




import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
       from transformers import WhisperFeatureExtractor

if is_torch_available():
       import torch

_lowerCamelCase      			=						random.Random()




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						str=1.0	,					__UpperCamelCase      :						List[Any]=None	,					__UpperCamelCase      :						List[str]=None				)  ->							List[str]:


    if rng is None:
        UpperCAmelCase_										=					global_rng

    UpperCAmelCase_										=					[]
    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       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Dict ,   __snake_case       :      Any ,   __snake_case       :      List[Any]=7 ,   __snake_case       :      Tuple=4_00 ,   __snake_case       :      Any=20_00 ,   __snake_case       :      Dict=10 ,   __snake_case       :      str=1_60 ,   __snake_case       :      List[str]=8 ,   __snake_case       :      Dict=0.0 ,   __snake_case       :      int=40_00 ,   __snake_case       :      str=False ,   __snake_case       :      Any=True ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					min_seq_length
         UpperCAmelCase_										=					max_seq_length
         UpperCAmelCase_										=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         UpperCAmelCase_										=					padding_value
         UpperCAmelCase_										=					sampling_rate
         UpperCAmelCase_										=					return_attention_mask
         UpperCAmelCase_										=					do_normalize
         UpperCAmelCase_										=					feature_size
         UpperCAmelCase_										=					chunk_length
         UpperCAmelCase_										=					hop_length
     def   lowerCamelCase_       (     self       :      Dict    ):
         return {
             "feature_size": self.feature_size,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }




     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Optional[int]=False ,   __snake_case       :      Optional[int]=False    ):
         def _flatten(__snake_case       :      Any    ):
             return list(itertools.chain(*_a    )    )

         if equal_length:
             UpperCAmelCase_										=					[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
         else:
             # make sure that inputs increase in size
             UpperCAmelCase_										=					[
                 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_										=					[np.asarray(_a    ) for x in speech_inputs]
         return speech_inputs



@require_torch
@require_torchaudio
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Dict       		=  WhisperFeatureExtractor if is_speech_available() else None
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					WhisperFeatureExtractionTester(self    )
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_dict    )

         with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCAmelCase_										=					feat_extract_first.save_pretrained(_a    )[0]
             check_json_file_has_correct_format(_a    )
             UpperCAmelCase_										=					self.feature_extraction_class.from_pretrained(_a    )

         UpperCAmelCase_										=					feat_extract_first.to_dict()
         UpperCAmelCase_										=					feat_extract_second.to_dict()
         UpperCAmelCase_										=					feat_extract_first.mel_filters
         UpperCAmelCase_										=					feat_extract_second.mel_filters
         self.assertTrue(np.allclose(_a ,   _a    )    )
         self.assertEqual(_a ,   _a    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_dict    )

         with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCAmelCase_										=					os.path.join(_a ,   '''feat_extract.json'''    )
             feat_extract_first.to_json_file(_a    )
             UpperCAmelCase_										=					self.feature_extraction_class.from_json_file(_a    )

         UpperCAmelCase_										=					feat_extract_first.to_dict()
         UpperCAmelCase_										=					feat_extract_second.to_dict()
         UpperCAmelCase_										=					feat_extract_first.mel_filters
         UpperCAmelCase_										=					feat_extract_second.mel_filters
         self.assertTrue(np.allclose(_a ,   _a    )    )
         self.assertEqual(_a ,   _a    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         # create three inputs of length 800, 1000, and 1200
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in range(8_00 ,   14_00 ,   2_00    )]
         UpperCAmelCase_										=					[np.asarray(_a    ) for speech_input in speech_inputs]

         # Test feature size
         UpperCAmelCase_										=					feature_extractor(_a ,   padding='''max_length''' ,   return_tensors='''np'''    ).input_features
         self.assertTrue(input_features.ndim == 3    )
         self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames    )
         self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size    )

