






'''simple docstring'''




# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowerCamelCase_												=					get_logger(__name__)

class    _UpperCAmelCase			:




   """simple docstring"""

   snake_case    				=						'''dummy_data'''
   snake_case    				=						'''datasets'''
   snake_case    				=						False

   def __init__(		self :	List[Any]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Union[Version, str]      ,      __UpperCAmelCase :	Optional[str] = None      ,      __UpperCAmelCase :	bool = False      ,      __UpperCAmelCase :	bool = True      ,      __UpperCAmelCase :	Optional[List[Callable]] = None      ,      ):





         '''simple docstring'''





         _A			     =	0
         _A			     =	dataset_name
         _A			     =	cache_dir
         _A			     =	use_local_dummy_data
         _A			     =	config
         # download_callbacks take a single url as input
         _A			     =	download_callbacks or []
         # if False, it doesn't load existing files and it returns the paths of the dummy files relative
         # to the dummy_data zip file root
         _A			     =	load_existing_dummy_data

         # TODO(PVP, QL) might need to make this more general
         _A			     =	str(__UpperCAmelCase		)
         # to be downloaded
         _A			     =	None
         _A			     =	None

   @property
   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         if self._dummy_file is None:
               _A			     =	self.download_dummy_data()
         return self._dummy_file

   @property
   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         if self.config is not None:
               # structure is dummy / config_name / version_name
               return os.path.join("dummy"      ,      self.config.name      ,      self.version_name		)
         # structure is dummy / version_name
         return os.path.join("dummy"      ,      self.version_name		)

   @property
   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         return os.path.join(self.dummy_data_folder      ,      "dummy_data.zip"		)

   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





         _A			     =	(
             self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
         )

         _A			     =	cached_path(
             __UpperCAmelCase      ,      cache_dir=self.cache_dir      ,      extract_compressed_file=__UpperCAmelCase      ,      force_extract=__UpperCAmelCase		)

         return os.path.join(__UpperCAmelCase      ,      self.dummy_file_name		)

   @property
   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         return os.path.join(self.datasets_scripts_dir      ,      self.dataset_name      ,      self.dummy_zip_file		)

   @property
   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         if self._bucket_url is None:
               _A			     =	hf_github_url(self.dataset_name      ,      self.dummy_zip_file.replace(os.sep      ,      "/"		)		)
         return self._bucket_url

   @property
   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         if os.path.isdir(self.dummy_file		):
               return self.dummy_file
         # else cut off path to file -> example `xsum`.
         return "/".join(self.dummy_file.replace(os.sep      ,      "/"		).split("/"		)[:-1]		)

   def 						lowerCAmelCase (		self :	Union[str, Any]      ,      __UpperCAmelCase :	Optional[Any]      ,      *__UpperCAmelCase :	Dict		):





         '''simple docstring'''





         if self.load_existing_dummy_data:
               # dummy data is downloaded and tested
               _A			     =	self.dummy_file
         else:
               # dummy data cannot be downloaded and only the path to dummy file is returned
               _A			     =	self.dummy_file_name

         # special case when data_url is a dict
         if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase		):
               return self.create_dummy_data_dict(__UpperCAmelCase      ,      __UpperCAmelCase		)
         elif isinstance(__UpperCAmelCase      ,      (list, tuple)		):
               return self.create_dummy_data_list(__UpperCAmelCase      ,      __UpperCAmelCase		)
         else:
               return self.create_dummy_data_single(__UpperCAmelCase      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	Optional[int]      ,      *__UpperCAmelCase :	Any		):





         '''simple docstring'''





         return self.download_and_extract(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	str		):





         '''simple docstring'''





         return self.download_and_extract(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	Optional[int]      ,      *__UpperCAmelCase :	List[str]      ,      **__UpperCAmelCase :	List[str]		):





         '''simple docstring'''





         return path

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         return {}

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	Optional[int]		):





         '''simple docstring'''





         _A			     =	{}
         for key, single_urls in data_url.items():
               for download_callback in self.download_callbacks:
                     if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase		):
                           for single_url in single_urls:
                                 download_callback(__UpperCAmelCase		)
                     else:
                           _A			     =	single_urls
                           download_callback(__UpperCAmelCase		)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
               if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase		):
                     _A			     =	[os.path.join(__UpperCAmelCase      ,      urllib.parse.quote_plus(Path(__UpperCAmelCase		).name		)		) for x in single_urls]
               else:
                     _A			     =	single_urls
                     _A			     =	os.path.join(__UpperCAmelCase      ,      urllib.parse.quote_plus(Path(__UpperCAmelCase		).name		)		)
               _A			     =	value

