




'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch


A_			      =		random.Random()






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=1.0				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=None		)    ->   Tuple:
      if rng is None:
            snake_case__       :   str	      =    global_rng

      snake_case__       :   List[str]	      =    []
      for batch_idx in range(shape[0]		):
            values.append([]		)
            for _ in range(shape[1]		):
                  values[-1].append(rng.random() * scale		)

      return values






@require_torch
class        lowercase_      (						unittest.TestCase							):





     def __init__(     self     :					List[str]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Optional[Any]=7     ,							__lowerCamelCase     :					Union[str, Any]=400     ,							__lowerCamelCase     :					Tuple=2000     ,							__lowerCamelCase     :					str=1     ,							__lowerCamelCase     :					int=0.0     ,							__lowerCamelCase     :					Optional[Any]=16000     ,							__lowerCamelCase     :					Optional[Any]=True     ,							__lowerCamelCase     :					Any=80     ,							__lowerCamelCase     :					Union[str, Any]=16     ,							__lowerCamelCase     :					List[str]=64     ,							__lowerCamelCase     :					List[Any]="hann_window"     ,							__lowerCamelCase     :					Any=80     ,							__lowerCamelCase     :					List[Any]=7600     ,							__lowerCamelCase     :					List[Any]=1E-10     ,							__lowerCamelCase     :					Optional[Any]=True     ,							):
           snake_case__       :   Dict	      =    parent
           snake_case__       :   Optional[Any]	      =    batch_size
           snake_case__       :   List[str]	      =    min_seq_length
           snake_case__       :   Optional[Any]	      =    max_seq_length
           snake_case__       :   Dict	      =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           snake_case__       :   Any	      =    feature_size
           snake_case__       :   Union[str, Any]	      =    padding_value
           snake_case__       :   List[Any]	      =    sampling_rate
           snake_case__       :   Optional[Any]	      =    do_normalize
           snake_case__       :   Tuple	      =    num_mel_bins
           snake_case__       :   List[str]	      =    hop_length
           snake_case__       :   int	      =    win_length
           snake_case__       :   Union[str, Any]	      =    win_function
           snake_case__       :   int	      =    fmin
           snake_case__       :   List[str]	      =    fmax
           snake_case__       :   str	      =    mel_floor
           snake_case__       :   Optional[int]	      =    return_attention_mask





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "do_normalize": self.do_normalize,
               "num_mel_bins": self.num_mel_bins,
               "hop_length": self.hop_length,
               "win_length": self.win_length,
               "win_function": self.win_function,
               "fmin": self.fmin,
               "fmax": self.fmax,
               "mel_floor": self.mel_floor,
               "return_attention_mask": self.return_attention_mask,
           }





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Optional[int]=False     ,							__lowerCamelCase     :					str=False			):
           def _flatten(__lowerCamelCase     :					Dict			):
                 return list(itertools.chain(*_lowerCAmelCase			)			)

           if equal_length:
                 snake_case__       :   Union[str, Any]	      =    floats_list((self.batch_size, self.max_seq_length)			)
           else:
                 # make sure that inputs increase in size
                 snake_case__       :   Dict	      =    [
                     _flatten(floats_list((x, self.feature_size)			)			)
                     for x in range(self.min_seq_length     ,							self.max_seq_length     ,							self.seq_length_diff			)
                 ]

           if numpify:
                 snake_case__       :   Any	      =    [np.asarray(_lowerCAmelCase			) for x in speech_inputs]

           return speech_inputs





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					Union[str, Any]=False     ,							__lowerCamelCase     :					Optional[int]=False			):
           if equal_length:
                 snake_case__       :   Any	      =    [floats_list((self.max_seq_length, self.num_mel_bins)			) for _ in range(self.batch_size			)]
           else:
                 # make sure that inputs increase in size
                 snake_case__       :   int	      =    [
                     floats_list((x, self.num_mel_bins)			)
                     for x in range(self.min_seq_length     ,							self.max_seq_length     ,							self.seq_length_diff			)
                 ]

           if numpify:
                 snake_case__       :   Union[str, Any]	      =    [np.asarray(_lowerCAmelCase			) for x in speech_inputs]

           return speech_inputs






@require_torch
class        lowercase_      (						_SCREAMING_SNAKE_CASE						,       unittest.TestCase							):
     A_											=			SpeechTaFeatureExtractor





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Any	      =    SpeechTaFeatureExtractionTester(self			)





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					int			):
           self.assertTrue(np.all(np.mean(_lowerCAmelCase     ,							axis=0			) < 1E-3			)			)
           self.assertTrue(np.all(np.abs(np.var(_lowerCAmelCase     ,							axis=0			) - 1			) < 1E-3			)			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           snake_case__       :   Union[str, Any]	      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           # create three inputs of length 800, 1000, and 1200
           snake_case__       :   Union[str, Any]	      =    [floats_list((1, x)			)[0] for x in range(800     ,							1400     ,							200			)]
           snake_case__       :   int	      =    [np.asarray(_lowerCAmelCase			) for speech_input in speech_inputs]

