import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import DonutSwinModel
     from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST







class      SCREAMING_SNAKE_CASE__					:

       def __init__(							self					:     Dict			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     str=13			,			_lowerCAmelCase					:     str=32			,			_lowerCAmelCase					:     List[str]=2			,			_lowerCAmelCase					:     Dict=3			,			_lowerCAmelCase					:     Dict=16			,			_lowerCAmelCase					:     Tuple=[1, 2, 1]			,			_lowerCAmelCase					:     Optional[int]=[2, 2, 4]			,			_lowerCAmelCase					:     Any=2			,			_lowerCAmelCase					:     Optional[Any]=2.0			,			_lowerCAmelCase					:     List[str]=True			,			_lowerCAmelCase					:     int=0.0			,			_lowerCAmelCase					:     Any=0.0			,			_lowerCAmelCase					:     Optional[Any]=0.1			,			_lowerCAmelCase					:     Any="gelu"			,			_lowerCAmelCase					:     str=False			,			_lowerCAmelCase					:     Optional[int]=True			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     Dict=1e-5			,			_lowerCAmelCase					:     Any=True			,			_lowerCAmelCase					:     Union[str, Any]=None			,			_lowerCAmelCase					:     List[Any]=True			,			_lowerCAmelCase					:     Optional[Any]=10			,			_lowerCAmelCase					:     Any=8			,			):
           __snake_case       :				str 			=    parent
           __snake_case       :				List[Any] 			=    batch_size
           __snake_case       :				Optional[int] 			=    image_size
           __snake_case       :				Tuple 			=    patch_size
           __snake_case       :				Union[str, Any] 			=    num_channels
           __snake_case       :				List[str] 			=    embed_dim
           __snake_case       :				List[Any] 			=    depths
           __snake_case       :				Tuple 			=    num_heads
           __snake_case       :				Union[str, Any] 			=    window_size
           __snake_case       :				Union[str, Any] 			=    mlp_ratio
           __snake_case       :				List[Any] 			=    qkv_bias
           __snake_case       :				Dict 			=    hidden_dropout_prob
           __snake_case       :				List[str] 			=    attention_probs_dropout_prob
           __snake_case       :				Dict 			=    drop_path_rate
           __snake_case       :				Any 			=    hidden_act
           __snake_case       :				Union[str, Any] 			=    use_absolute_embeddings
           __snake_case       :				Dict 			=    patch_norm
           __snake_case       :				int 			=    layer_norm_eps
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				str 			=    is_training
           __snake_case       :				Tuple 			=    scope
           __snake_case       :				Optional[int] 			=    use_labels
           __snake_case       :				Union[str, Any] 			=    type_sequence_label_size
           __snake_case       :				List[str] 			=    encoder_stride

       def 			snake_case__    (							self					:     int			):
           __snake_case       :				List[str] 			=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           __snake_case       :				int 			=    None
           if self.use_labels:
               __snake_case       :				Optional[int] 			=    ids_tensor([self.batch_size]			,			self.type_sequence_label_size			)

           __snake_case       :				str 			=    self.get_config()

           return config, pixel_values, labels

       def 			snake_case__    (							self					:     List[str]			):
           return DonutSwinConfig(
               image_size=self.image_size			,			patch_size=self.patch_size			,			num_channels=self.num_channels			,			embed_dim=self.embed_dim			,			depths=self.depths			,			num_heads=self.num_heads			,			window_size=self.window_size			,			mlp_ratio=self.mlp_ratio			,			qkv_bias=self.qkv_bias			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			drop_path_rate=self.drop_path_rate			,			hidden_act=self.hidden_act			,			use_absolute_embeddings=self.use_absolute_embeddings			,			path_norm=self.patch_norm			,			layer_norm_eps=self.layer_norm_eps			,			initializer_range=self.initializer_range			,			encoder_stride=self.encoder_stride			,			)

       def 			snake_case__    (							self					:     List[str]			,			_lowerCAmelCase					:     Dict			,			_lowerCAmelCase					:     List[str]			,			_lowerCAmelCase					:     Optional[Any]			):
           __snake_case       :				Tuple 			=    DonutSwinModel(config=lowerCAmelCase__			)
           model.to(lowerCAmelCase__			)
           model.eval()
           __snake_case       :				Optional[int] 			=    model(lowerCAmelCase__			)

