





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

SCREAMING_SNAKE_CASE_						       =	random.Random()



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=1.0			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None     )			->       List[Any]:


      '''simple docstring'''


      if rng is None:
            SCREAMING_SNAKE_CASE		=						global_rng

      SCREAMING_SNAKE_CASE		=						[]
      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 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Dict						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Union[str, Any]=7						,lowerCamelCase__						:   List[Any]=400						,lowerCamelCase__						:   List[str]=2000						,lowerCamelCase__						:   Dict=10						,lowerCamelCase__						:   List[Any]=160						,lowerCamelCase__						:   Dict=8						,lowerCamelCase__						:   Dict=0.0						,lowerCamelCase__						:   Tuple=4000						,lowerCamelCase__						:   int=False						,lowerCamelCase__						:   Union[str, Any]=True						,)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						min_seq_length
           SCREAMING_SNAKE_CASE		=						max_seq_length
           SCREAMING_SNAKE_CASE		=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           SCREAMING_SNAKE_CASE		=						padding_value
           SCREAMING_SNAKE_CASE		=						sampling_rate
           SCREAMING_SNAKE_CASE		=						return_attention_mask
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						feature_size
           SCREAMING_SNAKE_CASE		=						chunk_length
           SCREAMING_SNAKE_CASE		=						hop_length






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Dict=False						,lowerCamelCase__						:   Optional[int]=False	)					->		List[Any]:




           '''simple docstring'''


           def _flatten(lowerCamelCase__						:   Union[str, Any]	):
                 return list(itertools.chain(*lowerCamelCase__	)	)

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

@require_torch
@require_torchaudio
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      WhisperFeatureExtractor if is_speech_available() else None






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						WhisperFeatureExtractionTester(self	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict	)

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

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_first.mel_filters
           SCREAMING_SNAKE_CASE		=						feat_extract_second.mel_filters
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__	)	)
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict	)

           with tempfile.TemporaryDirectory() as tmpdirname:
                 SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,"""feat_extract.json"""	)
                 feat_extract_first.to_json_file(lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						self.feature_extraction_class.from_json_file(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_first.mel_filters
           SCREAMING_SNAKE_CASE		=						feat_extract_second.mel_filters
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__	)	)
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__	)






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




           '''simple docstring'''


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

           # Test feature size
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						feature_extractor(speech_inputs[0]						,return_tensors="""np"""	).input_features
           SCREAMING_SNAKE_CASE		=						feature_extractor(np_speech_inputs[0]						,return_tensors="""np"""	).input_features
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3	)	)

           # Test batched
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""	).input_features
           SCREAMING_SNAKE_CASE		=						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.
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
           SCREAMING_SNAKE_CASE		=						np.asarray(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""	).input_features
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in range(200						,(feature_extractor.n_samples + 500)						,200	)]
           SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__	) for speech_input in speech_inputs]

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

           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""	).input_features
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Dict:




           '''simple docstring'''


           import torch

           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           SCREAMING_SNAKE_CASE		=						np.random.rand(100						,32	).astype(np.floataa	)
           SCREAMING_SNAKE_CASE		=						np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                 SCREAMING_SNAKE_CASE		=						feature_extractor.pad([{"""input_features""": inputs}]						,return_tensors="""np"""	)
                 self.assertTrue(np_processed.input_features.dtype == np.floataa	)
                 SCREAMING_SNAKE_CASE		=						feature_extractor.pad([{"""input_features""": inputs}]						,return_tensors="""pt"""	)
                 self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)






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




           '''simple docstring'''


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

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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

           SCREAMING_SNAKE_CASE		=						self._load_datasamples(1	)
           SCREAMING_SNAKE_CASE		=						WhisperFeatureExtractor()
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
           SCREAMING_SNAKE_CASE		=						self._load_datasamples(1	)[0]
           SCREAMING_SNAKE_CASE		=						((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
           SCREAMING_SNAKE_CASE		=						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	)	)









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 10_00     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						0

      for divide_by_number in range(_SCREAMING_SNAKE_CASE			,						digit + 1     ):
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						numerator
            for _ in range(1			,						digit + 1     ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )
                              SCREAMING_SNAKE_CASE		=						divide_by_number
                  else:
                        has_been_divided.append(_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()


