




import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_snake_case										=       "\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

_snake_case										=       "\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

_snake_case										=       "\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,_KWARGS_DESCRIPTION		)
class 						lowercase							(   datasets.Metric		):

     def 					a__	(  self				)						->							Optional[Any]:
           return datasets.MetricInfo(
               description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
                   {
                       """predictions""": datasets.Sequence(datasets.Value("""string"""		,     id="""token"""				)		,     id="""sequence"""				),
                       """references""": datasets.Sequence(
                           datasets.Sequence(datasets.Value("""string"""		,     id="""token"""				)		,     id="""sequence"""				)		,     id="""references"""				),
                   }				)		,     codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]		,     reference_urls=[
                   """https://en.wikipedia.org/wiki/BLEU""",
                   """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
               ]		,     )



     def 					a__	(  self		,     _a		,     _a		,     _a=4		,     _a=False				)						->							Optional[int]:
           _A							:     Any			=						compute_bleu(
               reference_corpus=_snake_case		,     translation_corpus=_snake_case		,     max_order=_snake_case		,     smooth=_snake_case				)
           (_A)							:     Tuple			=						score
           return {
               "bleu": bleu,
               "precisions": precisions,
               "brevity_penalty": bp,
               "length_ratio": ratio,
               "translation_length": translation_length,
               "reference_length": reference_length,
           }






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


a_   :Any        =      random.Random()
def        lowercase_							(A : int		,					A : Union[str, Any]=1.0		,					A : List[str]=None		,					A : Any=None      ):
       if rng is None:
              snake_case__							:	List[str]   	=	global_rng

       snake_case__							:	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  :  Optional[Any], _snake_case  :  List[str], _snake_case  :  Tuple=7, _snake_case  :  Union[str, Any]=4_0_0, _snake_case  :  Any=2_0_0_0, _snake_case  :  Dict=1, _snake_case  :  Optional[Any]=0.0, _snake_case  :  List[Any]=1_6_0_0_0, _snake_case  :  List[Any]=True, _snake_case  :  List[Any]=8_0, _snake_case  :  Dict=1_6, _snake_case  :  str=6_4, _snake_case  :  Tuple="hann_window", _snake_case  :  Union[str, Any]=8_0, _snake_case  :  Optional[Any]=7_6_0_0, _snake_case  :  str=1e-10, _snake_case  :  Any=True, )   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	parent
            snake_case__							:	Optional[Any]   	=	batch_size
            snake_case__							:	List[Any]   	=	min_seq_length
            snake_case__							:	List[Any]   	=	max_seq_length
            snake_case__							:	Any   	=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            snake_case__							:	Tuple   	=	feature_size
            snake_case__							:	List[Any]   	=	padding_value
            snake_case__							:	Any   	=	sampling_rate
            snake_case__							:	Dict   	=	do_normalize
            snake_case__							:	Union[str, Any]   	=	num_mel_bins
            snake_case__							:	Any   	=	hop_length
            snake_case__							:	Any   	=	win_length
            snake_case__							:	Any   	=	win_function
            snake_case__							:	Optional[int]   	=	fmin
            snake_case__							:	int   	=	fmax
            snake_case__							:	Union[str, Any]   	=	mel_floor
            snake_case__							:	Union[str, Any]   	=	return_attention_mask






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

            if equal_length:
                   snake_case__							:	Any   	=	floats_list((self.batch_size, self.max_seq_length)					)
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	int   	=	[
                       _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:
                   snake_case__							:	Any   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str=False, _snake_case  :  Dict=False					)   ->List[str]:
            if equal_length:
                   snake_case__							:	Optional[Any]   	=	[floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	List[str]   	=	[
                       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:
                   snake_case__							:	int   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs




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






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	SpeechTaFeatureExtractor






     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	List[str]   	=	SpeechTaFeatureExtractionTester(self					)






     def    lowercase_   (       self  :  Any, _snake_case  :  Dict					)   ->Any:
            self.assertTrue(np.all(np.mean(_snake_case, axis=0					) < 1e-3					)					)
            self.assertTrue(np.all(np.abs(np.var(_snake_case, axis=0					) - 1					) < 1e-3					)					)






     def    lowercase_   (       self  :  List[Any]					)   ->Union[str, Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	int   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Tuple   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test not batched input
            snake_case__							:	str   	=	feat_extract(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	List[str]   	=	feat_extract(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Any   	=	feat_extract(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Union[str, Any]   	=	feat_extract(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)






     def    lowercase_   (       self  :  int					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]

            snake_case__							:	int   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	List[str]   	=	[None, 1_6_0_0, None]
            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	Optional[int]   	=	feat_extract(_snake_case, padding=_snake_case, max_length=_snake_case, return_tensors='np'					)
                   snake_case__							:	Optional[int]   	=	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    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[Any]:
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	range(8_0_0, 1_4_0_0, 2_0_0					)
            snake_case__							:	Optional[Any]   	=	[floats_list((1, x)					)[0] for x in lengths]

            snake_case__							:	Union[str, Any]   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	str   	=	[None, 1_6_0_0, None]

