
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

_snake_case      = random.Random()

def  __lowerCamelCase					( _lowercase						,     _lowercase=1.0						,     _lowercase=None						,     _lowercase=None			)       ->      Tuple:
     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       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 def __init__(						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Optional[int]=7					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=4_00					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=20_00					, SCREAMING_SNAKE_CASE__			:			List[Any]=10					, SCREAMING_SNAKE_CASE__			:			Tuple=1_60					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=8					, SCREAMING_SNAKE_CASE__			:			List[Any]=0.0					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=40_00					, SCREAMING_SNAKE_CASE__			:			str=False					, SCREAMING_SNAKE_CASE__			:			Dict=True					, ):






      """simple docstring"""



      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			:			List[str]   ):






      """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			:			str					, SCREAMING_SNAKE_CASE__			:			Optional[int]=False					, SCREAMING_SNAKE_CASE__			:			List[str]=False   ):






      """simple docstring"""



      def _flatten(SCREAMING_SNAKE_CASE__			:			Optional[int]   ):
           return list(itertools.chain(*SCREAMING_SNAKE_CASE__   )   )

      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(SCREAMING_SNAKE_CASE__   ) for x in speech_inputs]
      return speech_inputs




@require_torch
@require_torchaudio
class       _lowerCAmelCase  (		__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=WhisperFeatureExtractor if is_speech_available() else None

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					WhisperFeatureExtractionTester(self   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



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

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

      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(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )
      self.assertEqual(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



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

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

      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(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )
      self.assertEqual(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      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(SCREAMING_SNAKE_CASE__   ) for speech_input in speech_inputs]

      # Test feature size
      UpperCamelCase						    =					feature_extractor(SCREAMING_SNAKE_CASE__					, 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(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, atol=1e-3   )   )

      # Test batched
      UpperCamelCase						    =					feature_extractor(SCREAMING_SNAKE_CASE__					, return_tensors='np'   ).input_features
      UpperCamelCase						    =					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						    =					[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
      UpperCamelCase						    =					np.asarray(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					feature_extractor(SCREAMING_SNAKE_CASE__					, return_tensors='np'   ).input_features
      UpperCamelCase						    =					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 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(SCREAMING_SNAKE_CASE__   ) for speech_input in speech_inputs]

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

      UpperCamelCase						    =					feature_extractor(SCREAMING_SNAKE_CASE__					, return_tensors='np'   ).input_features
      UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      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			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple   ):






      """simple docstring"""



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

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

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



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






 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      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=SCREAMING_SNAKE_CASE__   )[0]

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




def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


