'''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 					__magic_name__		:







			def __init__(			self       :						List[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Tuple=13       ,    lowercase_       :						List[Any]=7       ,    lowercase_       :						List[Any]=True       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						Optional[Any]=False       ,    lowercase_       :						List[str]=True       ,    lowercase_       :						str=99       ,    lowercase_       :						Any=64       ,    lowercase_       :						List[Any]=5       ,    lowercase_       :						List[Any]=4       ,    lowercase_       :						List[Any]=64       ,    lowercase_       :						Tuple="gelu"       ,    lowercase_       :						Tuple=0.1       ,    lowercase_       :						Any=0.1       ,    lowercase_       :						Tuple=512       ,    lowercase_       :						Any=16       ,    lowercase_       :						Tuple=2       ,    lowercase_       :						Tuple=0.02       ,    lowercase_       :						Dict=3       ,    lowercase_       :						Tuple=4       ,    lowercase_       :						Optional[int]=None       ,    ):
										lowercase_      :    str       		= parent
										lowercase_      :    List[Any]       		= batch_size
										lowercase_      :    str       		= seq_length
										lowercase_      :    Optional[Any]       		= is_training
										lowercase_      :    List[str]       		= use_input_mask
										lowercase_      :    Optional[int]       		= use_token_type_ids
										lowercase_      :    Any       		= use_labels
										lowercase_      :    Any       		= vocab_size
										lowercase_      :    Optional[int]       		= hidden_size
										lowercase_      :    Tuple       		= num_hidden_layers
										lowercase_      :    str       		= num_attention_heads
										lowercase_      :    Tuple       		= intermediate_size
										lowercase_      :    List[str]       		= hidden_act
										lowercase_      :    Optional[Any]       		= hidden_dropout_prob
										lowercase_      :    List[str]       		= attention_probs_dropout_prob
										lowercase_      :    Tuple       		= max_position_embeddings
										lowercase_      :    List[Any]       		= type_vocab_size
										lowercase_      :    int       		= type_sequence_label_size
										lowercase_      :    Any       		= initializer_range
										lowercase_      :    int       		= num_labels
										lowercase_      :    int       		= num_choices
										lowercase_      :    List[str]       		= scope







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										lowercase_      :    Optional[int]       		= ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size					)

										lowercase_      :    Optional[Any]       		= None
										if self.use_input_mask:
																	lowercase_      :    str       		= random_attention_mask([self.batch_size, self.seq_length]					)

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

										lowercase_      :    Union[str, Any]       		= self.get_config()
										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										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 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Any       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						str       ,    lowercase_       :						Any       ,    lowercase_       :						Union[str, Any]					):
										lowercase_      :    Optional[Any]       		= MPNetModel(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    Optional[int]       		= model(lowercase_       ,    lowercase_					)
										lowercase_      :    int       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						int       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						str       ,    lowercase_       :						Any					):
										lowercase_      :    Union[str, Any]       		= MPNetForQuestionAnswering(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    List[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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						str       ,    lowercase_       :						Any       ,    lowercase_       :						int       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Any       		= self.num_labels
										lowercase_      :    Tuple       		= MPNetForSequenceClassification(lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    List[str]       		= model(lowercase_       ,    attention_mask=lowercase_       ,    labels=lowercase_					)
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Tuple       ,    lowercase_       :						str       ,    lowercase_       :						str       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Any       ,    lowercase_       :						Optional[Any]					):
										lowercase_      :    List[str]       		= self.num_choices
										lowercase_      :    List[str]       		= MPNetForMultipleChoice(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    Any       		= input_ids.unsqueeze(1					).expand(-1       ,    self.num_choices       ,    -1					).contiguous()
										lowercase_      :    Optional[int]       		= input_mask.unsqueeze(1					).expand(-1       ,    self.num_choices       ,    -1					).contiguous()
										lowercase_      :    Optional[Any]       		= model(
										    lowercase_       ,    attention_mask=lowercase_       ,    labels=lowercase_       ,    )
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_choices)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Any       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Optional[int]					):
										lowercase_      :    Union[str, Any]       		= self.num_labels
										lowercase_      :    Any       		= MPNetForTokenClassification(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    List[Any]       		= model(lowercase_       ,    attention_mask=lowercase_       ,    labels=lowercase_					)
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.num_labels)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= self.prepare_config_and_inputs()
										((lowercase_)		,		(lowercase_)		,		(lowercase_)		,		(lowercase_)		,		(lowercase_)		,		(lowercase_))      :    int       		= config_and_inputs
										lowercase_      :    Optional[int]       		= {"""input_ids""": input_ids, """attention_mask""": input_mask}
										return config, inputs_dict





@require_torch
class 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = (
			    (
			        MPNetForMaskedLM,
			        MPNetForMultipleChoice,
			        MPNetForQuestionAnswering,
			        MPNetForSequenceClassification,
			        MPNetForTokenClassification,
			        MPNetModel,
			    )
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase__       = (
			    {
			        '''feature-extraction''': MPNetModel,
			        '''fill-mask''': MPNetForMaskedLM,
			        '''question-answering''': MPNetForQuestionAnswering,
			        '''text-classification''': MPNetForSequenceClassification,
			        '''token-classification''': MPNetForTokenClassification,
			        '''zero-shot''': MPNetForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__       = False
			UpperCamelCase__       = True







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										lowercase_      :    List[Any]       		= MPNetModelTester(self					)
										lowercase_      :    Tuple       		= ConfigTester(self       ,    config_class=lowercase_       ,    hidden_size=37					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										self.config_tester.run_common_tests()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Optional[int]       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_model(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										lowercase_      :    Any       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    str       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    List[Any]       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Tuple       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_					)





@require_torch
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    List[str]       		= MPNetModel.from_pretrained("""microsoft/mpnet-base"""					)
										lowercase_      :    Dict       		= torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
										lowercase_      :    List[Any]       		= model(lowercase_					)[0]
										lowercase_      :    Optional[Any]       		= torch.Size((1, 11, 768)					)
										self.assertEqual(output.shape       ,    lowercase_					)
										lowercase_      :    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					)					)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





