





import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       snake_case__						(		__snake_case							):






							'''simple docstring'''



							__A     =							['''image_processor''', '''tokenizer''']
							__A     =							'''FlavaImageProcessor'''
							__A     =							('''BertTokenizer''', '''BertTokenizerFast''')


							def __init__(       self  :       Union[str, Any]					,			lowerCAmelCase_  :       Any=None					,			lowerCAmelCase_  :       List[Any]=None					,			**lowerCAmelCase_  :       str   )       ->		str:
												UpperCAmelCase_		=  None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''					,			lowerCAmelCase_					,			)
																	UpperCAmelCase_		=  kwargs.pop('''feature_extractor'''   )

												UpperCAmelCase_		=  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''   )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''   )

												super().__init__(lowerCAmelCase_					,			lowerCAmelCase_   )
												UpperCAmelCase_		=  self.image_processor


							def __call__(       self  :       List[Any]					,			lowerCAmelCase_  :       str = None					,			lowerCAmelCase_  :       Tuple = None					,			lowerCAmelCase_  :       Optional[int] = True					,			lowerCAmelCase_  :       Optional[Any] = False					,			lowerCAmelCase_  :       int = False					,			lowerCAmelCase_  :       Optional[int] = None					,			lowerCAmelCase_  :       Tuple = 0					,			lowerCAmelCase_  :       List[str] = None					,			lowerCAmelCase_  :       List[Any] = None					,			lowerCAmelCase_  :       List[str] = None					,			lowerCAmelCase_  :       Dict = None					,			lowerCAmelCase_  :       str = None					,			lowerCAmelCase_  :       Any = False					,			lowerCAmelCase_  :       Union[str, Any] = False					,			lowerCAmelCase_  :       Optional[Any] = False					,			lowerCAmelCase_  :       Optional[int] = False					,			lowerCAmelCase_  :       int = True					,			lowerCAmelCase_  :       Union[str, Any] = None					,			**lowerCAmelCase_  :       Dict					,			)       ->		List[Any]:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''   )

												if text is not None:
																	UpperCAmelCase_		=  self.tokenizer(
																	    text=lowerCAmelCase_					,			add_special_tokens=lowerCAmelCase_					,			padding=lowerCAmelCase_					,			truncation=lowerCAmelCase_					,			max_length=lowerCAmelCase_					,			stride=lowerCAmelCase_					,			pad_to_multiple_of=lowerCAmelCase_					,			return_token_type_ids=lowerCAmelCase_					,			return_attention_mask=lowerCAmelCase_					,			return_overflowing_tokens=lowerCAmelCase_					,			return_special_tokens_mask=lowerCAmelCase_					,			return_offsets_mapping=lowerCAmelCase_					,			return_length=lowerCAmelCase_					,			verbose=lowerCAmelCase_					,			return_tensors=lowerCAmelCase_					,			**lowerCAmelCase_					,			)
												if images is not None:
																	UpperCAmelCase_		=  self.image_processor(
																	    lowerCAmelCase_					,			return_image_mask=lowerCAmelCase_					,			return_codebook_pixels=lowerCAmelCase_					,			return_tensors=lowerCAmelCase_					,			**lowerCAmelCase_					,			)

												if text is not None and images is not None:
																	encoding.update(lowerCAmelCase_   )
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**lowerCAmelCase_   )					,			tensor_type=lowerCAmelCase_   )


							def 							UpperCamelCase      (       self  :       Optional[Any]					,			*lowerCAmelCase_  :       Optional[Any]					,			**lowerCAmelCase_  :       str   )       ->		List[str]:
												return self.tokenizer.batch_decode(*lowerCAmelCase_					,			**lowerCAmelCase_   )


							def 							UpperCamelCase      (       self  :       str					,			*lowerCAmelCase_  :       Optional[int]					,			**lowerCAmelCase_  :       Tuple   )       ->		Union[str, Any]:
												return self.tokenizer.decode(*lowerCAmelCase_					,			**lowerCAmelCase_   )


							@property
							def 							UpperCamelCase      (       self  :       Optional[Any]   )       ->		Optional[int]:
												UpperCAmelCase_		=  self.tokenizer.model_input_names
												UpperCAmelCase_		=  self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


							@property
							def 							UpperCamelCase      (       self  :       Optional[Any]   )       ->		Union[str, Any]:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,			lowerCAmelCase_					,			)
												return self.image_processor_class







							@property
							def 							UpperCamelCase      (       self  :       List[str]   )       ->		Optional[Any]:
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,			lowerCAmelCase_					,			)
												return self.image_processor






"""simple docstring"""






import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
			import torch

			from transformers import (
			    MPNetForMaskedLM,
			    MPNetForMultipleChoice,
			    MPNetForQuestionAnswering,
			    MPNetForSequenceClassification,
			    MPNetForTokenClassification,
			    MPNetModel,
			)






class lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=13    , lowercase=7    , lowercase=True    , lowercase=True    , lowercase=False    , lowercase=True    , lowercase=99    , lowercase=64    , lowercase=5    , lowercase=4    , lowercase=64    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=16    , lowercase=2    , lowercase=0.02    , lowercase=3    , lowercase=4    , lowercase=None    , ):
							_lowerCamelCase	:     List[str]       =       parent
							_lowerCamelCase	:     List[str]       =       batch_size
							_lowerCamelCase	:     Tuple       =       seq_length
							_lowerCamelCase	:     List[Any]       =       is_training
							_lowerCamelCase	:     int       =       use_input_mask
							_lowerCamelCase	:     Optional[Any]       =       use_token_type_ids
							_lowerCamelCase	:     List[Any]       =       use_labels
							_lowerCamelCase	:     str       =       vocab_size
							_lowerCamelCase	:     Optional[int]       =       hidden_size
							_lowerCamelCase	:     List[str]       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     List[str]       =       intermediate_size
							_lowerCamelCase	:     int       =       hidden_act
							_lowerCamelCase	:     Optional[int]       =       hidden_dropout_prob
							_lowerCamelCase	:     List[Any]       =       attention_probs_dropout_prob
							_lowerCamelCase	:     List[str]       =       max_position_embeddings
							_lowerCamelCase	:     List[Any]       =       type_vocab_size
							_lowerCamelCase	:     Optional[int]       =       type_sequence_label_size
							_lowerCamelCase	:     Union[str, Any]       =       initializer_range
							_lowerCamelCase	:     Tuple       =       num_labels
							_lowerCamelCase	:     int       =       num_choices
							_lowerCamelCase	:     int       =       scope
			def    A_       (    self    ):
							return MPNetConfig.from_pretrained('microsoft/mpnet-base'    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size    )

							_lowerCamelCase	:     Dict       =       None
							if self.use_input_mask:
											_lowerCamelCase	:     Optional[int]       =       random_attention_mask([self.batch_size, self.seq_length]    )

							_lowerCamelCase	:     int       =       None
							_lowerCamelCase	:     List[str]       =       None
							_lowerCamelCase	:     Any       =       None
							if self.use_labels:
											_lowerCamelCase	:     List[str]       =       ids_tensor([self.batch_size]    , self.type_sequence_label_size    )
											_lowerCamelCase	:     str       =       ids_tensor([self.batch_size, self.seq_length]    , self.num_labels    )
											_lowerCamelCase	:     Dict       =       ids_tensor([self.batch_size]    , self.num_choices    )

							_lowerCamelCase	:     int       =       self.get_config()
							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def    A_       (    self    ):
							return MPNetConfig(
							    vocab_size=self.vocab_size    , hidden_size=self.hidden_size    , num_hidden_layers=self.num_hidden_layers    , num_attention_heads=self.num_attention_heads    , intermediate_size=self.intermediate_size    , hidden_act=self.hidden_act    , hidden_dropout_prob=self.hidden_dropout_prob    , attention_probs_dropout_prob=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , initializer_range=self.initializer_range    , )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Optional[Any]       =       MPNetModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Tuple       =       model(lowercase    , lowercase    )
							_lowerCamelCase	:     Tuple       =       model(lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
							self.parent.assertEqual(result.pooler_output.shape    , (self.batch_size, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     str       =       MPNetForQuestionAnswering(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     str       =       model(
							    lowercase    , attention_mask=lowercase    , start_positions=lowercase    , end_positions=lowercase    , )
							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    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       self.num_labels
							_lowerCamelCase	:     int       =       MPNetForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Optional[Any]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     List[str]       =       self.num_choices
							_lowerCamelCase	:     List[str]       =       MPNetForMultipleChoice(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       input_ids.unsqueeze(1    ).expand(-1    , self.num_choices    , -1    ).contiguous()
							_lowerCamelCase	:     List[str]       =       input_mask.unsqueeze(1    ).expand(-1    , self.num_choices    , -1    ).contiguous()
							_lowerCamelCase	:     Any       =       model(
							    lowercase    , attention_mask=lowercase    , labels=lowercase    , )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_choices)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Any       =       self.num_labels
							_lowerCamelCase	:     Union[str, Any]       =       MPNetForTokenClassification(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.num_labels)    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.prepare_config_and_inputs()
							((_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase))	:     List[Any]       =       config_and_inputs
							_lowerCamelCase	:     str       =       {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     (
			    (
			        MPNetForMaskedLM,
			        MPNetForMultipleChoice,
			        MPNetForQuestionAnswering,
			        MPNetForSequenceClassification,
			        MPNetForTokenClassification,
			        MPNetModel,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__ =     (
			    {
			        """feature-extraction""": MPNetModel,
			        """fill-mask""": MPNetForMaskedLM,
			        """question-answering""": MPNetForQuestionAnswering,
			        """text-classification""": MPNetForSequenceClassification,
			        """token-classification""": MPNetForTokenClassification,
			        """zero-shot""": MPNetForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__ =     False
			lowerCamelCase__ =     True
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       MPNetModelTester(self    )
							_lowerCamelCase	:     List[Any]       =       ConfigTester(self    , config_class=lowercase    , hidden_size=37    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase    )






			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase    )






@require_torch
class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       MPNetModel.from_pretrained('microsoft/mpnet-base'    )
							_lowerCamelCase	:     Optional[Any]       =       torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
							_lowerCamelCase	:     Any       =       model(lowercase    )[0]
							_lowerCamelCase	:     Dict       =       torch.Size((1, 11, 768)    )
							self.assertEqual(output.shape    , lowercase    )
							_lowerCamelCase	:     List[Any]       =       torch.tensor(
							    [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]]    )
							# compare the actual values for a slice.
							self.assertTrue(torch.allclose(output[:, :3, :3]    , lowercase    , atol=1E-4    )    )