





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ConvNextImageProcessor
class      _UpperCamelCase (			unittest.TestCase						):
      def __init__(     self				:Optional[int]	, lowerCamelCase				:str	, lowerCamelCase				:List[Any]=7	, lowerCamelCase				:Optional[int]=3	, lowerCamelCase				:int=18	, lowerCamelCase				:Dict=30	, lowerCamelCase				:Optional[Any]=400	, lowerCamelCase				:List[str]=True	, lowerCamelCase				:List[str]=None	, lowerCamelCase				:str=0.8_75	, lowerCamelCase				:List[Any]=True	, lowerCamelCase				:Dict=[0.5, 0.5, 0.5]	, lowerCamelCase				:Dict=[0.5, 0.5, 0.5]	, )       ->     int:
       UpperCAmelCase__								=  size if size is not None else {"shortest_edge": 20}
       UpperCAmelCase__								=  parent
       UpperCAmelCase__								=  batch_size
       UpperCAmelCase__								=  num_channels
       UpperCAmelCase__								=  image_size
       UpperCAmelCase__								=  min_resolution
       UpperCAmelCase__								=  max_resolution
       UpperCAmelCase__								=  do_resize
       UpperCAmelCase__								=  size
       UpperCAmelCase__								=  crop_pct
       UpperCAmelCase__								=  do_normalize
       UpperCAmelCase__								=  image_mean
       UpperCAmelCase__								=  image_std







      def 		UpperCAmelCase_  (     self				:Any )       ->     Dict:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }



@require_torch
@require_vision
class      _UpperCamelCase (			lowerCAmelCase		,    unittest.TestCase						):
      UpperCAmelCase_            =						ConvNextImageProcessor if is_vision_available() else None
      def 		UpperCAmelCase_  (     self				:int )       ->     int:
       UpperCAmelCase__								=  ConvNextImageProcessingTester(self )
      @property
      def 		UpperCAmelCase_  (     self				:str )       ->     Tuple:
       return self.image_processor_tester.prepare_image_processor_dict()
      def 		UpperCAmelCase_  (     self				:Dict )       ->     Optional[Any]:
       UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCamelCase	, "do_resize" ) )
       self.assertTrue(hasattr(lowerCamelCase	, "size" ) )
       self.assertTrue(hasattr(lowerCamelCase	, "crop_pct" ) )
       self.assertTrue(hasattr(lowerCamelCase	, "do_normalize" ) )
       self.assertTrue(hasattr(lowerCamelCase	, "image_mean" ) )
       self.assertTrue(hasattr(lowerCamelCase	, "image_std" ) )
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     Optional[int]:
       UpperCAmelCase__								=  self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size	, {"shortest_edge": 20} )

       UpperCAmelCase__								=  self.image_processing_class.from_dict(self.image_processor_dict	, size=42 )
       self.assertEqual(image_processor.size	, {"shortest_edge": 42} )
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     List[Any]:
       pass
      def 		UpperCAmelCase_  (     self				:str )       ->     str:
       # Initialize image_processing
       UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       UpperCAmelCase__								=  prepare_image_inputs(self.image_processor_tester	, equal_resolution=lowerCamelCase )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase	, Image.Image )

       # Test not batched input
       UpperCAmelCase__								=  image_processing(image_inputs[0]	, return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape	, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )	, )

       # Test batched
       UpperCAmelCase__								=  image_processing(lowerCamelCase	, return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape	, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )	, )
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     int:
       # Initialize image_processing
       UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       UpperCAmelCase__								=  prepare_image_inputs(self.image_processor_tester	, equal_resolution=lowerCamelCase	, numpify=lowerCamelCase )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase	, np.ndarray )

       # Test not batched input
       UpperCAmelCase__								=  image_processing(image_inputs[0]	, return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape	, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )	, )

