


'''simple docstring'''
import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    FunnelBaseModel,
				    FunnelForMaskedLM,
				    FunnelForMultipleChoice,
				    FunnelForPreTraining,
				    FunnelForQuestionAnswering,
				    FunnelForSequenceClassification,
				    FunnelForTokenClassification,
				    FunnelModel,
				)






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Union[str, Any]=13	,				_lowerCamelCase				:					Optional[Any]=7	,				_lowerCamelCase				:					List[Any]=True	,				_lowerCamelCase				:					int=True	,				_lowerCamelCase				:					List[str]=True	,				_lowerCamelCase				:					Any=True	,				_lowerCamelCase				:					int=99	,				_lowerCamelCase				:					Tuple=[1, 1, 2]	,				_lowerCamelCase				:					Tuple=1	,				_lowerCamelCase				:					int=32	,				_lowerCamelCase				:					List[Any]=4	,				_lowerCamelCase				:					Any=8	,				_lowerCamelCase				:					Dict=37	,				_lowerCamelCase				:					Optional[Any]="gelu_new"	,				_lowerCamelCase				:					Optional[Any]=0.1	,				_lowerCamelCase				:					str=0.1	,				_lowerCamelCase				:					Optional[Any]=0.0	,				_lowerCamelCase				:					int=5_12	,				_lowerCamelCase				:					List[Any]=3	,				_lowerCamelCase				:					Optional[int]=0.02	,				_lowerCamelCase				:					List[Any]=3	,				_lowerCamelCase				:					Optional[Any]=4	,				_lowerCamelCase				:					Dict=None	,				_lowerCamelCase				:					List[str]=False	,				)							->					Tuple:
										__magic_name__	  = parent
										__magic_name__	  = batch_size
										__magic_name__	  = seq_length
										__magic_name__	  = is_training
										__magic_name__	  = use_input_mask
										__magic_name__	  = use_token_type_ids
										__magic_name__	  = use_labels
										__magic_name__	  = vocab_size
										__magic_name__	  = block_sizes
										__magic_name__	  = num_decoder_layers
										__magic_name__	  = d_model
										__magic_name__	  = n_head
										__magic_name__	  = d_head
										__magic_name__	  = d_inner
										__magic_name__	  = hidden_act
										__magic_name__	  = hidden_dropout
										__magic_name__	  = attention_dropout
										__magic_name__	  = activation_dropout
										__magic_name__	  = max_position_embeddings
										__magic_name__	  = type_vocab_size
										__magic_name__	  = 2
										__magic_name__	  = num_labels
										__magic_name__	  = num_choices
										__magic_name__	  = scope
										__magic_name__	  = initializer_std

										# Used in the tests to check the size of the first attention layer
										__magic_name__	  = n_head
										# Used in the tests to check the size of the first hidden state
										__magic_name__	  = self.d_model
										# Used in the tests to check the number of output hidden states/attentions
										__magic_name__	  = sum(self.block_sizes  ) + (0 if base else self.num_decoder_layers)
										# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
										# the last hidden state of the first block (which is the first hidden state of the decoder).
										if not base:
														__magic_name__	  = self.num_hidden_layers + 2
						def 					__A					( self				:					Optional[int]  )							->					List[str]:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

										__magic_name__	  = None
										if self.use_input_mask:
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				vocab_size=2  )

										__magic_name__	  = None
										if self.use_token_type_ids:
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size  )

										__magic_name__	  = None
										__magic_name__	  = None
										__magic_name__	  = None
										if self.use_labels:
														__magic_name__	  = ids_tensor([self.batch_size]	,				self.type_sequence_label_size  )
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels  )
														__magic_name__	  = ids_tensor([self.batch_size]	,				self.num_choices  )
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				1  )

										__magic_name__	  = self.get_config()

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    fake_token_labels,
										)
						def 					__A					( self				:					str  )							->					List[str]:
										return FunnelConfig(
										    vocab_size=self.vocab_size	,				block_sizes=self.block_sizes	,				num_decoder_layers=self.num_decoder_layers	,				d_model=self.d_model	,				n_head=self.n_head	,				d_head=self.d_head	,				d_inner=self.d_inner	,				hidden_act=self.hidden_act	,				hidden_dropout=self.hidden_dropout	,				attention_dropout=self.attention_dropout	,				activation_dropout=self.activation_dropout	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				initializer_std=self.initializer_std	,				)
						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Dict	,				)							->					List[str]:
										__magic_name__	  = FunnelModel(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase  )
										__magic_name__	  = model(_lowerCamelCase	,				token_type_ids=_lowerCamelCase  )
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)  )

