






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowercase  							=					logging.get_logger(__name__)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			*a       ,			**a					)       ->    None:
								warnings.warn(
								    'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
								    ' Please use ChineseCLIPImageProcessor instead.'       ,			__SCREAMING_SNAKE_CASE       ,			)
								super().__init__(*__SCREAMING_SNAKE_CASE       ,			**__SCREAMING_SNAKE_CASE					)








'''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			)			)






