




import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 							__lowercase   (			unittest.TestCase    ):
     _lowerCAmelCase		       =						"hf-internal-testing/tiny-stable-diffusion-pipe"
     _lowerCAmelCase		       =						1
     _lowerCAmelCase		       =						77
     _lowerCAmelCase		       =						32
     _lowerCAmelCase		       =						4
     _lowerCAmelCase		       =						_lowerCAmelCase		       =						64
     _lowerCAmelCase		       =						torch.manual_seed(0    )



     def 				__magic_name__		(  self   :							List[Any]      ,		lowercase__   :							Dict=0.9999       ):
       a_		    =  UNetaDConditionModel.from_pretrained(self.model_id      ,		subfolder='''unet'''       )
       a_		    =  unet.to(lowercase__       )
       a_		    =  EMAModel(unet.parameters()      ,		decay=lowercase__      ,		model_cls=lowercase__      ,		model_config=unet.config       )
       return unet, ema_unet



     def 				__magic_name__		(  self   :							List[str]       ):
       a_		    =  torch.randn(
           self.batch_size      ,		self.num_in_channels      ,		self.latent_height      ,		self.latent_width      ,		generator=self.generator       ).to(lowercase__       )
       a_		    =  torch.randint(0      ,		1_0_0_0      ,		size=(self.batch_size,)      ,		generator=self.generator       ).to(lowercase__       )
       a_		    =  torch.randn(
           self.batch_size      ,		self.prompt_length      ,		self.text_encoder_hidden_dim      ,		generator=self.generator       ).to(lowercase__       )
       return noisy_latents, timesteps, encoder_hidden_states



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							List[Any]       ):
       a_		    =  {}
       for k, param in unet.state_dict().items():
         a_		    =  torch.randn_like(lowercase__       ) + (param * torch.randn_like(lowercase__       ))
         updated_state_dict.update({k: updated_param}       )
       unet.load_state_dict(lowercase__       )
       return unet



     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       a_    , a_		    =  self.get_models()
       # Take the first (hypothetical) EMA step.
       ema_unet.step(unet.parameters()       )
       assert ema_unet.optimization_step == 1

       # Take two more.
       for _ in range(2       ):
         ema_unet.step(unet.parameters()       )
       assert ema_unet.optimization_step == 3



     def 				__magic_name__		(  self   :							Optional[int]       ):
       a_    , a_		    =  self.get_models()
       # Since the `unet` is not being updated (i.e., backprop'd)
       # there won't be any difference between the `params` of `unet`
       # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
       ema_unet.step(unet.parameters()       )
       a_		    =  list(unet.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params      ,		lowercase__       ):
         assert torch.allclose(lowercase__      ,		lowercase__       )

       # The above holds true even if we call `ema.step()` multiple times since
       # `unet` params are still not being updated.
       for _ in range(4       ):
         ema_unet.step(unet.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params      ,		lowercase__       ):
         assert torch.allclose(lowercase__      ,		lowercase__       )



     def 				__magic_name__		(  self   :							List[Any]       ):
       a_    , a_		    =  self.get_models()
       # Here we simulate the parameter updates for `unet`. Since there might
       # be some parameters which are initialized to zero we take extra care to
       # initialize their values to something non-zero before the multiplication.
       a_		    =  self.simulate_backprop(lowercase__       )

       # Take the EMA step.
       ema_unet.step(unet_pseudo_updated_step_one.parameters()       )

       # Now the EMA'd parameters won't be equal to the original model parameters.
       a_		    =  list(unet_pseudo_updated_step_one.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params      ,		lowercase__       ):
         assert ~torch.allclose(lowercase__      ,		lowercase__       )

       # Ensure this is the case when we take multiple EMA steps.
       for _ in range(4       ):
         ema_unet.step(unet.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params      ,		lowercase__       ):
         assert ~torch.allclose(lowercase__      ,		lowercase__       )



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       # If we call EMA step after a backpropagation consecutively for two times,
       # the shadow params from those two steps should be different.
       a_    , a_		    =  self.get_models()

       # First backprop + EMA
       a_		    =  self.simulate_backprop(lowercase__       )
       ema_unet.step(unet_step_one.parameters()       )
       a_		    =  ema_unet.shadow_params

       # Second backprop + EMA
       a_		    =  self.simulate_backprop(lowercase__       )
       ema_unet.step(unet_step_two.parameters()       )
       a_		    =  ema_unet.shadow_params

       for step_one, step_two in zip(lowercase__      ,		lowercase__       ):
         assert ~torch.allclose(lowercase__      ,		lowercase__       )



     def 				__magic_name__		(  self   :							Tuple       ):
       # If there's no decay even if there are backprops, EMA steps
       # won't take any effect i.e., the shadow params would remain the
       # same.
       a_    , a_		    =  self.get_models(decay=0.0       )
       a_		    =  self.simulate_backprop(lowercase__       )
       ema_unet.step(unet_step_one.parameters()       )
       a_		    =  ema_unet.shadow_params

       a_		    =  self.simulate_backprop(lowercase__       )
       ema_unet.step(unet_step_two.parameters()       )
       a_		    =  ema_unet.shadow_params

       for step_one, step_two in zip(lowercase__      ,		lowercase__       ):
         assert torch.allclose(lowercase__      ,		lowercase__       )



