_tensors="""np"""  ).input_values
									self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_ ,       atol=1E-3  )  )

									# Test batched
									a__       :						Optional[Any]							=  feat_extract(SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  ).input_values
									a__       :						Dict							=  feat_extract(SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  ).input_values
									for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_  ):
												self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_ ,       atol=1E-3  )  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a__       :						Any							=  [floats_list((1, x)  )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0  )]

									a__       :						List[Any]							=  ["""longest""", """max_length""", """do_not_pad"""]
									a__       :						Union[str, Any]							=  [None, 1_6_0_0, None]
									for max_length, padding in zip(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_  ):
												a__       :						Optional[int]							=  feat_extract(SCREAMING_SNAKE_CASE_ ,       padding=SCREAMING_SNAKE_CASE_ ,       max_length=SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  )
												a__       :						Dict							=  processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:8_0_0]  )
												self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6  )
												self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]  )
												self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6  )
												self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Tuple							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a__       :						Optional[Any]							=  range(8_0_0 ,       1_4_0_0 ,       2_0_0  )
									a__       :						Any							=  [floats_list((1, x)  )[0] for x in lengths]

									a__       :						int							=  ["""longest""", """max_length""", """do_not_pad"""]
									a__       :						str							=  [None, 1_6_0_0, None]

									for max_length, padding in zip(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_  ):
												a__       :						Any							=  feat_extract(SCREAMING_SNAKE_CASE_ ,       max_length=SCREAMING_SNAKE_CASE_ ,       padding=SCREAMING_SNAKE_CASE_  )
												a__       :						Optional[Any]							=  processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:8_0_0]  )
												self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]  )
												self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						List[str]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a__       :						Union[str, Any]							=  [floats_list((1, x)  )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0  )]
									a__       :						str							=  feat_extract(
									    SCREAMING_SNAKE_CASE_ ,       truncation=SCREAMING_SNAKE_CASE_ ,       max_length=1_0_0_0 ,       padding="""max_length""" ,       return_tensors="""np"""  )
									a__       :						int							=  processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :8_0_0]  )
									self._check_zero_mean_unit_variance(input_values[1]  )
									self._check_zero_mean_unit_variance(input_values[2]  )

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




									a__       :						Any							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a__       :						int							=  [floats_list((1, x)  )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0  )]
									a__       :						str							=  feat_extract(
									    SCREAMING_SNAKE_CASE_ ,       truncation=SCREAMING_SNAKE_CASE_ ,       max_length=1_0_0_0 ,       padding="""longest""" ,       return_tensors="""np"""  )
									a__       :						Optional[int]							=  processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :8_0_0]  )
									self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]  )
									self._check_zero_mean_unit_variance(input_values[2]  )

									# make sure that if max_length < longest -> then pad to max_length
									self.assertTrue(input_values.shape == (3, 1_0_0_0)  )

									a__       :						Any							=  [floats_list((1, x)  )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0  )]
									a__       :						Dict							=  feat_extract(
									    SCREAMING_SNAKE_CASE_ ,       truncation=SCREAMING_SNAKE_CASE_ ,       max_length=2_0_0_0 ,       padding="""longest""" ,       return_tensors="""np"""  )
									a__       :						Any							=  processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :8_0_0]  )
									self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]  )
									self._check_zero_mean_unit_variance(input_values[2]  )

									# make sure that if max_length > longest -> then pad to longest
									self.assertTrue(input_values.shape == (3, 1_2_0_0)  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a__       :						Dict							=  np.random.rand(1_0_0  ).astype(np.floataa  )
									a__       :						Any							=  np_speech_inputs.tolist()

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

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




									a__       :						Dict							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									# create three inputs of length 800, 1000, and 1200
									a__       :						Any							=  [floats_list((1, x)  )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0  )]
									a__       :						Tuple							=  [np.asarray(SCREAMING_SNAKE_CASE_  ) for speech_input in speech_inputs]

									# Test feature size
									a__       :						Optional[Any]							=  feature_extractor(audio_target=SCREAMING_SNAKE_CASE_ ,       padding=SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  ).input_values
									self.assertTrue(input_values.ndim == 3  )
									self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins  )

