


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


_lowerCamelCase			       =      random.Random()




def    __UpperCAmelCase(      lowercase_ ,    lowercase_=1.0 ,    lowercase_=None ,    lowercase_=None      ):




       if rng is None:
              _lowerCamelCase							:     Optional[int]	=		global_rng

       _lowerCamelCase							:     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 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=7      ,							a__=400      ,							a__=2000      ,							a__=1      ,							a__=0.0      ,							a__=1_6000      ,							a__=True      ,							a__=80      ,							a__=16      ,							a__=64      ,							a__="hann_window"      ,							a__=80      ,							a__=7600      ,							a__=1e-10      ,							a__=True      ,							):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """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 				__snake_case			(		self      ,							a__=False      ,							a__=False):



             """simple docstring"""





             def _flatten(a__):
                    return list(itertools.chain(*a__))

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

             return speech_inputs



      def 				__snake_case			(		self      ,							a__=False      ,							a__=False):



             """simple docstring"""





             if equal_length:
                    _lowerCamelCase							:     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
                    _lowerCamelCase							:     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:
                    _lowerCamelCase							:     Any	=		[np.asarray(a__) for x in speech_inputs]

             return speech_inputs







@require_torch
class 			__A       (      UpperCamelCase__ ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      SpeechTaFeatureExtractor


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		SpeechTaFeatureExtractionTester(self)


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             # create three inputs of length 800, 1000, and 1200
             _lowerCamelCase							:     List[Any]	=		[floats_list((1, x))[0] for x in range(800      ,							1400      ,							200)]
             _lowerCamelCase							:     Any	=		[np.asarray(a__) for speech_input in speech_inputs]

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             _lowerCamelCase							:     Any	=		[floats_list((1, x))[0] for x in range(800      ,							1400      ,							200)]

             _lowerCamelCase							:     Any	=		['''longest''', '''max_length''', '''do_not_pad''']
             _lowerCamelCase							:     List[str]	=		[None, 1600, None]
             for max_length, padding in zip(a__      ,							a__):
                    _lowerCamelCase							:     Any	=		feat_extract(a__      ,							padding=a__      ,							max_length=a__      ,							return_tensors='''np''')
                    _lowerCamelCase							:     Union[str, Any]	=		processed.input_values

                    self._check_zero_mean_unit_variance(input_values[0][:800])
                    self.assertTrue(input_values[0][800:].sum() < 1e-6)
                    self._check_zero_mean_unit_variance(input_values[1][:1000])
                    self.assertTrue(input_values[0][1000:].sum() < 1e-6)
                    self._check_zero_mean_unit_variance(input_values[2][:1200])


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             _lowerCamelCase							:     Optional[Any]	=		range(800      ,							1400      ,							200)
             _lowerCamelCase							:     Any	=		[floats_list((1, x))[0] for x in lengths]

             _lowerCamelCase							:     Tuple	=		['''longest''', '''max_length''', '''do_not_pad''']
             _lowerCamelCase							:     Dict	=		[None, 1600, None]

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

                    self._check_zero_mean_unit_variance(input_values[0][:800])
                    self._check_zero_mean_unit_variance(input_values[1][:1000])
                    self._check_zero_mean_unit_variance(input_values[2][:1200])


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             _lowerCamelCase							:     Tuple	=		[floats_list((1, x))[0] for x in range(800      ,							1400      ,							200)]
             _lowerCamelCase							:     str	=		feat_extract(
                 a__      ,							truncation=a__      ,							max_length=1000      ,							padding='''max_length'''      ,							return_tensors='''np''')
             _lowerCamelCase							:     Union[str, Any]	=		processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :800])
             self._check_zero_mean_unit_variance(input_values[1])
             self._check_zero_mean_unit_variance(input_values[2])


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             _lowerCamelCase							:     str	=		[floats_list((1, x))[0] for x in range(800      ,							1400      ,							200)]
             _lowerCamelCase							:     int	=		feat_extract(
                 a__      ,							truncation=a__      ,							max_length=1000      ,							padding='''longest'''      ,							return_tensors='''np''')
             _lowerCamelCase							:     Optional[int]	=		processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :800])
             self._check_zero_mean_unit_variance(input_values[1, :1000])
             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, 1000))

             _lowerCamelCase							:     Optional[Any]	=		[floats_list((1, x))[0] for x in range(800      ,							1400      ,							200)]
             _lowerCamelCase							:     Union[str, Any]	=		feat_extract(
                 a__      ,							truncation=a__      ,							max_length=2000      ,							padding='''longest'''      ,							return_tensors='''np''')
             _lowerCamelCase							:     Optional[int]	=		processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :800])
             self._check_zero_mean_unit_variance(input_values[1, :1000])
             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, 1200))