										__magic_name__	  = False
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)  )

										__magic_name__	  = False
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)  )
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					int	,				)							->					Optional[Any]:
										__magic_name__	  = FunnelBaseModel(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase  )
										__magic_name__	  = model(_lowerCamelCase	,				token_type_ids=_lowerCamelCase  )
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 2, self.d_model)  )

										__magic_name__	  = False
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 3, self.d_model)  )

										__magic_name__	  = False
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 2, self.d_model)  )
						def 					__A					( self				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					str	,				)							->					Union[str, Any]:
										__magic_name__	  = self.num_labels
										__magic_name__	  = FunnelForPreTraining(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase	,				labels=_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length)  )
						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[str]	,				)							->					Optional[int]:
										__magic_name__	  = FunnelForMaskedLM(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase	,				labels=_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)  )
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					int	,				)							->					str:
										__magic_name__	  = self.num_labels
										__magic_name__	  = FunnelForSequenceClassification(_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase	,				labels=_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)  )
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[Any]	,				)							->					Tuple:
										__magic_name__	  = self.num_choices
										__magic_name__	  = FunnelForMultipleChoice(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = input_ids.unsqueeze(1  ).expand(-1	,				self.num_choices	,				-1  ).contiguous()
										__magic_name__	  = token_type_ids.unsqueeze(1  ).expand(-1	,				self.num_choices	,				-1  ).contiguous()
										__magic_name__	  = input_mask.unsqueeze(1  ).expand(-1	,				self.num_choices	,				-1  ).contiguous()
										__magic_name__	  = model(
										    _lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase	,				labels=_lowerCamelCase	,				)
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_choices)  )
						def 					__A					( self				:					List[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					Tuple	,				)							->					Any:
										__magic_name__	  = self.num_labels
										__magic_name__	  = FunnelForTokenClassification(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase	,				labels=_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)  )
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					str	,				)							->					List[str]:
										__magic_name__	  = FunnelForQuestionAnswering(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(
										    _lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase	,				start_positions=_lowerCamelCase	,				end_positions=_lowerCamelCase	,				)
										self.parent.assertEqual(result.start_logits.shape	,				(self.batch_size, self.seq_length)  )
										self.parent.assertEqual(result.end_logits.shape	,				(self.batch_size, self.seq_length)  )

						def 					__A					( self				:					Dict  )							->					Tuple:
										__magic_name__	  = self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__      
										)  ,      (
										    __magic_name__      
										)  ,      (
										    __magic_name__      
										)  ,      (
										    __magic_name__      
										)  ,      (
										    __magic_name__      
										)  ,      (
										    __magic_name__      
										)  ,      (
										    __magic_name__      
										)  ,      (
										    __magic_name__      
										)  ,            
										)	  = config_and_inputs
										__magic_name__	  = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_torch
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     False
						UpperCAmelCase__	:       Tuple         =     False
						UpperCAmelCase__	:       List[str]         =     (
						    (
						        FunnelModel,
						        FunnelForMaskedLM,
						        FunnelForPreTraining,
						        FunnelForQuestionAnswering,
						        FunnelForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase__	:       int         =     (
						    {
						        '''feature-extraction''': (FunnelBaseModel, FunnelModel),
						        '''fill-mask''': FunnelForMaskedLM,
						        '''question-answering''': FunnelForQuestionAnswering,
						        '''text-classification''': FunnelForSequenceClassification,
						        '''token-classification''': FunnelForTokenClassification,
						        '''zero-shot''': FunnelForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						def 					__A					( self				:					List[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Dict=False  )							->					int:
										__magic_name__	  = super()._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase	,				return_labels=_lowerCamelCase  )

										if return_labels:
														if model_class in get_values(_lowerCamelCase  ):
																		__magic_name__	  = torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.seq_length)	,				dtype=torch.long	,				device=_lowerCamelCase  )
										return inputs_dict
						def 					__A					( self				:					int  )							->					Union[str, Any]:
										__magic_name__	  = FunnelModelTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase  )
						def 					__A					( self				:					Optional[int]  )							->					Optional[int]:
										self.config_tester.run_common_tests()
						def 					__A					( self				:					int  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase  )
						def 					__A					( self				:					str  )							->					Tuple:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase  )
						def 					__A					( self				:					List[Any]  )							->					List[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					Tuple:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )

						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					List[Any]  )							->					Dict:
										if hasattr(_lowerCamelCase	,				"weight"  ) and module.weight is not None:
														module.weight.data.fill_(3  )
										if hasattr(_lowerCamelCase	,				"bias"  ) and module.bias is not None:
														module.bias.data.fill_(3  )

										for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
														if hasattr(_lowerCamelCase	,				_lowerCamelCase  ) and getattr(_lowerCamelCase	,				_lowerCamelCase  ) is not None:
																		__magic_name__	  = getattr(_lowerCamelCase	,				_lowerCamelCase  )
																		weight.data.fill_(3  )