           # Test not batched input
           snake_case__       :   str	      =    feat_extract(speech_inputs[0]     ,							return_tensors='np'			).input_values
           snake_case__       :   Tuple	      =    feat_extract(np_speech_inputs[0]     ,							return_tensors='np'			).input_values
           self.assertTrue(np.allclose(_lowerCAmelCase     ,							_lowerCAmelCase     ,							atol=1E-3			)			)

           # Test batched
           snake_case__       :   Union[str, Any]	      =    feat_extract(_lowerCAmelCase     ,							return_tensors='np'			).input_values
           snake_case__       :   str	      =    feat_extract(_lowerCAmelCase     ,							return_tensors='np'			).input_values
           for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase     ,							_lowerCAmelCase			):
                 self.assertTrue(np.allclose(_lowerCAmelCase     ,							_lowerCAmelCase     ,							atol=1E-3			)			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           snake_case__       :   List[str]	      =    [floats_list((1, x)			)[0] for x in range(800     ,							1400     ,							200			)]

           snake_case__       :   Any	      =    ['longest', 'max_length', 'do_not_pad']
           snake_case__       :   List[str]	      =    [None, 1600, None]
           for max_length, padding in zip(_lowerCAmelCase     ,							_lowerCAmelCase			):
                 snake_case__       :   Optional[int]	      =    feat_extract(_lowerCAmelCase     ,							padding=_lowerCAmelCase     ,							max_length=_lowerCAmelCase     ,							return_tensors='np'			)
                 snake_case__       :   Optional[Any]	      =    processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:800]			)
                 self.assertTrue(input_values[0][800:].sum() < 1E-6			)
                 self._check_zero_mean_unit_variance(input_values[1][:1000]			)
                 self.assertTrue(input_values[0][1000:].sum() < 1E-6			)
                 self._check_zero_mean_unit_variance(input_values[2][:1200]			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   str	      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           snake_case__       :   Any	      =    range(800     ,							1400     ,							200			)
           snake_case__       :   Optional[int]	      =    [floats_list((1, x)			)[0] for x in lengths]

           snake_case__       :   Any	      =    ['longest', 'max_length', 'do_not_pad']
           snake_case__       :   List[Any]	      =    [None, 1600, None]

           for max_length, padding in zip(_lowerCAmelCase     ,							_lowerCAmelCase			):
                 snake_case__       :   int	      =    feat_extract(_lowerCAmelCase     ,							max_length=_lowerCAmelCase     ,							padding=_lowerCAmelCase			)
                 snake_case__       :   Dict	      =    processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:800]			)
                 self._check_zero_mean_unit_variance(input_values[1][:1000]			)
                 self._check_zero_mean_unit_variance(input_values[2][:1200]			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Union[str, Any]	      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           snake_case__       :   str	      =    [floats_list((1, x)			)[0] for x in range(800     ,							1400     ,							200			)]
           snake_case__       :   Optional[Any]	      =    feat_extract(
               _lowerCAmelCase     ,							truncation=_lowerCAmelCase     ,							max_length=1000     ,							padding='max_length'     ,							return_tensors='np'			)
           snake_case__       :   str	      =    processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]			)
           self._check_zero_mean_unit_variance(input_values[1]			)
           self._check_zero_mean_unit_variance(input_values[2]			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   int	      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           snake_case__       :   Optional[Any]	      =    [floats_list((1, x)			)[0] for x in range(800     ,							1400     ,							200			)]
           snake_case__       :   Dict	      =    feat_extract(
               _lowerCAmelCase     ,							truncation=_lowerCAmelCase     ,							max_length=1000     ,							padding='longest'     ,							return_tensors='np'			)
           snake_case__       :   Tuple	      =    processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]			)
           self._check_zero_mean_unit_variance(input_values[1, :1000]			)
           self._check_zero_mean_unit_variance(input_values[2]			)

           # make sure that if max_length < longest -> then pad to max_length
           self.assertTrue(input_values.shape == (3, 1000)			)

           snake_case__       :   Dict	      =    [floats_list((1, x)			)[0] for x in range(800     ,							1400     ,							200			)]
           snake_case__       :   List[Any]	      =    feat_extract(
               _lowerCAmelCase     ,							truncation=_lowerCAmelCase     ,							max_length=2000     ,							padding='longest'     ,							return_tensors='np'			)
           snake_case__       :   Dict	      =    processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]			)
           self._check_zero_mean_unit_variance(input_values[1, :1000]			)
           self._check_zero_mean_unit_variance(input_values[2]			)