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             _lowerCamelCase							:     List[Any]	=		np.random.rand(100).astype(np.floataa)
             _lowerCamelCase							:     str	=		np_speech_inputs.tolist()

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


      def 				__snake_case			(		self):



             """simple docstring"""





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

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

             # Test batched
             _lowerCamelCase							:     Dict	=		feature_extractor(a__      ,							return_tensors='''np''').input_values
             _lowerCamelCase							:     List[Any]	=		feature_extractor(a__      ,							return_tensors='''np''').input_values
             for enc_seq_a, enc_seq_a in zip(a__      ,							a__):
                    self.assertTrue(np.allclose(a__      ,							a__      ,							atol=1e-3))

             # Test 2-D numpy arrays are batched.
             _lowerCamelCase							:     List[str]	=		[floats_list((1, x))[0] for x in (800, 800, 800)]
             _lowerCamelCase							:     Dict	=		np.asarray(a__)
             _lowerCamelCase							:     str	=		feature_extractor(a__      ,							return_tensors='''np''').input_values
             _lowerCamelCase							:     Optional[Any]	=		feature_extractor(a__      ,							return_tensors='''np''').input_values
             for enc_seq_a, enc_seq_a in zip(a__      ,							a__):
                    self.assertTrue(np.allclose(a__      ,							a__      ,							atol=1e-3))


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Optional[Any]	=		BatchFeature({input_name: speech_inputs})

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

             _lowerCamelCase							:     Optional[Any]	=		self.feat_extract_tester.prepare_inputs_for_target(equal_length=a__)
             _lowerCamelCase							:     int	=		BatchFeature({input_name: speech_inputs}      ,							tensor_type='''np''')

             _lowerCamelCase							:     Optional[int]	=		processed_features[input_name]

             if len(batch_features_input.shape) < 3:
                    _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





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

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

             _lowerCamelCase							:     Any	=		processed_features[input_name]

             if len(batch_features_input.shape) < 3:
                    _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.feature_extraction_class(**self.feat_extract_dict)
             _lowerCamelCase							:     Optional[Any]	=		self.feat_extract_tester.prepare_inputs_for_target()
             _lowerCamelCase							:     Dict	=		feat_extract.model_input_names[0]

             _lowerCamelCase							:     int	=		BatchFeature({input_name: speech_inputs})

             _lowerCamelCase							:     List[Any]	=		feat_extract.num_mel_bins  # hack!

             _lowerCamelCase							:     Optional[Any]	=		feat_extract.pad(a__      ,							padding='''longest'''      ,							return_tensors='''np''')[input_name]
             _lowerCamelCase							:     Dict	=		feat_extract.pad(a__      ,							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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		self.feat_extract_dict
             _lowerCamelCase							:     Any	=		True
             _lowerCamelCase							:     List[Any]	=		self.feature_extraction_class(**a__)
             _lowerCamelCase							:     Dict	=		self.feat_extract_tester.prepare_inputs_for_target()
             _lowerCamelCase							:     List[Any]	=		[len(a__) for x in speech_inputs]
             _lowerCamelCase							:     Tuple	=		feat_extract.model_input_names[0]

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

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Tuple	=		BatchFeature({input_name: speech_inputs})
             _lowerCamelCase							:     int	=		min(a__)

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

             _lowerCamelCase							:     Tuple	=		feat_extract.pad(
                 a__      ,							padding='''max_length'''      ,							max_length=a__      ,							truncation=a__      ,							return_tensors='''np''')
             self.assertIn('''attention_mask'''      ,							a__)
             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 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             from datasets import load_dataset

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		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

             _lowerCamelCase							:     List[Any]	=		self._load_datasamples(1)
             _lowerCamelCase							:     Optional[Any]	=		SpeechTaFeatureExtractor()
             _lowerCamelCase							:     Any	=		feature_extractor(a__      ,							return_tensors='''pt''').input_values
             self.assertEquals(input_values.shape      ,							(1, 9_3680))
             self.assertTrue(torch.allclose(input_values[0, :30]      ,							a__      ,							atol=1e-6))



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		torch.tensor(
                 [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
                  -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
                  -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
                  -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998])
             # fmt: on

             _lowerCamelCase							:     Tuple	=		self._load_datasamples(1)
             _lowerCamelCase							:     Dict	=		SpeechTaFeatureExtractor()
             _lowerCamelCase							:     Dict	=		feature_extractor(audio_target=a__      ,							return_tensors='''pt''').input_values
             self.assertEquals(input_values.shape      ,							(1, 366, 80))
             self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,							a__      ,							atol=1e-4))








import warnings

from .generation import TFGenerationMixin







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,lowerCamelCase__ ,)