           __snake_case       :				List[Any] 			=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
           __snake_case       :				Union[str, Any] 			=    int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

           self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, expected_seq_len, expected_dim)			)





       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				str 			=    self.prepare_config_and_inputs()
           (
               __snake_case				
           )       :				List[str] 			=    config_and_inputs
           __snake_case       :				Tuple 			=    {'''pixel_values''': pixel_values}
           return config, inputs_dict







@require_torch
class      SCREAMING_SNAKE_CASE__					(  _lowerCAmelCase   ,							_lowerCAmelCase   ,							unittest.TestCase    ):
       A							:	Dict          =	(DonutSwinModel,) if is_torch_available() else ()
       A							:	Optional[int]          =	{"feature-extraction": DonutSwinModel} if is_torch_available() else {}
       A							:	Optional[int]          =	True

       A							:	Optional[Any]          =	False
       A							:	List[Any]          =	False
       A							:	Optional[int]          =	False

       def 			snake_case__    (							self					:     List[str]			):
           __snake_case       :				Union[str, Any] 			=    DonutSwinModelTester(self			)
           __snake_case       :				Union[str, Any] 			=    ConfigTester(self			,			config_class=lowerCAmelCase__			,			embed_dim=37			)

       def 			snake_case__    (							self					:     int			):
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()

       def 			snake_case__    (							self					:     Any			):
           return

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				int 			=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCAmelCase__			)

       def 			snake_case__    (							self					:     List[Any]			):
           pass

       def 			snake_case__    (							self					:     Dict			):
           __snake_case       :				Optional[Any] 			=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __snake_case       :				Any 			=    model_class(lowerCAmelCase__			)
               self.assertIsInstance(model.get_input_embeddings()			,			(nn.Module)			)
               __snake_case       :				Union[str, Any] 			=    model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__			,			nn.Linear			)			)

       def 			snake_case__    (							self					:     List[str]			):
           __snake_case       :				Union[str, Any] 			=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __snake_case       :				Any 			=    model_class(lowerCAmelCase__			)
               __snake_case       :				List[Any] 			=    inspect.signature(model.forward			)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __snake_case       :				str 			=    [*signature.parameters.keys()]

               __snake_case       :				str 			=    ['''pixel_values''']
               self.assertListEqual(arg_names[:1]			,			lowerCAmelCase__			)

       def 			snake_case__    (							self					:     List[str]			):
           __snake_case       :				Union[str, Any] 			=    self.model_tester.prepare_config_and_inputs_for_common()
           __snake_case       :				Optional[Any] 			=    True

           for model_class in self.all_model_classes:
               __snake_case       :				str 			=    True
               __snake_case       :				int 			=    False
               __snake_case       :				int 			=    True
               __snake_case       :				Dict 			=    model_class(lowerCAmelCase__			)
               model.to(lowerCAmelCase__			)
               model.eval()
               with torch.no_grad():
                   __snake_case       :				Dict 			=    model(**self._prepare_for_class(lowerCAmelCase__			,			lowerCAmelCase__			)			)
               __snake_case       :				Union[str, Any] 			=    outputs.attentions
               __snake_case       :				Any 			=    len(self.model_tester.depths			)
               self.assertEqual(len(lowerCAmelCase__			)			,			lowerCAmelCase__			)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __snake_case       :				Optional[Any] 			=    True
               __snake_case       :				str 			=    config.window_size**2
               __snake_case       :				Dict 			=    model_class(lowerCAmelCase__			)
               model.to(lowerCAmelCase__			)
               model.eval()
               with torch.no_grad():
                   __snake_case       :				Optional[Any] 			=    model(**self._prepare_for_class(lowerCAmelCase__			,			lowerCAmelCase__			)			)
               __snake_case       :				str 			=    outputs.attentions
               self.assertEqual(len(lowerCAmelCase__			)			,			lowerCAmelCase__			)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]			)			,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,			)
               __snake_case       :				List[Any] 			=    len(lowerCAmelCase__			)