									# Test not batched input
									a__       :						List[str]							=  feature_extractor(speech_inputs[0] ,       return_tensors="""np"""  ).input_values
									a__       :						List[str]							=  feature_extractor(np_speech_inputs[0] ,       return_tensors="""np"""  ).input_values
									self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_ ,       atol=1E-3  )  )

									# Test batched
									a__       :						int							=  feature_extractor(SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  ).input_values
									a__       :						Tuple							=  feature_extractor(SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  ).input_values
									for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_  ):
												self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_ ,       atol=1E-3  )  )

									# Test 2-D numpy arrays are batched.
									a__       :						Optional[Any]							=  [floats_list((1, x)  )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
									a__       :						int							=  np.asarray(SCREAMING_SNAKE_CASE_  )
									a__       :						List[Any]							=  feature_extractor(SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  ).input_values
									a__       :						List[str]							=  feature_extractor(SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  ).input_values
									for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_  ):
												self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_ ,       atol=1E-3  )  )

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




									a__       :						Dict							=  self.feat_extract_tester.prepare_inputs_for_target()
									a__       :						Any							=  self.feature_extraction_class(**self.feat_extract_dict  )
									a__       :						List[str]							=  feat_extract.model_input_names[0]

									a__       :						Optional[int]							=  BatchFeature({input_name: speech_inputs}  )

									self.assertTrue(all(len(SCREAMING_SNAKE_CASE_  ) == len(SCREAMING_SNAKE_CASE_  ) for x, y in zip(SCREAMING_SNAKE_CASE_ ,       processed_features[input_name]  )  )  )

									a__       :						str							=  self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE_  )
									a__       :						List[str]							=  BatchFeature({input_name: speech_inputs} ,       tensor_type="""np"""  )

									a__       :						str							=  processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
												a__       :						Union[str, Any]							=  batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.num_mel_bins)  )

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




									a__       :						Optional[int]							=  self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE_  )
									a__       :						List[Any]							=  self.feature_extraction_class(**self.feat_extract_dict  )
									a__       :						List[Any]							=  feat_extract.model_input_names[0]

									a__       :						Any							=  BatchFeature({input_name: speech_inputs} ,       tensor_type="""pt"""  )

									a__       :						Dict							=  processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
												a__       :						str							=  batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.num_mel_bins)  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Any							=  self.feature_extraction_class(**self.feat_extract_dict  )
									a__       :						int							=  self.feat_extract_tester.prepare_inputs_for_target()
									a__       :						str							=  feat_extract.model_input_names[0]

									a__       :						str							=  BatchFeature({input_name: speech_inputs}  )

									a__       :						Optional[int]							=  feat_extract.num_mel_bins  # hack!

									a__       :						int							=  feat_extract.pad(SCREAMING_SNAKE_CASE_ ,       padding="""longest""" ,       return_tensors="""np"""  )[input_name]
									a__       :						Dict							=  feat_extract.pad(SCREAMING_SNAKE_CASE_ ,       padding="""longest""" ,       return_tensors="""pt"""  )[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa  ).sum() - input_pt.numpy().astype(np.floataa  ).sum()  ) < 1E-2  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						List[Any]							=  self.feat_extract_dict
									a__       :						List[Any]							=  True
									a__       :						Optional[Any]							=  self.feature_extraction_class(**SCREAMING_SNAKE_CASE_  )
									a__       :						Optional[Any]							=  self.feat_extract_tester.prepare_inputs_for_target()
									a__       :						str							=  [len(SCREAMING_SNAKE_CASE_  ) for x in speech_inputs]
									a__       :						str							=  feat_extract.model_input_names[0]

									a__       :						List[Any]							=  BatchFeature({input_name: speech_inputs}  )

									a__       :						Tuple							=  feat_extract.num_mel_bins  # hack!

									a__       :						Optional[int]							=  feat_extract.pad(SCREAMING_SNAKE_CASE_ ,       padding="""longest""" ,       return_tensors="""np"""  )
									self.assertIn("""attention_mask""" ,       SCREAMING_SNAKE_CASE_  )
									self.assertListEqual(list(processed.attention_mask.shape  ) ,       list(processed[input_name].shape[:2]  )  )
									self.assertListEqual(processed.attention_mask.sum(-1  ).tolist() ,       SCREAMING_SNAKE_CASE_  )