       # Test batched
       UpperCAmelCase__								=  image_processing(lowerCamelCase	, return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape	, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )	, )







      def 		UpperCAmelCase_  (     self				:int )       ->     Any:
       # Initialize image_processing
       UpperCAmelCase__								=  self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       UpperCAmelCase__								=  prepare_image_inputs(self.image_processor_tester	, equal_resolution=lowerCamelCase	, torchify=lowerCamelCase )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase	, torch.Tensor )

       # Test not batched input
       UpperCAmelCase__								=  image_processing(image_inputs[0]	, return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape	, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )	, )

       # Test batched
       UpperCAmelCase__								=  image_processing(lowerCamelCase	, return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape	, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )	, )











import unittest

from transformers import YosoConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      YosoForMaskedLM,
      YosoForMultipleChoice,
      YosoForQuestionAnswering,
      YosoForSequenceClassification,
      YosoForTokenClassification,
      YosoModel,
  )
  from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST
class      _UpperCamelCase :
      def __init__(     self				:int	, lowerCamelCase				:int	, lowerCamelCase				:Optional[Any]=13	, lowerCamelCase				:Optional[Any]=7	, lowerCamelCase				:Union[str, Any]=True	, lowerCamelCase				:Tuple=True	, lowerCamelCase				:Optional[Any]=True	, lowerCamelCase				:str=True	, lowerCamelCase				:Any=99	, lowerCamelCase				:Optional[int]=32	, lowerCamelCase				:Any=5	, lowerCamelCase				:List[Any]=4	, lowerCamelCase				:Optional[Any]=37	, lowerCamelCase				:Tuple="gelu"	, lowerCamelCase				:Optional[int]=0.1	, lowerCamelCase				:Any=0.1	, lowerCamelCase				:List[Any]=512	, lowerCamelCase				:Tuple=16	, lowerCamelCase				:Optional[int]=2	, lowerCamelCase				:str=0.02	, lowerCamelCase				:Dict=3	, lowerCamelCase				:Union[str, Any]=4	, lowerCamelCase				:Union[str, Any]=None	, )       ->     List[Any]:
       UpperCAmelCase__								=  parent
       UpperCAmelCase__								=  batch_size
       UpperCAmelCase__								=  seq_length
       UpperCAmelCase__								=  is_training
       UpperCAmelCase__								=  use_input_mask
       UpperCAmelCase__								=  use_token_type_ids
       UpperCAmelCase__								=  use_labels
       UpperCAmelCase__								=  vocab_size
       UpperCAmelCase__								=  hidden_size
       UpperCAmelCase__								=  num_hidden_layers
       UpperCAmelCase__								=  num_attention_heads
       UpperCAmelCase__								=  intermediate_size
       UpperCAmelCase__								=  hidden_act
       UpperCAmelCase__								=  hidden_dropout_prob
       UpperCAmelCase__								=  attention_probs_dropout_prob
       UpperCAmelCase__								=  max_position_embeddings
       UpperCAmelCase__								=  type_vocab_size
       UpperCAmelCase__								=  type_sequence_label_size
       UpperCAmelCase__								=  initializer_range
       UpperCAmelCase__								=  num_labels
       UpperCAmelCase__								=  num_choices
       UpperCAmelCase__								=  scope
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     Any:
       UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size )

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

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

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

       UpperCAmelCase__								=  self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     Optional[Any]:
       return YosoConfig(
           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	, type_vocab_size=self.type_vocab_size	, is_decoder=lowerCamelCase	, initializer_range=self.initializer_range	, )
      def 		UpperCAmelCase_  (     self				:int )       ->     Any:
       UpperCAmelCase__								=  self.get_config()
       UpperCAmelCase__								=  300
       return config
      def 		UpperCAmelCase_  (     self				:List[str] )       ->     Tuple:
       (
           (
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	
       )								=  self.prepare_config_and_inputs()