               # Check attention is always last and order is fine
               __snake_case       :				List[str] 			=    True
               __snake_case       :				Dict 			=    True
               __snake_case       :				Any 			=    model_class(lowerCAmelCase__			)
               model.to(lowerCAmelCase__			)
               model.eval()
               with torch.no_grad():
                   __snake_case       :				int 			=    model(**self._prepare_for_class(lowerCAmelCase__			,			lowerCAmelCase__			)			)

               if hasattr(self.model_tester			,			"""num_hidden_states_types"""			):
                   __snake_case       :				int 			=    self.model_tester.num_hidden_states_types
               else:
                   # also another +1 for reshaped_hidden_states
                   __snake_case       :				Any 			=    2
               self.assertEqual(out_len + added_hidden_states			,			len(lowerCAmelCase__			)			)

               __snake_case       :				List[str] 			=    outputs.attentions

               self.assertEqual(len(lowerCAmelCase__			)			,			lowerCAmelCase__			)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]			)			,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,			)

       def 			snake_case__    (							self					:     List[str]			,			_lowerCAmelCase					:     Optional[Any]			,			_lowerCAmelCase					:     List[Any]			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     Optional[Any]			):
           __snake_case       :				Union[str, Any] 			=    model_class(lowerCAmelCase__			)
           model.to(lowerCAmelCase__			)
           model.eval()

           with torch.no_grad():
               __snake_case       :				Optional[int] 			=    model(**self._prepare_for_class(lowerCAmelCase__			,			lowerCAmelCase__			)			)

           __snake_case       :				Any 			=    outputs.hidden_states

           __snake_case       :				Tuple 			=    getattr(
               self.model_tester			,			"""expected_num_hidden_layers"""			,			len(self.model_tester.depths			) + 1			)
           self.assertEqual(len(lowerCAmelCase__			)			,			lowerCAmelCase__			)

           # DonutSwin has a different seq_length
           __snake_case       :				List[Any] 			=    (
               config.patch_size
               if isinstance(config.patch_size			,			collections.abc.Iterable			)
               else (config.patch_size, config.patch_size)
           )

           __snake_case       :				List[Any] 			=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]			)			,			[num_patches, self.model_tester.embed_dim]			,			)

           __snake_case       :				Union[str, Any] 			=    outputs.reshaped_hidden_states
           self.assertEqual(len(lowerCAmelCase__			)			,			lowerCAmelCase__			)

           __snake_case       :				Union[str, Any] 			=    reshaped_hidden_states[0].shape
           __snake_case       :				Optional[Any] 			=    (
               reshaped_hidden_states[0].view(lowerCAmelCase__			,			lowerCAmelCase__			,			height * width			).permute(0			,			2			,			1			)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]			)			,			[num_patches, self.model_tester.embed_dim]			,			)

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				Optional[Any] 			=    self.model_tester.prepare_config_and_inputs_for_common()

           __snake_case       :				int 			=    (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size			,			collections.abc.Iterable			)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
               __snake_case       :				int 			=    True
               self.check_hidden_states_output(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __snake_case       :				Any 			=    True

               self.check_hidden_states_output(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			)

       def 			snake_case__    (							self					:     Dict			):
           __snake_case       :				int 			=    self.model_tester.prepare_config_and_inputs_for_common()
           __snake_case       :				Any 			=    3

