

import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import UperNetForSemanticSegmentation
   from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[int]=13	,							lowerCAmelCase			:   Union[str, Any]=32	,							lowerCAmelCase			:   List[Any]=3	,							lowerCAmelCase			:   int=4	,							lowerCAmelCase			:   int=[10, 20, 30, 40]	,							lowerCAmelCase			:   List[Any]=[2, 2, 3, 2]	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   List[Any]=True	,							lowerCAmelCase			:   Dict=37	,							lowerCAmelCase			:   List[Any]="gelu"	,							lowerCAmelCase			:   str=10	,							lowerCAmelCase			:   List[Any]=0.02	,							lowerCAmelCase			:   str=["stage2", "stage3", "stage4"]	,							lowerCAmelCase			:   List[Any]=3	,							lowerCAmelCase			:   Tuple=None	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						parent
     __lowerCAmelCase  :      List[Any]    =						batch_size
     __lowerCAmelCase  :      Optional[int]    =						image_size
     __lowerCAmelCase  :      Tuple    =						num_channels
     __lowerCAmelCase  :      str    =						num_stages
     __lowerCAmelCase  :      str    =						hidden_sizes
     __lowerCAmelCase  :      Optional[Any]    =						depths
     __lowerCAmelCase  :      Optional[Any]    =						is_training
     __lowerCAmelCase  :      int    =						use_labels
     __lowerCAmelCase  :      int    =						intermediate_size
     __lowerCAmelCase  :      Optional[int]    =						hidden_act
     __lowerCAmelCase  :      List[Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      Optional[int]    =						initializer_range
     __lowerCAmelCase  :      Dict    =						out_features
     __lowerCAmelCase  :      List[Any]    =						num_labels
     __lowerCAmelCase  :      Union[str, Any]    =						scope
     __lowerCAmelCase  :      Union[str, Any]    =						num_stages







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

     __lowerCAmelCase  :      Tuple    =						None
     if self.use_labels:
        __lowerCAmelCase  :      Tuple    =						ids_tensor([self.batch_size]	,							self.type_sequence_label_size   )

     __lowerCAmelCase  :      List[Any]    =						self.get_config()

     return config, pixel_values, labels







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[Any]:






     """simple docstring"""







     return ConvNextConfig(
         num_channels=self.num_channels	,							num_stages=self.num_stages	,							hidden_sizes=self.hidden_sizes	,							depths=self.depths	,							is_training=self.is_training	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							out_features=self.out_features	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






     """simple docstring"""







     return UperNetConfig(
         backbone_config=self.get_backbone_config()	,							hidden_size=5_12	,							pool_scales=[1, 2, 3, 6]	,							use_auxiliary_head=lowerCAmelCase	,							auxiliary_loss_weight=0.4	,							auxiliary_in_channels=40	,							auxiliary_channels=2_56	,							auxiliary_num_convs=1	,							auxiliary_concat_input=lowerCAmelCase	,							loss_ignore_index=2_55	,							num_labels=self.num_labels	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						UperNetForSemanticSegmentation(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase   )
     self.parent.assertEqual(
         result.logits.shape	,							(self.batch_size, self.num_labels, self.image_size, self.image_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.prepare_config_and_inputs()
     (
         __lowerCAmelCase
     )  :      Dict    =						config_and_inputs
     __lowerCAmelCase  :      Optional[Any]    =						{"""pixel_values""": pixel_values}
     return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Any										=(UperNetForSemanticSegmentation,) if is_torch_available() else ()
  lowerCamelCase   :      List[str]										={"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
  lowerCamelCase   :      Dict										=False
  lowerCamelCase   :      Union[str, Any]										=False
  lowerCamelCase   :      Union[str, Any]										=False
  lowerCamelCase   :      str										=False
  lowerCamelCase   :      List[Any]										=False
  lowerCamelCase   :      Optional[Any]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						UperNetModelTester(self   )
     __lowerCAmelCase  :      Dict    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							has_text_modality=lowerCAmelCase	,							hidden_size=37   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     return







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Optional[int]    =						model_class(lowerCAmelCase   )
        __lowerCAmelCase  :      int    =						inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCAmelCase  :      str    =						[*signature.parameters.keys()]

        __lowerCAmelCase  :      Optional[int]    =						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase   )







  @unittest.skip(reason="""UperNet does not use inputs_embeds"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip(reason="""UperNet does not support input and output embeddings"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip(reason="""UperNet does not have a base model"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     List[str]:






