





"""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 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""




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




				def        __lowercase	(  self	:Any      ):
											return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''      )




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Optional[int]       =ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size      )

											__lowerCamelCase   :   int       =None
											if self.use_input_mask:
																		__lowerCamelCase   :   Union[str, Any]       =random_attention_mask([self.batch_size, self.seq_length]      )

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

											__lowerCamelCase   :   List[Any]       =self.get_config()
											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




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




				def        __lowercase	(  self	:Optional[Any]    ,     __lowercase	:str    ,     __lowercase	:Any    ,     __lowercase	:List[str]    ,     __lowercase	:int    ,     __lowercase	:str    ,     __lowercase	:Optional[Any]      ):
											__lowerCamelCase   :   Tuple       =self.num_choices
											__lowerCamelCase   :   int       =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[Any]       =input_mask.unsqueeze(1      ).expand(-1    ,     self.num_choices    ,     -1      ).contiguous()
											__lowerCamelCase   :   List[Any]       =model(
											    __lowercase    ,     attention_mask=__lowercase    ,     labels=__lowercase    ,     )
											self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_choices)      )




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




				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =self.prepare_config_and_inputs()
											((__lowerCamelCase)     ,    (__lowerCamelCase)     ,    (__lowerCamelCase)     ,    (__lowerCamelCase)     ,    (__lowerCamelCase)     ,    (__lowerCamelCase))   :   Optional[Any]       =config_and_inputs
											__lowerCamelCase   :   Any       ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict

@require_torch
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Any									=		(
				    (
				        MPNetForMaskedLM,
				        MPNetForMultipleChoice,
				        MPNetForQuestionAnswering,
				        MPNetForSequenceClassification,
				        MPNetForTokenClassification,
				        MPNetModel,
				    )
				    if is_torch_available()
				    else ()
				)
				__snake_case :							Dict									=		(
				    {
				        """feature-extraction""": MPNetModel,
				        """fill-mask""": MPNetForMaskedLM,
				        """question-answering""": MPNetForQuestionAnswering,
				        """text-classification""": MPNetForSequenceClassification,
				        """token-classification""": MPNetForTokenClassification,
				        """zero-shot""": MPNetForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__snake_case :							List[str]									=		False
				__snake_case :							str									=		True




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   Any       =MPNetModelTester(self      )
											__lowerCamelCase   :   Optional[Any]       =ConfigTester(self    ,     config_class=__lowercase    ,     hidden_size=37      )




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




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   Dict       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_model(*__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Optional[Any]       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase      )




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   List[Any]       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase      )




				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   int       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase      )




				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   Optional[Any]       =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase      )

@require_torch
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   Tuple       =MPNetModel.from_pretrained('''microsoft/mpnet-base'''      )
											__lowerCamelCase   :   int       =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
											__lowerCamelCase   :   str       =model(__lowercase      )[0]
											__lowerCamelCase   :   Optional[int]       =torch.Size((1, 11, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											__lowerCamelCase   :   int       =torch.tensor(
											    [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]      )
											# 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, is_vision_available


_UpperCamelCase					      =   {
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   ['Pix2StructImageProcessor']


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   [
							    'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Pix2StructPreTrainedModel',
							    'Pix2StructForConditionalGeneration',
							    'Pix2StructVisionModel',
							    'Pix2StructTextModel',
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_UpperCamelCase					      =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




