'''simple docstring'''

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

UpperCamelCase__  :						List[str]					=      random.Random()







def __UpperCamelCase( _A       :					Tuple	,      _A       :					int=1.0	,      _A       :					Any=None	,      _A       :					Tuple=None     ):
   '''simple docstring'''
   if rng is None:
      UpperCAmelCase__				:					Tuple 					=	global_rng

   UpperCAmelCase__				:					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 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=7       ,lowerCamelCase_=400       ,lowerCamelCase_=2000       ,lowerCamelCase_=10       ,lowerCamelCase_=160       ,lowerCamelCase_=8       ,lowerCamelCase_=0.0       ,lowerCamelCase_=4000       ,lowerCamelCase_=False       ,lowerCamelCase_=True       ,)      ->      Any:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         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       ,lowerCamelCase_=False       ,lowerCamelCase_=False				)      ->      List[Any]:



         '''simple docstring'''



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

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




@require_torch
@require_torchaudio
class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= WhisperFeatureExtractor if is_speech_available() else None




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



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	WhisperFeatureExtractionTester(self				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
            UpperCAmelCase__				:					List[str] 					=	feat_extract_first.save_pretrained(lowerCamelCase_				)[0]
            check_json_file_has_correct_format(lowerCamelCase_				)
            UpperCAmelCase__				:					int 					=	self.feature_extraction_class.from_pretrained(lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	feat_extract_first.to_dict()
         UpperCAmelCase__				:					Tuple 					=	feat_extract_second.to_dict()
         UpperCAmelCase__				:					List[Any] 					=	feat_extract_first.mel_filters
         UpperCAmelCase__				:					Dict 					=	feat_extract_second.mel_filters
         self.assertTrue(np.allclose(lowerCamelCase_       ,lowerCamelCase_				)				)
         self.assertEqual(lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
            UpperCAmelCase__				:					Any 					=	os.path.join(lowerCamelCase_       ,'''feat_extract.json'''				)
            feat_extract_first.to_json_file(lowerCamelCase_				)
            UpperCAmelCase__				:					List[Any] 					=	self.feature_extraction_class.from_json_file(lowerCamelCase_				)

         UpperCAmelCase__				:					Dict 					=	feat_extract_first.to_dict()
         UpperCAmelCase__				:					Optional[Any] 					=	feat_extract_second.to_dict()
         UpperCAmelCase__				:					Dict 					=	feat_extract_first.mel_filters
         UpperCAmelCase__				:					Optional[Any] 					=	feat_extract_second.mel_filters
         self.assertTrue(np.allclose(lowerCamelCase_       ,lowerCamelCase_				)				)
         self.assertEqual(lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



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

         # Test feature size
         UpperCAmelCase__				:					Dict 					=	feature_extractor(lowerCamelCase_       ,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__				:					Union[str, Any] 					=	feature_extractor(speech_inputs[0]       ,return_tensors='''np'''				).input_features
         UpperCAmelCase__				:					List[Any] 					=	feature_extractor(np_speech_inputs[0]       ,return_tensors='''np'''				).input_features
         self.assertTrue(np.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-3				)				)

         # Test batched
         UpperCAmelCase__				:					Optional[int] 					=	feature_extractor(lowerCamelCase_       ,return_tensors='''np'''				).input_features
         UpperCAmelCase__				:					List[Any] 					=	feature_extractor(lowerCamelCase_       ,return_tensors='''np'''				).input_features
         for enc_seq_a, enc_seq_a in zip(lowerCamelCase_       ,lowerCamelCase_				):
            self.assertTrue(np.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-3				)				)

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

         # Test truncation required
         UpperCAmelCase__				:					int 					=	[floats_list((1, x)				)[0] for x in range(200       ,(feature_extractor.n_samples + 500)       ,200				)]
         UpperCAmelCase__				:					str 					=	[np.asarray(lowerCamelCase_				) for speech_input in speech_inputs]

         UpperCAmelCase__				:					str 					=	[x[: feature_extractor.n_samples] for x in speech_inputs]
         UpperCAmelCase__				:					List[Any] 					=	[np.asarray(lowerCamelCase_				) for speech_input in speech_inputs_truncated]

         UpperCAmelCase__				:					int 					=	feature_extractor(lowerCamelCase_       ,return_tensors='''np'''				).input_features
         UpperCAmelCase__				:					Tuple 					=	feature_extractor(lowerCamelCase_       ,return_tensors='''np'''				).input_features
         for enc_seq_a, enc_seq_a in zip(lowerCamelCase_       ,lowerCamelCase_				):
            self.assertTrue(np.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-3				)				)




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         import torch

         UpperCAmelCase__				:					Optional[Any] 					=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
         UpperCAmelCase__				:					Union[str, Any] 					=	np.random.rand(100       ,32				).astype(np.floataa				)
         UpperCAmelCase__				:					Optional[Any] 					=	np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
            UpperCAmelCase__				:					List[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 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



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

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




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	torch.tensor(
             [
                 0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
                 0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
                 0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
                 -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
             ]				)
         # fmt: on

         UpperCAmelCase__				:					Any 					=	self._load_datasamples(1				)
         UpperCAmelCase__				:					List[str] 					=	WhisperFeatureExtractor()
         UpperCAmelCase__				:					Union[str, Any] 					=	feature_extractor(lowerCamelCase_       ,return_tensors='''pt'''				).input_features
         self.assertEqual(input_features.shape       ,(1, 80, 3000)				)
         self.assertTrue(torch.allclose(input_features[0, 0, :30]       ,lowerCamelCase_       ,atol=1e-4				)				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



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

         self.assertTrue(np.all(np.mean(lowerCamelCase_				) < 1e-3				)				)
         self.assertTrue(np.all(np.abs(np.var(lowerCamelCase_				) - 1				) < 1e-3				)				)





'''simple docstring'''

import baseaa







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''
   return baseaa.baaencode(string.encode('''utf-8'''     )     )







def __UpperCamelCase( _A       :					bytes     ):
   '''simple docstring'''
   return baseaa.baadecode(_A     ).decode('''utf-8'''     )


if __name__ == "__main__":
  UpperCamelCase__  :						List[str]					=      'Hello World!'
  UpperCamelCase__  :						str					=      baseaa_encode(test)
  print(encoded)

  UpperCamelCase__  :						int					=      baseaa_decode(encoded)
  print(decoded)