           # make sure that if max_length > longest -> then pad to longest
           self.assertTrue(input_values.shape == (3, 1200)			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Optional[int]	      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           snake_case__       :   str	      =    np.random.rand(100			).astype(np.floataa			)
           snake_case__       :   Any	      =    np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                 snake_case__       :   Tuple	      =    feature_extractor.pad([{'input_values': inputs}]     ,							return_tensors='np'			)
                 self.assertTrue(np_processed.input_values.dtype == np.floataa			)
                 snake_case__       :   Optional[int]	      =    feature_extractor.pad([{'input_values': inputs}]     ,							return_tensors='pt'			)
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           snake_case__       :   Tuple	      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           # create three inputs of length 800, 1000, and 1200
           snake_case__       :   List[str]	      =    [floats_list((1, x)			)[0] for x in range(800     ,							1400     ,							200			)]
           snake_case__       :   Optional[int]	      =    [np.asarray(_lowerCAmelCase			) for speech_input in speech_inputs]

           # Test feature size
           snake_case__       :   List[Any]	      =    feature_extractor(audio_target=_lowerCAmelCase     ,							padding=_lowerCAmelCase     ,							return_tensors='np'			).input_values
           self.assertTrue(input_values.ndim == 3			)
           self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins			)

           # Test not batched input
           snake_case__       :   str	      =    feature_extractor(speech_inputs[0]     ,							return_tensors='np'			).input_values
           snake_case__       :   Any	      =    feature_extractor(np_speech_inputs[0]     ,							return_tensors='np'			).input_values
           self.assertTrue(np.allclose(_lowerCAmelCase     ,							_lowerCAmelCase     ,							atol=1E-3			)			)

           # Test batched
           snake_case__       :   int	      =    feature_extractor(_lowerCAmelCase     ,							return_tensors='np'			).input_values
           snake_case__       :   int	      =    feature_extractor(_lowerCAmelCase     ,							return_tensors='np'			).input_values
           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.
           snake_case__       :   Optional[int]	      =    [floats_list((1, x)			)[0] for x in (800, 800, 800)]
           snake_case__       :   List[Any]	      =    np.asarray(_lowerCAmelCase			)
           snake_case__       :   List[Any]	      =    feature_extractor(_lowerCAmelCase     ,							return_tensors='np'			).input_values
           snake_case__       :   str	      =    feature_extractor(_lowerCAmelCase     ,							return_tensors='np'			).input_values
           for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase     ,							_lowerCAmelCase			):
                 self.assertTrue(np.allclose(_lowerCAmelCase     ,							_lowerCAmelCase     ,							atol=1E-3			)			)





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Any	      =    self.feat_extract_tester.prepare_inputs_for_target()
           snake_case__       :   List[Any]	      =    self.feature_extraction_class(**self.feat_extract_dict			)
           snake_case__       :   List[str]	      =    feat_extract.model_input_names[0]

           snake_case__       :   Union[str, Any]	      =    BatchFeature({input_name: speech_inputs}			)

           self.assertTrue(all(len(_lowerCAmelCase			) == len(_lowerCAmelCase			) for x, y in zip(_lowerCAmelCase     ,							processed_features[input_name]			)			)			)

           snake_case__       :   int	      =    self.feat_extract_tester.prepare_inputs_for_target(equal_length=_lowerCAmelCase			)
           snake_case__       :   Any	      =    BatchFeature({input_name: speech_inputs}     ,							tensor_type='np'			)

           snake_case__       :   Any	      =    processed_features[input_name]

           if len(batch_features_input.shape			) < 3:
                 snake_case__       :   str	      =    batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.num_mel_bins)			)





     @require_torch
     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Optional[Any]	      =    self.feat_extract_tester.prepare_inputs_for_target(equal_length=_lowerCAmelCase			)
           snake_case__       :   Any	      =    self.feature_extraction_class(**self.feat_extract_dict			)
           snake_case__       :   int	      =    feat_extract.model_input_names[0]

           snake_case__       :   Union[str, Any]	      =    BatchFeature({input_name: speech_inputs}     ,							tensor_type='pt'			)

           snake_case__       :   Optional[int]	      =    processed_features[input_name]

           if len(batch_features_input.shape			) < 3:
                 snake_case__       :   Optional[Any]	      =    batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.num_mel_bins)			)





     @require_torch
     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   str	      =    self.feature_extraction_class(**self.feat_extract_dict			)
           snake_case__       :   List[str]	      =    self.feat_extract_tester.prepare_inputs_for_target()
           snake_case__       :   Any	      =    feat_extract.model_input_names[0]

           snake_case__       :   Optional[int]	      =    BatchFeature({input_name: speech_inputs}			)

           snake_case__       :   List[str]	      =    feat_extract.num_mel_bins  # hack!