           __snake_case       :				Tuple 			=    (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size			,			collections.abc.Iterable			)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           __snake_case       :				Any 			=    (
               config.patch_size
               if isinstance(config.patch_size			,			collections.abc.Iterable			)
               else (config.patch_size, config.patch_size)
           )

           __snake_case       :				Optional[int] 			=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __snake_case       :				int 			=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
               __snake_case       :				Union[str, Any] 			=    True
               self.check_hidden_states_output(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			(padded_height, padded_width)			)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __snake_case       :				Dict 			=    True
               self.check_hidden_states_output(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			(padded_height, padded_width)			)

       @slow
       def 			snake_case__    (							self					:     Any			):



           for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __snake_case       :				Union[str, Any] 			=    DonutSwinModel.from_pretrained(lowerCAmelCase__			)
               self.assertIsNotNone(lowerCAmelCase__			)





       def 			snake_case__    (							self					:     str			):
           __snake_case       :				Tuple 			=    self.model_tester.prepare_config_and_inputs_for_common()

           __snake_case       :				str 			=    _config_zero_init(lowerCAmelCase__			)
           for model_class in self.all_model_classes:
               __snake_case       :				Optional[Any] 			=    model_class(config=lowerCAmelCase__			)
               for name, param in model.named_parameters():
                   if "embeddings" not in name and param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1e9).round() / 1e9).item()			,			[0.0, 1.0]			,			msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,			)










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 					_a		( unittest.TestCase							):






				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''				)
										_UpperCamelCase     :   Tuple      =       dict(zip(lowerCAmelCase__,					range(len(lowerCAmelCase__				)				)				)				)

										_UpperCamelCase     :   Any      =       {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										_UpperCamelCase     :   Optional[int]      =       {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										_UpperCamelCase     :   List[str]      =       tempfile.mkdtemp()
										_UpperCamelCase     :   Any      =       os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']				)
										_UpperCamelCase     :   Optional[int]      =       os.path.join(self.tmpdirname,					lowerCAmelCase__				)
										with open(self.vocab_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)

										with open(self.feature_extraction_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)






				def 							snake_case      (					self						:       List[Any],					**lowerCAmelCase__						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase__				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					**lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()
										_UpperCamelCase     :   str      =       self.get_feature_extractor()

										_UpperCamelCase     :   str      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										processor.save_pretrained(self.tmpdirname				)
										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor.from_pretrained(self.tmpdirname				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       str				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       WavaVecaProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										_UpperCamelCase     :   Tuple      =       self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''				)
										_UpperCamelCase     :   Any      =       self.get_feature_extractor(do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										_UpperCamelCase     :   int      =       WavaVecaProcessor.from_pretrained(
										    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor_add_kwargs.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.get_feature_extractor()
										_UpperCamelCase     :   str      =       self.get_tokenizer()

										_UpperCamelCase     :   Union[str, Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       floats_list((3, 1_0_0_0)				)

										_UpperCamelCase     :   int      =       feature_extractor(lowerCAmelCase__,					return_tensors='''np'''				)
										_UpperCamelCase     :   List[Any]      =       processor(lowerCAmelCase__,					return_tensors='''np'''				)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2				)






				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.get_feature_extractor()
										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       '''This is a test string'''

										_UpperCamelCase     :   List[Any]      =       processor(text=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       tokenizer(lowerCAmelCase__				)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],					encoded_processor[key]				)






				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_feature_extractor()
										_UpperCamelCase     :   Dict      =       self.get_tokenizer()

										_UpperCamelCase     :   List[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase     :   List[str]      =       processor.batch_decode(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer.batch_decode(lowerCAmelCase__				)

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.get_feature_extractor()
										_UpperCamelCase     :   List[Any]      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[int]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										self.assertListEqual(
										    processor.model_input_names,					feature_extractor.model_input_names,					msg='''`processor` and `feature_extractor` model input names do not match''',					)