@require_torch
class 		UpperCamelCase_		(					A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[str]         =     False
						UpperCAmelCase__	:       int         =     False
						UpperCAmelCase__	:       str         =     (
						    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
						)
						def 					__A					( self				:					List[str]  )							->					Tuple:
										__magic_name__	  = FunnelModelTester(self	,				base=_lowerCamelCase  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					str:
										self.config_tester.run_common_tests()
						def 					__A					( self				:					int  )							->					List[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_base_model(*_lowerCamelCase  )
						def 					__A					( self				:					List[Any]  )							->					List[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					Any:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase  )
						def 					__A					( self				:					Optional[Any]  )							->					Dict:
										__magic_name__  ,      __magic_name__	  = self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__	  = True

										for model_class in self.all_model_classes:
														if model_class.__name__ == "FunnelBaseModel":
																		continue
														__magic_name__	  = model_class(_lowerCamelCase  )
														model.to(_lowerCamelCase  )
														model.train()
														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase	,				return_labels=_lowerCamelCase  )
														__magic_name__	  = model(**_lowerCamelCase  ).loss
														loss.backward()

						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					Optional[int]  )							->					List[str]:
										if hasattr(_lowerCamelCase	,				"weight"  ) and module.weight is not None:
														module.weight.data.fill_(3  )
										if hasattr(_lowerCamelCase	,				"bias"  ) and module.bias is not None:
														module.bias.data.fill_(3  )

										for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
														if hasattr(_lowerCamelCase	,				_lowerCamelCase  ) and getattr(_lowerCamelCase	,				_lowerCamelCase  ) is not None:
																		__magic_name__	  = getattr(_lowerCamelCase	,				_lowerCamelCase  )
																		weight.data.fill_(3  )






@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def 					__A					( self				:					Any  )							->					List[Any]:
										__magic_name__	  = 13
										__magic_name__	  = 7
										__magic_name__	  = torch.arange(0	,				batch_size * sequence_length  ).long().reshape(_lowerCamelCase	,				_lowerCamelCase  )
										__magic_name__	  = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
										__magic_name__	  = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]  )

										__magic_name__	  = FunnelModel.from_pretrained("sgugger/funnel-random-tiny"  )
										__magic_name__	  = model(_lowerCamelCase	,				token_type_ids=_lowerCamelCase  )[0].abs()

										__magic_name__	  = torch.tensor(23_44.83_52  )
										__magic_name__	  = torch.tensor(0.8_052  )
										self.assertTrue(torch.allclose(output.sum()	,				_lowerCamelCase	,				atol=1e-4  )  )
										self.assertTrue(torch.allclose(output.mean()	,				_lowerCamelCase	,				atol=1e-4  )  )

										__magic_name__	  = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]  )
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase	,				token_type_ids=_lowerCamelCase  )[0].abs()

										__magic_name__	  = torch.tensor(23_43.84_25  )
										__magic_name__	  = torch.tensor(0.8_049  )
										self.assertTrue(torch.allclose(output.sum()	,				_lowerCamelCase	,				atol=1e-4  )  )
										self.assertTrue(torch.allclose(output.mean()	,				_lowerCamelCase	,				atol=1e-4  )  )

						@slow
						def 					__A					( self				:					List[str]  )							->					str:
										__magic_name__	  = FunnelTokenizer.from_pretrained("huggingface/funnel-small"  )
										__magic_name__	  = FunnelModel.from_pretrained("huggingface/funnel-small"  )
										__magic_name__	  = tokenizer("Hello! I am the Funnel Transformer model."	,				return_tensors="pt"  )
										__magic_name__	  = model(**_lowerCamelCase  )[0]

										__magic_name__	  = torch.tensor(235.7_246  )
										__magic_name__	  = torch.tensor(0.0_256  )
										self.assertTrue(torch.allclose(output.sum()	,				_lowerCamelCase	,				atol=1e-4  )  )
										self.assertTrue(torch.allclose(output.mean()	,				_lowerCamelCase	,				atol=1e-4  )  )







'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__magic_name__   :   int  							=logging.get_logger(__name__)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					List[str]	,				*_lowerCamelCase				:					List[str]	,				**_lowerCamelCase				:					Optional[Any]  )							->					None:
										warnings.warn(
										    "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConvNextImageProcessor instead."	,				_lowerCamelCase	,				)
										super().__init__(*_lowerCamelCase	,				**_lowerCamelCase  )