       UpperCAmelCase__								=  True
       UpperCAmelCase__								=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )
      def 		UpperCAmelCase_  (     self				:Any	, lowerCamelCase				:List[str]	, lowerCamelCase				:List[str]	, lowerCamelCase				:str	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:Any	, lowerCamelCase				:Tuple )       ->     List[Any]:
       UpperCAmelCase__								=  YosoModel(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase )
       UpperCAmelCase__								=  model(lowerCamelCase	, token_type_ids=lowerCamelCase )
       UpperCAmelCase__								=  model(lowerCamelCase )
       self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size) )
      def 		UpperCAmelCase_  (     self				:Any	, lowerCamelCase				:str	, lowerCamelCase				:Dict	, lowerCamelCase				:Any	, lowerCamelCase				:Any	, lowerCamelCase				:int	, lowerCamelCase				:Dict	, lowerCamelCase				:Any	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:List[Any]	, )       ->     Optional[int]:
       UpperCAmelCase__								=  True
       UpperCAmelCase__								=  YosoModel(lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(
           lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, encoder_hidden_states=lowerCamelCase	, encoder_attention_mask=lowerCamelCase	, )
       UpperCAmelCase__								=  model(
           lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, encoder_hidden_states=lowerCamelCase	, )
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase )
       self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size) )
      def 		UpperCAmelCase_  (     self				:Tuple	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Tuple	, lowerCamelCase				:List[str]	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:Tuple	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:int )       ->     Optional[int]:
       UpperCAmelCase__								=  YosoForMaskedLM(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  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 		UpperCAmelCase_  (     self				:Dict	, lowerCamelCase				:str	, lowerCamelCase				:Dict	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:int	, lowerCamelCase				:Optional[Any] )       ->     Any:
       UpperCAmelCase__								=  YosoForQuestionAnswering(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  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 		UpperCAmelCase_  (     self				:Optional[Any]	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:Any	, lowerCamelCase				:List[str]	, lowerCamelCase				:Dict	, lowerCamelCase				:str	, lowerCamelCase				:Tuple	, lowerCamelCase				:int )       ->     Dict:
       UpperCAmelCase__								=  self.num_labels
       UpperCAmelCase__								=  YosoForSequenceClassification(lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels) )
      def 		UpperCAmelCase_  (     self				:Tuple	, lowerCamelCase				:str	, lowerCamelCase				:int	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[str]	, lowerCamelCase				:Dict	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:str )       ->     Dict:
       UpperCAmelCase__								=  self.num_labels
       UpperCAmelCase__								=  YosoForTokenClassification(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  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 		UpperCAmelCase_  (     self				:Optional[int]	, lowerCamelCase				:Tuple	, lowerCamelCase				:Tuple	, lowerCamelCase				:int	, lowerCamelCase				:Tuple	, lowerCamelCase				:List[str]	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[str] )       ->     List[Any]:
       UpperCAmelCase__								=  self.num_choices
       UpperCAmelCase__								=  YosoForMultipleChoice(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  input_ids.unsqueeze(1 ).expand(-1	, self.num_choices	, -1 ).contiguous()
       UpperCAmelCase__								=  token_type_ids.unsqueeze(1 ).expand(-1	, self.num_choices	, -1 ).contiguous()
       UpperCAmelCase__								=  input_mask.unsqueeze(1 ).expand(-1	, self.num_choices	, -1 ).contiguous()
       UpperCAmelCase__								=  model(
           lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase	, )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_choices) )







      def 		UpperCAmelCase_  (     self				:int )       ->     List[Any]:
       UpperCAmelCase__								=  self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	(
           UpperCAmelCase__
       )	,	
       )								=  config_and_inputs
       UpperCAmelCase__								=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_torch
class      _UpperCamelCase (			lowerCAmelCase		,    lowerCAmelCase		,    unittest.TestCase						):
      UpperCAmelCase_            =						(
          (
              YosoModel,
              YosoForMaskedLM,
              YosoForMultipleChoice,
              YosoForQuestionAnswering,
              YosoForSequenceClassification,
              YosoForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_            =						False
      UpperCAmelCase_            =						False
      UpperCAmelCase_            =						False

