


'''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

__magic_name__			=      random.Random()




def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]=1.0							,						lowerCamelCase  :      Tuple=None							,						lowerCamelCase  :      Dict=None):
      if rng is None:
            A_						:  Any          =				global_rng

      A_						:  Optional[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 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







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

        '''simple docstring'''




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






  def        _a							(							self :       Union[str, 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        _a							(							self :       Optional[Any]	,_a :       Dict=False	,_a :       List[Any]=False ):

        '''simple docstring'''




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

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







@require_torch
@require_torchaudio
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							WhisperFeatureExtractor if is_speech_available() else None






  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  str          =				WhisperFeatureExtractionTester(self )






  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  str          =				self.feature_extraction_class(**self.feat_extract_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
              A_						:  Dict          =				feat_extract_first.save_pretrained(_a )[0]
              check_json_file_has_correct_format(_a )
              A_						:  Optional[Any]          =				self.feature_extraction_class.from_pretrained(_a )

        A_						:  Any          =				feat_extract_first.to_dict()
        A_						:  Any          =				feat_extract_second.to_dict()
        A_						:  str          =				feat_extract_first.mel_filters
        A_						:  Optional[Any]          =				feat_extract_second.mel_filters
        self.assertTrue(np.allclose(_a	,_a ) )
        self.assertEqual(_a	,_a )






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.feature_extraction_class(**self.feat_extract_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
              A_						:  Optional[int]          =				os.path.join(_a	,"""feat_extract.json""" )
              feat_extract_first.to_json_file(_a )
              A_						:  str          =				self.feature_extraction_class.from_json_file(_a )

        A_						:  Any          =				feat_extract_first.to_dict()
        A_						:  List[str]          =				feat_extract_second.to_dict()
        A_						:  Any          =				feat_extract_first.mel_filters
        A_						:  Union[str, Any]          =				feat_extract_second.mel_filters
        self.assertTrue(np.allclose(_a	,_a ) )
        self.assertEqual(_a	,_a )






  def        _a							(							self :       str ):

        '''simple docstring'''




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

        # Test feature size
        A_						:  List[Any]          =				feature_extractor(_a	,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
        A_						:  List[Any]          =				feature_extractor(speech_inputs[0]	,return_tensors="""np""" ).input_features
        A_						:  List[str]          =				feature_extractor(np_speech_inputs[0]	,return_tensors="""np""" ).input_features
        self.assertTrue(np.allclose(_a	,_a	,atol=1e-3 ) )

        # Test batched
        A_						:  List[str]          =				feature_extractor(_a	,return_tensors="""np""" ).input_features
        A_						:  str          =				feature_extractor(_a	,return_tensors="""np""" ).input_features
        for enc_seq_a, enc_seq_a in zip(_a	,_a ):
              self.assertTrue(np.allclose(_a	,_a	,atol=1e-3 ) )

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

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

        A_						:  List[Any]          =				[x[: feature_extractor.n_samples] for x in speech_inputs]
        A_						:  Any          =				[np.asarray(_a ) for speech_input in speech_inputs_truncated]

        A_						:  Any          =				feature_extractor(_a	,return_tensors="""np""" ).input_features
        A_						:  List[str]          =				feature_extractor(_a	,return_tensors="""np""" ).input_features
        for enc_seq_a, enc_seq_a in zip(_a	,_a ):
              self.assertTrue(np.allclose(_a	,_a	,atol=1e-3 ) )






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        import torch

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

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






  def        _a							(							self :       List[Any]	,_a :       Any ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,"""clean"""	,split="""validation""" )
        # automatic decoding with librispeech
        A_						:  List[Any]          =				ds.sort("""id""" ).select(range(_a ) )[:num_samples]["""audio"""]

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






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Optional[int]          =				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

        A_						:  Union[str, Any]          =				self._load_datasamples(1 )
        A_						:  int          =				WhisperFeatureExtractor()
        A_						:  Dict          =				feature_extractor(_a	,return_tensors="""pt""" ).input_features
        self.assertEqual(input_features.shape	,(1, 80, 3000) )
        self.assertTrue(torch.allclose(input_features[0, 0, :30]	,_a	,atol=1e-4 ) )






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Any          =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        A_						:  Tuple          =				self._load_datasamples(1 )[0]
        A_						:  Optional[Any]          =				((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
        A_						:  str          =				feat_extract.zero_mean_unit_var_norm([audio]	,attention_mask=_a )[0]

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




'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       Union[str, Any]	,*_a :       Optional[Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        warnings.warn(
            """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
            """ use OwlViTImageProcessor instead."""	,_a	,)
        super().__init__(*_a	,**_a )