     """simple docstring"""







     pass







  @unittest.skip(reason="""UperNet does not have a base model"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     pass







  @require_torch_multi_gpu
  @unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     pass







  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[str]:






     """simple docstring"""







     def check_hidden_states_output(lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   List[Any]   ):
        __lowerCAmelCase  :      int    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()

        with torch.no_grad():
           __lowerCAmelCase  :      Optional[int]    =						model(**self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )   )

        __lowerCAmelCase  :      str    =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        __lowerCAmelCase  :      Optional[Any]    =						self.model_tester.num_stages
        self.assertEqual(len(lowerCAmelCase   )	,							expected_num_stages + 1   )

        # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )	,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,							)

     __lowerCAmelCase  :      Optional[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Dict    =						True
        check_hidden_states_output(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowerCAmelCase  :      str    =						True

        check_hidden_states_output(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs_for_common()

     __lowerCAmelCase  :      Any    =						_config_zero_init(lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						_config_zero_init(configs_no_init.backbone_config   )
     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      str    =						model_class(config=lowerCAmelCase   )
        for name, param in model.named_parameters():
           if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()	,							[0.0, 1.0]	,							msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,							)







  @unittest.skip(reason="""UperNet does not have tied weights"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







     pass







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase  :      int    =						UperNetForSemanticSegmentation.from_pretrained(lowerCAmelCase   )
        self.assertIsNotNone(lowerCAmelCase   )






def      snake_case_				()      ->     int:
   __lowerCAmelCase  :      int    =						hf_hub_download(
       repo_id="""hf-internal-testing/fixtures_ade20k""" ,       repo_type="""dataset""" ,       filename="""ADE_val_00000001.jpg"""					)
   __lowerCAmelCase  :      str    =						Image.open(__A					).convert("""RGB"""					)
   return image



@require_torch
@require_vision
@slow
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""   )
     __lowerCAmelCase  :      Any    =						UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""   ).to(lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						prepare_img()
     __lowerCAmelCase  :      int    =						processor(images=lowerCAmelCase	,							return_tensors="""pt"""   ).to(lowerCAmelCase   )

     with torch.no_grad():
        __lowerCAmelCase  :      Any    =						model(**lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
     self.assertEqual(outputs.logits.shape	,							lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						torch.tensor(
         [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]   ).to(lowerCAmelCase   )
     self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""   )
     __lowerCAmelCase  :      Optional[int]    =						UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""   ).to(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						prepare_img()
     __lowerCAmelCase  :      List[Any]    =						processor(images=lowerCAmelCase	,							return_tensors="""pt"""   ).to(lowerCAmelCase   )

     with torch.no_grad():
        __lowerCAmelCase  :      Union[str, Any]    =						model(**lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
     self.assertEqual(outputs.logits.shape	,							lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						torch.tensor(
         [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]   ).to(lowerCAmelCase   )
     self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )





import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
       MODEL_FOR_PRETRAINING_MAPPING,
       MobileBertForMaskedLM,
       MobileBertForMultipleChoice,
       MobileBertForNextSentencePrediction,
       MobileBertForPreTraining,
       MobileBertForQuestionAnswering,
       MobileBertForSequenceClassification,
       MobileBertForTokenClassification,
       MobileBertModel,
   )



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Dict=13	,							lowerCAmelCase			:   int=7	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Tuple=99	,							lowerCAmelCase			:   int=64	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   str=5	,							lowerCAmelCase			:   List[str]=4	,							lowerCAmelCase			:   str=37	,							lowerCAmelCase			:   Union[str, Any]="gelu"	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Optional[int]=5_12	,							lowerCAmelCase			:   List[str]=16	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   Union[str, Any]=0.02	,							lowerCAmelCase			:   Dict=3	,							lowerCAmelCase			:   int=4	,							lowerCAmelCase			:   Union[str, Any]=None	,							)							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						parent
     __lowerCAmelCase  :      Tuple    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      List[str]    =						is_training
     __lowerCAmelCase  :      Dict    =						use_input_mask
     __lowerCAmelCase  :      Optional[int]    =						use_token_type_ids
     __lowerCAmelCase  :      List[str]    =						use_labels
     __lowerCAmelCase  :      Dict    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						embedding_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      Optional[Any]    =						num_attention_heads
     __lowerCAmelCase  :      Optional[Any]    =						intermediate_size
     __lowerCAmelCase  :      Optional[int]    =						hidden_act
     __lowerCAmelCase  :      Any    =						hidden_dropout_prob
     __lowerCAmelCase  :      Optional[int]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      List[str]    =						max_position_embeddings
     __lowerCAmelCase  :      Optional[Any]    =						type_vocab_size
     __lowerCAmelCase  :      Optional[Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      Optional[Any]    =						initializer_range
     __lowerCAmelCase  :      Optional[Any]    =						num_labels
     __lowerCAmelCase  :      Union[str, Any]    =						num_choices
     __lowerCAmelCase  :      Union[str, Any]    =						scope