            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	List[str]   	=	feat_extract(_snake_case, max_length=_snake_case, padding=_snake_case					)
                   snake_case__							:	Tuple   	=	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    lowercase_   (       self  :  List[Any]					)   ->Optional[Any]:
            snake_case__							:	Any   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	str   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Optional[Any]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='max_length', return_tensors='np'					)
            snake_case__							:	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    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Dict   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	str   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	Dict   	=	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)					)

            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	List[str]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=2_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	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]					)

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






     def    lowercase_   (       self  :  List[str]					)   ->Dict:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	List[Any]   	=	np.random.rand(1_0_0					).astype(np.floataa					)
            snake_case__							:	int   	=	np_speech_inputs.tolist()

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






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	List[Any]   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Dict   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test feature size
            snake_case__							:	Optional[int]   	=	feature_extractor(audio_target=_snake_case, padding=_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
            snake_case__							:	Dict   	=	feature_extractor(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	Any   	=	feature_extractor(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Dict   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Dict   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

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






     def    lowercase_   (       self  :  Union[str, Any]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[str]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Tuple   	=	BatchFeature({input_name: speech_inputs}					)

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

            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	BatchFeature({input_name: speech_inputs}, tensor_type='np'					)

            snake_case__							:	Dict   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	List[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    lowercase_   (       self  :  List[str]					)   ->Any:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Tuple   	=	feat_extract.model_input_names[0]

            snake_case__							:	List[Any]   	=	BatchFeature({input_name: speech_inputs}, tensor_type='pt'					)

            snake_case__							:	Tuple   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	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    lowercase_   (       self  :  Optional[int]					)   ->Tuple:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Union[str, Any]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	List[str]   	=	BatchFeature({input_name: speech_inputs}					)

            snake_case__							:	int   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='np'					)[input_name]
            snake_case__							:	Union[str, Any]   	=	feat_extract.pad(_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    lowercase_   (       self  :  int					)   ->Any:
            snake_case__							:	Any   	=	self.feat_extract_dict
            snake_case__							:	List[Any]   	=	True
            snake_case__							:	Union[str, Any]   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	Any   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[Any]   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Union[str, Any]   	=	feat_extract.model_input_names[0]

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

            snake_case__							:	List[str]   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	str   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='np'					)
            self.assertIn('attention_mask', _snake_case					)
            self.assertListEqual(list(processed.attention_mask.shape					), list(processed[input_name].shape[:2]					)					)
            self.assertListEqual(processed.attention_mask.sum(-1					).tolist(), _snake_case					)






     def    lowercase_   (       self  :  Optional[int]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_dict
            snake_case__							:	List[str]   	=	True
            snake_case__							:	Tuple   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	List[str]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	str   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Optional[int]   	=	BatchFeature({input_name: speech_inputs}					)
            snake_case__							:	Optional[Any]   	=	min(_snake_case					)

            snake_case__							:	Union[str, Any]   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(
                _snake_case, padding='max_length', max_length=_snake_case, truncation=_snake_case, return_tensors='np'					)
            self.assertIn('attention_mask', _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    lowercase_   (       self  :  List[Any], _snake_case  :  Optional[int]					)   ->Optional[Any]:
            from datasets import load_dataset

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

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






     def    lowercase_   (       self  :  str					)   ->str:
            # fmt: off
            snake_case__							:	List[Any]   	=	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

            snake_case__							:	Union[str, Any]   	=	self._load_datasamples(1					)
            snake_case__							:	Optional[int]   	=	SpeechTaFeatureExtractor()
            snake_case__							:	List[Any]   	=	feature_extractor(_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], _snake_case, atol=1e-6					)					)






     def    lowercase_   (       self  :  Any					)   ->str:
            # fmt: off
            snake_case__							:	Optional[Any]   	=	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

            snake_case__							:	List[str]   	=	self._load_datasamples(1					)
            snake_case__							:	str   	=	SpeechTaFeatureExtractor()
            snake_case__							:	Optional[Any]   	=	feature_extractor(audio_target=_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], _snake_case, atol=1e-4					)					)