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




									a__       :						Optional[Any]							=  self.feat_extract_dict
									a__       :						Union[str, Any]							=  True
									a__       :						List[str]							=  self.feature_extraction_class(**SCREAMING_SNAKE_CASE_  )
									a__       :						Dict							=  self.feat_extract_tester.prepare_inputs_for_target()
									a__       :						Any							=  [len(SCREAMING_SNAKE_CASE_  ) for x in speech_inputs]
									a__       :						int							=  feat_extract.model_input_names[0]

									a__       :						Any							=  BatchFeature({input_name: speech_inputs}  )
									a__       :						str							=  min(SCREAMING_SNAKE_CASE_  )

									a__       :						Tuple							=  feat_extract.num_mel_bins  # hack!

									a__       :						Optional[Any]							=  feat_extract.pad(
									    SCREAMING_SNAKE_CASE_ ,       padding="""max_length""" ,       max_length=SCREAMING_SNAKE_CASE_ ,       truncation=SCREAMING_SNAKE_CASE_ ,       return_tensors="""np"""  )
									self.assertIn("""attention_mask""" ,       SCREAMING_SNAKE_CASE_  )
									self.assertListEqual(
									    list(processed_pad.attention_mask.shape  ) ,       [processed_pad[input_name].shape[0], max_length]  )
									self.assertListEqual(
									    processed_pad.attention_mask[:, :max_length].sum(-1  ).tolist() ,       [max_length for x in speech_inputs]  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Dict:
									"""simple docstring"""




									from datasets import load_dataset

									a__       :						List[str]							=  load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,       """clean""" ,       split="""validation"""  )
									# automatic decoding with librispeech
									a__       :						Union[str, Any]							=  ds.sort("""id"""  ).select(range(SCREAMING_SNAKE_CASE_  )  )[:num_samples]["""audio"""]

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

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




									a__       :						int							=  torch.tensor(
									    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
									     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
									     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
									     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
									     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
									     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]  )
									# fmt: on

									a__       :						List[Any]							=  self._load_datasamples(1  )
									a__       :						int							=  SpeechTaFeatureExtractor()
									a__       :						str							=  feature_extractor(SCREAMING_SNAKE_CASE_ ,       return_tensors="""pt"""  ).input_values
									self.assertEquals(input_values.shape ,       (1, 9_3_6_8_0)  )
									self.assertTrue(torch.allclose(input_values[0, :3_0] ,       SCREAMING_SNAKE_CASE_ ,       atol=1E-6  )  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Dict							=  torch.tensor(
									    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
									     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
									     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
									     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]  )
									# fmt: on

									a__       :						Tuple							=  self._load_datasamples(1  )
									a__       :						Optional[Any]							=  SpeechTaFeatureExtractor()
									a__       :						Optional[int]							=  feature_extractor(audio_target=SCREAMING_SNAKE_CASE_ ,       return_tensors="""pt"""  ).input_values
									self.assertEquals(input_values.shape ,       (1, 3_6_6, 8_0)  )
									self.assertTrue(torch.allclose(input_values[0, 0, :3_0] ,       SCREAMING_SNAKE_CASE_ ,       atol=1E-4  )  )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowercase		:				int      		={
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Optional[Any]      		=[
		    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "LongT5EncoderModel",
		    "LongT5ForConditionalGeneration",
		    "LongT5Model",
		    "LongT5PreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				List[Any]      		=[
		    "FlaxLongT5ForConditionalGeneration",
		    "FlaxLongT5Model",
		    "FlaxLongT5PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longta import (
				    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongTaEncoderModel,
				    LongTaForConditionalGeneration,
				    LongTaModel,
				    LongTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_longta import (
				    FlaxLongTaForConditionalGeneration,
				    FlaxLongTaModel,
				    FlaxLongTaPreTrainedModel,
				)


else:
		import sys

		_lowercase		:				Any      		=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