           snake_case__       :   Dict	      =    feat_extract.pad(_lowerCAmelCase     ,							padding='longest'     ,							return_tensors='np'			)[input_name]
           snake_case__       :   str	      =    feat_extract.pad(_lowerCAmelCase     ,							padding='longest'     ,							return_tensors='pt'			)[input_name]

           self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_pt.numpy().astype(np.floataa			).sum()			) < 1E-2			)





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Tuple	      =    self.feat_extract_dict
           snake_case__       :   List[Any]	      =    True
           snake_case__       :   List[str]	      =    self.feature_extraction_class(**_lowerCAmelCase			)
           snake_case__       :   int	      =    self.feat_extract_tester.prepare_inputs_for_target()
           snake_case__       :   Union[str, Any]	      =    [len(_lowerCAmelCase			) for x in speech_inputs]
           snake_case__       :   List[str]	      =    feat_extract.model_input_names[0]

           snake_case__       :   Optional[Any]	      =    BatchFeature({input_name: speech_inputs}			)

           snake_case__       :   Any	      =    feat_extract.num_mel_bins  # hack!

           snake_case__       :   Any	      =    feat_extract.pad(_lowerCAmelCase     ,							padding='longest'     ,							return_tensors='np'			)
           self.assertIn('attention_mask'     ,							_lowerCAmelCase			)
           self.assertListEqual(list(processed.attention_mask.shape			)     ,							list(processed[input_name].shape[:2]			)			)
           self.assertListEqual(processed.attention_mask.sum(-1			).tolist()     ,							_lowerCAmelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   List[str]	      =    self.feat_extract_dict
           snake_case__       :   List[str]	      =    True
           snake_case__       :   Tuple	      =    self.feature_extraction_class(**_lowerCAmelCase			)
           snake_case__       :   Any	      =    self.feat_extract_tester.prepare_inputs_for_target()
           snake_case__       :   Optional[int]	      =    [len(_lowerCAmelCase			) for x in speech_inputs]
           snake_case__       :   Optional[int]	      =    feat_extract.model_input_names[0]

           snake_case__       :   Optional[Any]	      =    BatchFeature({input_name: speech_inputs}			)
           snake_case__       :   Optional[Any]	      =    min(_lowerCAmelCase			)

           snake_case__       :   List[str]	      =    feat_extract.num_mel_bins  # hack!

           snake_case__       :   List[str]	      =    feat_extract.pad(
               _lowerCAmelCase     ,							padding='max_length'     ,							max_length=_lowerCAmelCase     ,							truncation=_lowerCAmelCase     ,							return_tensors='np'			)
           self.assertIn('attention_mask'     ,							_lowerCAmelCase			)
           self.assertListEqual(
               list(processed_pad.attention_mask.shape			)     ,							[processed_pad[input_name].shape[0], max_length]			)
           self.assertListEqual(
               processed_pad.attention_mask[:, :max_length].sum(-1			).tolist()     ,							[max_length for x in speech_inputs]			)





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					Tuple			):
           from datasets import load_dataset

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

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





     def 			_lowerCAmelCase      (     self     :					Any			):
           # fmt: off
           snake_case__       :   str	      =    torch.tensor(
               [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
                3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
                2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
                4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
                7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
                4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]			)
           # fmt: on

           snake_case__       :   int	      =    self._load_datasamples(1			)
           snake_case__       :   str	      =    SpeechTaFeatureExtractor()
           snake_case__       :   Optional[int]	      =    feature_extractor(_lowerCAmelCase     ,							return_tensors='pt'			).input_values
           self.assertEquals(input_values.shape     ,							(1, 93680)			)
           self.assertTrue(torch.allclose(input_values[0, :30]     ,							_lowerCAmelCase     ,							atol=1E-6			)			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           # fmt: off
           snake_case__       :   int	      =    torch.tensor(
               [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
                -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
                -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
                -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]			)
           # fmt: on

           snake_case__       :   Any	      =    self._load_datasamples(1			)
           snake_case__       :   str	      =    SpeechTaFeatureExtractor()
           snake_case__       :   Union[str, Any]	      =    feature_extractor(audio_target=_lowerCAmelCase     ,							return_tensors='pt'			).input_values
           self.assertEquals(input_values.shape     ,							(1, 366, 80)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,							_lowerCAmelCase     ,							atol=1E-4			)			)






import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowerCamelCase__					:  Union[str, Any]      							=				logging.get_logger(__name__)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Dict							,							*_lowerCAmelCase    :						Optional[Any]							,							**_lowerCAmelCase    :						Any		):
       warnings.warn(
           'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use LayoutLMv2ImageProcessor instead.'							,							_lowerCAmelCase							,							)
       super().__init__(*_lowerCAmelCase							,							**_lowerCAmelCase		)