      UpperCAmelCase_            =						()
      UpperCAmelCase_            =						(
          {
              """feature-extraction""": YosoModel,
              """fill-mask""": YosoForMaskedLM,
              """question-answering""": YosoForQuestionAnswering,
              """text-classification""": YosoForSequenceClassification,
              """token-classification""": YosoForTokenClassification,
              """zero-shot""": YosoForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      def 		UpperCAmelCase_  (     self				:Any )       ->     Optional[Any]:
       UpperCAmelCase__								=  YosoModelTester(self )
       UpperCAmelCase__								=  ConfigTester(self	, config_class=lowerCamelCase	, hidden_size=37 )
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     List[str]:
       self.config_tester.run_common_tests()
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     List[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     Optional[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        UpperCAmelCase__								=  type
        self.model_tester.create_and_check_model(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     Optional[int]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     Dict:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:str )       ->     Optional[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Any )       ->     List[str]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Any )       ->     str:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
      @slow
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     Optional[int]:
       for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__								=  YosoModel.from_pretrained(lowerCamelCase )
        self.assertIsNotNone(lowerCamelCase )







      def 		UpperCAmelCase_  (     self				:int )       ->     List[str]:
       return



@require_torch
class      _UpperCamelCase (			unittest.TestCase						):
      @slow
      def 		UpperCAmelCase_  (     self				:Optional[Any] )       ->     Optional[int]:
       UpperCAmelCase__								=  YosoModel.from_pretrained("uw-madison/yoso-4096" )
       UpperCAmelCase__								=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

       with torch.no_grad():
        UpperCAmelCase__								=  model(lowerCamelCase )[0]

       UpperCAmelCase__								=  torch.Size((1, 6, 768) )
       self.assertEqual(output.shape	, lowerCamelCase )

       UpperCAmelCase__								=  torch.tensor(
           [[[-0.06_11, 0.12_42, 0.08_40], [0.02_80, -0.00_48, 0.11_25], [0.01_06, 0.02_26, 0.07_51]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]	, lowerCamelCase	, atol=1e-4 ) )
      @slow
      def 		UpperCAmelCase_  (     self				:Optional[Any] )       ->     List[Any]:
       UpperCAmelCase__								=  YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096" )
       UpperCAmelCase__								=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

       with torch.no_grad():
        UpperCAmelCase__								=  model(lowerCamelCase )[0]

       UpperCAmelCase__								=  5_0265

       UpperCAmelCase__								=  torch.Size((1, 6, vocab_size) )
       self.assertEqual(output.shape	, lowerCamelCase )

       UpperCAmelCase__								=  torch.tensor(
           [[[-2.13_13, -3.72_85, -2.24_07], [-2.70_47, -3.33_14, -2.64_08], [0.06_29, -2.51_66, -0.33_56]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]	, lowerCamelCase	, atol=1e-4 ) )







      @slow
      def 		UpperCAmelCase_  (     self				:Any )       ->     str:
       UpperCAmelCase__								=  YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096" )
       UpperCAmelCase__								=  torch.arange(4096 ).unsqueeze(0 )

       with torch.no_grad():
        UpperCAmelCase__								=  model(lowerCamelCase )[0]

       UpperCAmelCase__								=  5_0265

       UpperCAmelCase__								=  torch.Size((1, 4096, vocab_size) )
       self.assertEqual(output.shape	, lowerCamelCase )

       UpperCAmelCase__								=  torch.tensor(
           [[[-2.39_14, -4.37_42, -5.09_56], [-4.09_88, -4.23_84, -7.04_06], [-3.14_27, -3.71_92, -6.68_00]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]	, lowerCamelCase	, atol=1e-4 ) )