         # make sure that values are unique
         if all(isinstance(__UpperCAmelCase      ,      __UpperCAmelCase		) for i in dummy_data_dict.values()		) and len(set(dummy_data_dict.values()		)		) < len(
             dummy_data_dict.values()		):
               # append key to value to make its name unique
               _A			     =	{key: value + key for key, value in dummy_data_dict.items()}

         return dummy_data_dict

   def 						lowerCAmelCase (		self :	Union[str, Any]      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	[]
         # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
         _A			     =	all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"      ,      __UpperCAmelCase		)		) for url in data_url		)
         _A			     =	all(
             url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"		) for url in data_url		)
         if data_url and (is_tf_records or is_pubmed_records):
               _A			     =	[data_url[0]] * len(__UpperCAmelCase		)
         for single_url in data_url:
               for download_callback in self.download_callbacks:
                     download_callback(__UpperCAmelCase		)
               # we force the name of each key to be the last file / folder name of the url path
               # if the url has arguments, we need to encode them with urllib.parse.quote_plus
               _A			     =	os.path.join(__UpperCAmelCase      ,      urllib.parse.quote_plus(single_url.split("/"		)[-1]		)		)
               dummy_data_list.append(__UpperCAmelCase		)
         return dummy_data_list

   def 						lowerCAmelCase (		self :	str      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	List[str]		):





         '''simple docstring'''





         for download_callback in self.download_callbacks:
               download_callback(__UpperCAmelCase		)
         # we force the name of each key to be the last file / folder name of the url path
         # if the url has arguments, we need to encode them with urllib.parse.quote_plus
         _A			     =	os.path.join(__UpperCAmelCase      ,      urllib.parse.quote_plus(data_url.split("/"		)[-1]		)		)
         if os.path.exists(__UpperCAmelCase		) or not self.load_existing_dummy_data:
               return value
         else:
               # Backward compatibility, maybe deprecate at one point.
               # For many datasets with single url calls to dl_manager.download_and_extract,
               # the dummy_data.zip file is actually the zipped downloaded file
               # while now we expected the dummy_data.zip file to be a directory containing
               # the downloaded file.
               return path_to_dummy_data

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         pass

   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





         pass

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





         def _iter_archive_members(__UpperCAmelCase :	List[Any]		):
               # this preserves the order of the members inside the ZIP archive
               _A			     =	Path(self.dummy_file		).parent
               _A			     =	path.relative_to(__UpperCAmelCase		)
               with ZipFile(self.local_path_to_dummy_data		) as zip_file:
                     _A			     =	zip_file.namelist()
               for member in members:
                     if member.startswith(relative_path.as_posix()		):
                           yield dummy_parent_path.joinpath(__UpperCAmelCase		)

         _A			     =	Path(__UpperCAmelCase		)
         _A			     =	_iter_archive_members(__UpperCAmelCase		) if self.use_local_dummy_data else path.rglob("*"		)
         for file_path in file_paths:
               if file_path.is_file() and not file_path.name.startswith((".", "__")		):
                     yield file_path.relative_to(__UpperCAmelCase		).as_posix(), file_path.open("rb"		)

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	str		):





         '''simple docstring'''





         if not isinstance(__UpperCAmelCase      ,      __UpperCAmelCase		):
               _A			     =	[paths]
         for path in paths:
               if os.path.isfile(__UpperCAmelCase		):
                     if os.path.basename(__UpperCAmelCase		).startswith((".", "__")		):
                           return
                     yield path
               else:
                     for dirpath, dirnames, filenames in os.walk(__UpperCAmelCase		):
                           if os.path.basename(__UpperCAmelCase		).startswith((".", "__")		):
                                 continue
                           dirnames.sort()
                           for filename in sorted(__UpperCAmelCase		):
                                 if filename.startswith((".", "__")		):
                                       continue
                                 yield os.path.join(__UpperCAmelCase      ,      __UpperCAmelCase		)








'''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 :       int			   =     random.Random()







def    a__       (  a__      ,    a__=1.0      ,    a__=None      ,    a__=None						):




     """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 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str=7					,							__SCREAMING_SNAKE_CASE					:       List[str]=400					,							__SCREAMING_SNAKE_CASE					:       Any=2_000					,							__SCREAMING_SNAKE_CASE					:       List[str]=10					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=160					,							__SCREAMING_SNAKE_CASE					:       List[str]=8					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=0.0					,							__SCREAMING_SNAKE_CASE					:       Dict=4_000					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=False					,							__SCREAMING_SNAKE_CASE					:       List[Any]=True					,							)  -> str:


      """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       UpperCAmelCase__		(		self					:       Dict			)  -> Dict:


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


      """simple docstring"""




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

      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(__SCREAMING_SNAKE_CASE			) for x in speech_inputs]
      return speech_inputs







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



 """simple docstring"""



 lowerCAmelCase__  					=      WhisperFeatureExtractor if is_speech_available() else None
 def       UpperCAmelCase__		(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      WhisperFeatureExtractionTester(self			)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[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(__SCREAMING_SNAKE_CASE			)[0]
           check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class.from_pretrained(__SCREAMING_SNAKE_CASE			)

      __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(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[str]:


      """simple docstring"""




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

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

      __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(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Union[str, 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					,							1_400					,							200			)]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs]

      # Test feature size
      __SCREAMING_SNAKE_CASE     =      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
      __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(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

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

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

      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[int]:


      """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       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> 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(__SCREAMING_SNAKE_CASE			)			)[:num_samples]["""audio"""]

      return [x["array"] for x in speech_samples]
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> int:


      """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(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).input_features
      self.assertEqual(input_features.shape					,							(1, 80, 3_000)			)
      self.assertTrue(torch.allclose(input_features[0, 0, :30]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-4			)			)


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> 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())) * 65_535  # Rescale to [0, 65535] to show issue
      __SCREAMING_SNAKE_CASE     =      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			)			)