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

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

     __lowerCAmelCase  :      List[str]    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      Tuple    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

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

     __lowerCAmelCase  :      Optional[int]    =						self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     return MobileBertConfig(
         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	,							embedding_size=self.embedding_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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertModel(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Union[str, Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							token_type_ids=lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase   )

     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			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertForMaskedLM(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      List[Any]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						MobileBertForNextSentencePrediction(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      List[Any]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							)
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, 2)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertForPreTraining(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Optional[int]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							next_sentence_label=lowerCAmelCase	,							)
     self.parent.assertEqual(result.prediction_logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)   )
     self.parent.assertEqual(result.seq_relationship_logits.shape	,							(self.batch_size, 2)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						MobileBertForQuestionAnswering(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      int    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.num_labels
     __lowerCAmelCase  :      int    =						MobileBertForSequenceClassification(lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Optional[int]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[int]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.num_labels
     __lowerCAmelCase  :      Dict    =						MobileBertForTokenClassification(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Tuple    =						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 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.num_choices
     __lowerCAmelCase  :      List[Any]    =						MobileBertForMultipleChoice(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Dict    =						input_ids.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Union[str, Any]    =						token_type_ids.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Optional[int]    =						input_mask.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Optional[int]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							)
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_choices)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,
     )  :      List[Any]    =						config_and_inputs
     __lowerCAmelCase  :      List[str]    =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
     return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      str										=(
      (
          MobileBertModel,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase   :      Optional[int]										=(
      {
          "feature-extraction": MobileBertModel,
          "fill-mask": MobileBertForMaskedLM,
          "question-answering": MobileBertForQuestionAnswering,
          "text-classification": MobileBertForSequenceClassification,
          "token-classification": MobileBertForTokenClassification,
          "zero-shot": MobileBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase   :      Union[str, Any]										=True







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   List[Any]=False   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						super()._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase	,							return_labels=lowerCAmelCase   )

     if return_labels:
        if model_class in get_values(lowerCAmelCase   ):
           __lowerCAmelCase  :      Tuple    =						torch.zeros(
               (self.model_tester.batch_size, self.model_tester.seq_length)	,							dtype=torch.long	,							device=lowerCAmelCase   )
           __lowerCAmelCase  :      Union[str, Any]    =						torch.zeros(
               self.model_tester.batch_size	,							dtype=torch.long	,							device=lowerCAmelCase   )
     return inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						MobileBertModelTester(self   )
     __lowerCAmelCase  :      Optional[Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							hidden_size=37   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Optional[Any]:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase   )






def      snake_case_				(__A					:						Any					)      ->     Optional[Any]:
   return torch.tensor(
       __A ,       dtype=torch.long ,       device=__A ,       )


__UpperCAmelCase								=			1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						MobileBertModel.from_pretrained("""google/mobilebert-uncased"""   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]   )
     with torch.no_grad():
        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase   )[0]
     __lowerCAmelCase  :      List[Any]    =						torch.Size((1, 9, 5_12)   )
     self.assertEqual(output.shape	,							lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						torch.tensor(
         [
             [
                 [-2.473_6526e07, 8.269_1656e04, 1.652_1838e05],
                 [-5.754_1704e-01, 3.905_6022e00, 4.401_1507e00],
                 [2.604_7359e00, 1.567_7652e00, -1.732_4188e-01],
             ]
         ]	,							device=lowerCAmelCase	,							)

     # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
     # ~1 difference, it's therefore not a good idea to measure using addition.
     # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
     # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
     __lowerCAmelCase  :      Union[str, Any]    =						torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
     __lowerCAmelCase  :      Union[str, Any]    =						torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

     self.assertTrue(lower_bound and upper_bound   )