         # Test not batched input
         UpperCAmelCase_										=					feature_extractor(speech_inputs[0] ,   return_tensors='''np'''    ).input_features
         UpperCAmelCase_										=					feature_extractor(np_speech_inputs[0] ,   return_tensors='''np'''    ).input_features
         self.assertTrue(np.allclose(_a ,   _a ,   atol=1E-3    )    )

         # Test batched
         UpperCAmelCase_										=					feature_extractor(_a ,   return_tensors='''np'''    ).input_features
         UpperCAmelCase_										=					feature_extractor(_a ,   return_tensors='''np'''    ).input_features
         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.
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
         UpperCAmelCase_										=					np.asarray(_a    )
         UpperCAmelCase_										=					feature_extractor(_a ,   return_tensors='''np'''    ).input_features
         UpperCAmelCase_										=					feature_extractor(_a ,   return_tensors='''np'''    ).input_features
         for enc_seq_a, enc_seq_a in zip(_a ,   _a    ):
             self.assertTrue(np.allclose(_a ,   _a ,   atol=1E-3    )    )

         # Test truncation required
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in range(2_00 ,   (feature_extractor.n_samples + 5_00) ,   2_00    )]
         UpperCAmelCase_										=					[np.asarray(_a    ) for speech_input in speech_inputs]

         UpperCAmelCase_										=					[x[: feature_extractor.n_samples] for x in speech_inputs]
         UpperCAmelCase_										=					[np.asarray(_a    ) for speech_input in speech_inputs_truncated]

         UpperCAmelCase_										=					feature_extractor(_a ,   return_tensors='''np'''    ).input_features
         UpperCAmelCase_										=					feature_extractor(_a ,   return_tensors='''np'''    ).input_features
         for enc_seq_a, enc_seq_a in zip(_a ,   _a    ):
             self.assertTrue(np.allclose(_a ,   _a ,   atol=1E-3    )    )
     def   lowerCamelCase_       (     self       :      Any    ):
         import torch

         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         UpperCAmelCase_										=					np.random.rand(1_00 ,   32    ).astype(np.floataa    )
         UpperCAmelCase_										=					np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
             UpperCAmelCase_										=					feature_extractor.pad([{'''input_features''': inputs}] ,   return_tensors='''np'''    )
             self.assertTrue(np_processed.input_features.dtype == np.floataa    )
             UpperCAmelCase_										=					feature_extractor.pad([{'''input_features''': inputs}] ,   return_tensors='''pt'''    )
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Any    ):
         UpperCAmelCase_										=					load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,   '''clean''' ,   split='''validation'''    )
         # automatic decoding with librispeech
         UpperCAmelCase_										=					ds.sort('''id'''    ).select(range(_a    )    )[:num_samples]["audio"]

         return [x["array"] for x in speech_samples]
     def   lowerCamelCase_       (     self       :      str    ):
         # fmt: off
         UpperCAmelCase_										=					torch.tensor(
             [
                 0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
                 0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
                 0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
                 -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
             ]    )
         # fmt: on

         UpperCAmelCase_										=					self._load_datasamples(1    )
         UpperCAmelCase_										=					WhisperFeatureExtractor()
         UpperCAmelCase_										=					feature_extractor(_a ,   return_tensors='''pt'''    ).input_features
         self.assertEqual(input_features.shape ,   (1, 80, 30_00)    )
         self.assertTrue(torch.allclose(input_features[0, 0, :30] ,   _a ,   atol=1E-4    )    )




     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         UpperCAmelCase_										=					self._load_datasamples(1    )[0]
         UpperCAmelCase_										=					((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
         UpperCAmelCase_										=					feat_extract.zero_mean_unit_var_norm([audio] ,   attention_mask=_a    )[0]

         self.assertTrue(np.all(np.mean(_a    ) < 1E-3    )    )
         self.assertTrue(np.all(np.abs(np.var(_a    ) - 1    ) < 1E-3    )    )











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						float	,					__UpperCamelCase      :						float				)  ->							float:

    if density <= 0:
        raise ValueError('''Impossible fluid density'''				)
    if bulk_modulus <= 0:
        raise ValueError('''Impossible bulk modulus'''				)

    return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()





