

"""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        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=13 ,						UpperCamelCase_=7 ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						UpperCamelCase_=False ,						UpperCamelCase_=True ,						UpperCamelCase_=99 ,						UpperCamelCase_=64 ,						UpperCamelCase_=5 ,						UpperCamelCase_=4 ,						UpperCamelCase_=64 ,						UpperCamelCase_="gelu" ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=512 ,						UpperCamelCase_=16 ,						UpperCamelCase_=2 ,						UpperCamelCase_=0.0_2 ,						UpperCamelCase_=3 ,						UpperCamelCase_=4 ,						UpperCamelCase_=None ,						):
      __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__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      attention_probs_dropout_prob
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      type_vocab_size
      __magic_name__     =      type_sequence_label_size
      __magic_name__     =      initializer_range
      __magic_name__     =      num_labels
      __magic_name__     =      num_choices
      __magic_name__     =      scope







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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size       )

      __magic_name__     =      None
      if self.use_input_mask:
       __magic_name__     =      random_attention_mask([self.batch_size, self.seq_length]       )

      __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__     =      self.get_config()
      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







     def 	lowerCAmelCase__    (      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      MPNetModel(config=__lowercase       )
      model.to(__lowercase       )
      model.eval()
      __magic_name__     =      model(__lowercase ,						__lowercase       )
      __magic_name__     =      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      MPNetForQuestionAnswering(config=__lowercase       )
      model.to(__lowercase       )
      model.eval()
      __magic_name__     =      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      self.num_labels
      __magic_name__     =      MPNetForSequenceClassification(__lowercase       )
      model.to(__lowercase       )
      model.eval()
      __magic_name__     =      model(__lowercase ,						attention_mask=__lowercase ,						labels=__lowercase       )
      self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      self.num_choices
      __magic_name__     =      MPNetForMultipleChoice(config=__lowercase       )
      model.to(__lowercase       )
      model.eval()
      __magic_name__     =      input_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(
          __lowercase ,						attention_mask=__lowercase ,						labels=__lowercase ,						)
      self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices)       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      self.num_labels
      __magic_name__     =      MPNetForTokenClassification(config=__lowercase       )
      model.to(__lowercase       )
      model.eval()
      __magic_name__     =      model(__lowercase ,						attention_mask=__lowercase ,						labels=__lowercase       )
      self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.prepare_config_and_inputs()
      ((__magic_name__)   ,						(__magic_name__)   ,						(__magic_name__)   ,						(__magic_name__)   ,						(__magic_name__)   ,						(__magic_name__))     =      config_and_inputs
      __magic_name__     =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict


@require_torch
class        _lowercase					(     lowercase__   , lowercase__   , unittest.TestCase     ):
     _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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      MPNetModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=__lowercase ,						hidden_size=37       )







     def 	lowerCAmelCase__    (      self       ):
      self.config_tester.run_common_tests()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*__lowercase       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase       )


@require_torch
class        _lowercase					(     unittest.TestCase     ):







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      MPNetModel.from_pretrained('''microsoft/mpnet-base'''       )
      __magic_name__     =      torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
      __magic_name__     =      model(__lowercase       )[0]
      __magic_name__     =      torch.Size((1, 11, 768)       )
      self.assertEqual(output.shape ,						__lowercase       )
      __magic_name__     =      torch.tensor(
          [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]       )
      # compare the actual values for a slice.
      self.assertTrue(torch.allclose(output[:, :3, :3] ,						__lowercase ,						atol=1E-4       )       )




"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      tempfile.mkdtemp()

      # fmt: off
      __magic_name__     =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
      # fmt: on
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

      __magic_name__     =      {
          '''do_resize''': True,
          '''size''': {'''height''': 18, '''width''': 18},
          '''do_normalize''': True,
          '''image_mean''': [0.5, 0.5, 0.5],
          '''image_std''': [0.5, 0.5, 0.5],
      }
      __magic_name__     =      os.path.join(self.tmpdirname ,						UpperCamelCase_       )
      with open(self.image_processor_file ,						'''w''' ,						encoding='''utf-8'''       ) as fp:
       json.dump(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return BertTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return ViTImageProcessor.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      shutil.rmtree(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [np.random.randint(255 ,						size=(3, 30, 400) ,						dtype=np.uinta       )]

      __magic_name__     =      [Image.fromarray(np.moveaxis(UpperCamelCase_ ,						0 ,						-1       )       ) for x in image_inputs]

      return image_inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_image_processor()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      processor.save_pretrained(self.tmpdirname       )
      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()       )
      processor.save_pretrained(self.tmpdirname       )

      __magic_name__     =      self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''       )
      __magic_name__     =      self.get_image_processor(do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(
          self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor_add_kwargs.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      image_processor(UpperCamelCase_ ,						return_tensors='''np'''       )
      __magic_name__     =      processor(images=UpperCamelCase_ ,						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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''

      __magic_name__     =      processor(text=UpperCamelCase_       )

      __magic_name__     =      tokenizer(UpperCamelCase_       )

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

      # test if it raises when no input is passed
      with self.assertRaises(UpperCamelCase_       ):
       processor()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __magic_name__     =      processor.batch_decode(UpperCamelCase_       )
      __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_       )

      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						processor.model_input_names       )

