
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

lowerCamelCase  =random.Random()


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__=1.0			,		UpperCamelCase__=None			,		UpperCamelCase__=None		):
      if rng is None:
            UpperCamelCase__     : Union[str, Any]											=					global_rng

      UpperCamelCase__     : Optional[Any]											=					[]
      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 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=7    ,    __SCREAMING_SNAKE_CASE=4_0_0    ,    __SCREAMING_SNAKE_CASE=2_0_0_0    ,    __SCREAMING_SNAKE_CASE=2_4    ,    __SCREAMING_SNAKE_CASE=2_4    ,    __SCREAMING_SNAKE_CASE=0.0    ,    __SCREAMING_SNAKE_CASE=1_6_0_0_0    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=True    ,    )	-> str:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					parent
           UpperCamelCase__     : List[str]											=					batch_size
           UpperCamelCase__     : List[Any]											=					min_seq_length
           UpperCamelCase__     : int											=					max_seq_length
           UpperCamelCase__     : Dict											=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           UpperCamelCase__     : Optional[int]											=					feature_size
           UpperCamelCase__     : List[Any]											=					num_mel_bins
           UpperCamelCase__     : str											=					padding_value
           UpperCamelCase__     : Optional[int]											=					sampling_rate
           UpperCamelCase__     : Any											=					return_attention_mask
           UpperCamelCase__     : Union[str, Any]											=					do_normalize






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=False	)	-> Union[str, Any]:




           """simple docstring"""







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

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




@require_torch
@require_torchaudio
class 					_lowerCamelCase			(   UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	SpeechaTextFeatureExtractor if is_speech_available() else None






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					SpeechaTextFeatureExtractionTester(self	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> 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	)	-> List[str]:




           """simple docstring"""







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

           # Test feature size
           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    padding=__SCREAMING_SNAKE_CASE    ,    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
           UpperCamelCase__     : List[Any]											=					feature_extractor(speech_inputs[0]    ,    return_tensors='''np'''	).input_features
           UpperCamelCase__     : Dict											=					feature_extractor(np_speech_inputs[0]    ,    return_tensors='''np'''	).input_features
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)

           # Test batched
           UpperCamelCase__     : Any											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	).input_features
           UpperCamelCase__     : Optional[int]											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	).input_features
           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.
           UpperCamelCase__     : List[str]											=					[floats_list((1, x)	)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
           UpperCamelCase__     : Any											=					np.asarray(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	).input_features
           UpperCamelCase__     : Optional[int]											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	).input_features
           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	)	-> int:




           """simple docstring"""







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

           UpperCamelCase__     : List[str]											=					['''longest''', '''max_length''', '''do_not_pad''']
           UpperCamelCase__     : Any											=					[None, 1_6, None]
           for max_length, padding in zip(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : Tuple											=					feature_extractor(
                     __SCREAMING_SNAKE_CASE    ,    padding=__SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    return_attention_mask=__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : List[Any]											=					inputs.input_features
                 UpperCamelCase__     : Dict											=					inputs.attention_mask
                 UpperCamelCase__     : int											=					[np.sum(__SCREAMING_SNAKE_CASE	) 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  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







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

           UpperCamelCase__     : List[str]											=					['''longest''', '''max_length''', '''do_not_pad''']
           UpperCamelCase__     : Optional[Any]											=					[None, 1_6, None]
           for max_length, padding in zip(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : Union[str, Any]											=					feature_extractor(
                     __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    padding=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''    ,    return_attention_mask=__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Any											=					inputs.input_features
                 UpperCamelCase__     : List[str]											=					inputs.attention_mask
                 UpperCamelCase__     : Optional[Any]											=					[np.sum(__SCREAMING_SNAKE_CASE	) 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  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           UpperCamelCase__     : List[str]											=					[floats_list((1, x)	)[0] for x in range(8_0_0    ,    1_4_0_0    ,    2_0_0	)]
           UpperCamelCase__     : Dict											=					feature_extractor(
               __SCREAMING_SNAKE_CASE    ,    padding='''max_length'''    ,    max_length=4    ,    truncation=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''    ,    return_attention_mask=__SCREAMING_SNAKE_CASE    ,    )
           UpperCamelCase__     : Dict											=					inputs.input_features
           UpperCamelCase__     : Optional[Any]											=					inputs.attention_mask
           UpperCamelCase__     : Dict											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           UpperCamelCase__     : List[str]											=					[floats_list((1, x)	)[0] for x in range(8_0_0    ,    1_4_0_0    ,    2_0_0	)]
           UpperCamelCase__     : Dict											=					feature_extractor(
               __SCREAMING_SNAKE_CASE    ,    padding='''longest'''    ,    max_length=4    ,    truncation=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''    ,    return_attention_mask=__SCREAMING_SNAKE_CASE    ,    )
           UpperCamelCase__     : Tuple											=					inputs.input_features
           UpperCamelCase__     : int											=					inputs.attention_mask
           UpperCamelCase__     : Any											=					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, 2_4)	)

           UpperCamelCase__     : Dict											=					[floats_list((1, x)	)[0] for x in range(8_0_0    ,    1_4_0_0    ,    2_0_0	)]
           UpperCamelCase__     : Optional[Any]											=					feature_extractor(
               __SCREAMING_SNAKE_CASE    ,    padding='''longest'''    ,    max_length=1_6    ,    truncation=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''    ,    return_attention_mask=__SCREAMING_SNAKE_CASE    ,    )
           UpperCamelCase__     : List[Any]											=					inputs.input_features
           UpperCamelCase__     : List[Any]											=					inputs.attention_mask
           UpperCamelCase__     : str											=					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, 2_4)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           import torch

           UpperCamelCase__     : Dict											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           UpperCamelCase__     : Any											=					np.random.rand(1_0_0    ,    3_2	).astype(np.floataa	)
           UpperCamelCase__     : Dict											=					np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                 UpperCamelCase__     : Union[str, Any]											=					feature_extractor.pad([{'''input_features''': inputs}]    ,    return_tensors='''np'''	)
                 self.assertTrue(np_processed.input_features.dtype == np.floataa	)
                 UpperCamelCase__     : Dict											=					feature_extractor.pad([{'''input_features''': inputs}]    ,    return_tensors='''pt'''	)
                 self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Union[str, Any]:




           """simple docstring"""







           from datasets import load_dataset

           UpperCamelCase__     : Dict											=					load_dataset('''hf-internal-testing/librispeech_asr_dummy'''    ,    '''clean'''    ,    split='''validation'''	)
           # automatic decoding with librispeech
           UpperCamelCase__     : Optional[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	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					np.array([
               -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
               -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
               -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
           ]	)
           # fmt: on

           UpperCamelCase__     : Any											=					self._load_datasamples(1	)
           UpperCamelCase__     : Tuple											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           UpperCamelCase__     : int											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	).input_features
           self.assertEquals(input_features.shape    ,    (1, 5_8_4, 2_4)	)
           self.assertTrue(np.allclose(input_features[0, 0, :3_0]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-4	)	)




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = " "		):
      UpperCamelCase__     : Optional[int]											=					[]

      UpperCamelCase__     : int											=					0
      for index, char in enumerate(UpperCamelCase__		):
            if char == separator:
                  split_words.append(string[last_index:index]		)
                  UpperCamelCase__     : int											=					index + 1
            elif index + 1 == len(UpperCamelCase__		):
                  split_words.append(string[last_index : index + 1]		)
      return split_words


if __name__ == "__main__":
    from doctest import testmod

    testmod()


