

import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, 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.models.esm.modeling_esmfold import EsmForProteinFolding



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]=13	,							lowerCAmelCase			:   List[str]=7	,							lowerCAmelCase			:   int=False	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   List[Any]=False	,							lowerCAmelCase			:   List[str]=False	,							lowerCAmelCase			:   Optional[int]=19	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   Optional[Any]=4	,							lowerCAmelCase			:   Optional[Any]=37	,							lowerCAmelCase			:   Dict="gelu"	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   List[str]=5_12	,							lowerCAmelCase			:   Dict=16	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   Union[str, Any]=0.02	,							lowerCAmelCase			:   Dict=3	,							lowerCAmelCase			:   List[Any]=4	,							lowerCAmelCase			:   Union[str, Any]=None	,							)							->     List[str]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

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

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

     __lowerCAmelCase  :      Tuple    =						self.get_config()

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







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






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						EsmConfig(
         vocab_size=33	,							hidden_size=self.hidden_size	,							pad_token_id=1	,							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	,							initializer_range=self.initializer_range	,							is_folding_model=lowerCAmelCase	,							esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False}	,							)
     return config







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						EsmForProteinFolding(config=lowerCAmelCase   ).float()
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						model(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						model(lowerCAmelCase   )

     self.parent.assertEqual(result.positions.shape	,							(8, self.batch_size, self.seq_length, 14, 3)   )
     self.parent.assertEqual(result.angles.shape	,							(8, self.batch_size, self.seq_length, 7, 2)   )







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






     """simple docstring"""







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






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






  lowerCamelCase   :      Tuple										=False

  lowerCamelCase   :      int										=(EsmForProteinFolding,) if is_torch_available() else ()
  lowerCamelCase   :      int										=()
  lowerCamelCase   :      Dict										={} if is_torch_available() else {}
  lowerCamelCase   :      str										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						EsmFoldModelTester(self   )
     __lowerCAmelCase  :      Optional[Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							hidden_size=37   )







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






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase   )







  @unittest.skip("""Does not support attention outputs"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






     """simple docstring"""







     pass







  @unittest.skip
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     List[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""Esm does not support embedding resizing"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Tuple:






     """simple docstring"""







     pass







  @unittest.skip("""Esm does not support embedding resizing"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Union[str, Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support passing input embeds!"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[int]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     str:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not output hidden states in the normal way."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMfold does not output hidden states in the normal way."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold only has one output format."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     pass







  @unittest.skip("""This test doesn't work for ESMFold and doesn't test core functionality"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support input chunking."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Any:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold doesn't support torchscript compilation."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold doesn't support torchscript compilation."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold doesn't support torchscript compilation."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold doesn't support data parallel."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     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			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     pass






@require_torch
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						EsmForProteinFolding.from_pretrained("""facebook/esmfold_v1"""   ).float()
     model.eval()
     __lowerCAmelCase  :      Union[str, Any]    =						torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]   )
     __lowerCAmelCase  :      str    =						model(lowerCAmelCase   )["""positions"""]
     __lowerCAmelCase  :      str    =						torch.tensor([2.5828, 0.7993, -10.9334]	,							dtype=torch.floataa   )
     self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]	,							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   )