     @skip_mps
     def 				__magic_name__		(  self   :							int       ):
       a_    , a_		    =  self.get_models()
       a_    , a_    , a_		    =  self.get_dummy_inputs()

       with tempfile.TemporaryDirectory() as tmpdir:
         ema_unet.save_pretrained(lowercase__       )
         a_		    =  UNetaDConditionModel.from_pretrained(lowercase__      ,		model_cls=lowercase__       )
         a_		    =  loaded_unet.to(unet.device       )

       # Since no EMA step has been performed the outputs should match.
       a_		    =  unet(lowercase__      ,		lowercase__      ,		lowercase__       ).sample
       a_		    =  loaded_unet(lowercase__      ,		lowercase__      ,		lowercase__       ).sample

       assert torch.allclose(lowercase__      ,		lowercase__      ,		atol=1e-4       )






import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
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 (
      MODEL_FOR_PRETRAINING_MAPPING,
      ElectraForCausalLM,
      ElectraForMaskedLM,
      ElectraForMultipleChoice,
      ElectraForPreTraining,
      ElectraForQuestionAnswering,
      ElectraForSequenceClassification,
      ElectraForTokenClassification,
      ElectraModel,
  )
  from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
class 							__lowercase   :



     def __init__(  self   :							Optional[Any]      ,		lowercase__   :							int      ,		lowercase__   :							Tuple=1_3      ,		lowercase__   :							Any=7      ,		lowercase__   :							Optional[int]=True      ,		lowercase__   :							str=True      ,		lowercase__   :							Optional[Any]=True      ,		lowercase__   :							int=True      ,		lowercase__   :							str=9_9      ,		lowercase__   :							Dict=3_2      ,		lowercase__   :							Union[str, Any]=5      ,		lowercase__   :							Any=4      ,		lowercase__   :							Tuple=3_7      ,		lowercase__   :							Tuple="gelu"      ,		lowercase__   :							Optional[int]=0.1      ,		lowercase__   :							str=0.1      ,		lowercase__   :							int=5_1_2      ,		lowercase__   :							Any=1_6      ,		lowercase__   :							Dict=2      ,		lowercase__   :							Tuple=0.02      ,		lowercase__   :							Union[str, Any]=3      ,		lowercase__   :							Tuple=4      ,		lowercase__   :							Dict=None      ,		):
       a_		    =  parent
       a_		    =  batch_size
       a_		    =  seq_length
       a_		    =  is_training
       a_		    =  use_input_mask
       a_		    =  use_token_type_ids
       a_		    =  use_labels
       a_		    =  vocab_size
       a_		    =  hidden_size
       a_		    =  num_hidden_layers
       a_		    =  num_attention_heads
       a_		    =  intermediate_size
       a_		    =  hidden_act
       a_		    =  hidden_dropout_prob
       a_		    =  attention_probs_dropout_prob
       a_		    =  max_position_embeddings
       a_		    =  type_vocab_size
       a_		    =  type_sequence_label_size
       a_		    =  initializer_range
       a_		    =  num_labels
       a_		    =  num_choices
       a_		    =  scope



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

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

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

       a_		    =  None
       a_		    =  None
       a_		    =  None
       if self.use_labels:
         a_		    =  ids_tensor([self.batch_size]      ,		self.type_sequence_label_size       )
         a_		    =  ids_tensor([self.batch_size, self.seq_length]      ,		self.num_labels       )
         a_		    =  ids_tensor([self.batch_size]      ,		self.num_choices       )
         a_		    =  ids_tensor([self.batch_size, self.seq_length]      ,		1       )

       a_		    =  self.get_config()

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           fake_token_labels,
       )



     def 				__magic_name__		(  self   :							Any       ):
       return ElectraConfig(
           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=lowercase__      ,		initializer_range=self.initializer_range      ,		)



     def 				__magic_name__		(  self   :							Dict       ):
       (
           (
           a_
       )    , (
           a_
       )    , (
           a_
       )    , (
           a_
       )    , (
           a_
       )    , (
           a_
       )    , (
           a_
       )    , (
           a_
       )    , 
       )		    =  self.prepare_config_and_inputs()

       a_		    =  True
       a_		    =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
       a_		    =  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 				__magic_name__		(  self   :							Union[str, Any]      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							int      ,		lowercase__   :							List[Any]      ,		lowercase__   :							List[Any]      ,		lowercase__   :							str      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							str      ,		lowercase__   :							str      ,		):
       a_		    =  ElectraModel(config=lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__       )
       a_		    =  model(lowercase__      ,		token_type_ids=lowercase__       )
       a_		    =  model(lowercase__       )
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)       )



     def 				__magic_name__		(  self   :							Optional[int]      ,		lowercase__   :							int      ,		lowercase__   :							Tuple      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							Any      ,		lowercase__   :							Dict      ,		lowercase__   :							Optional[Any]      ,		):
       a_		    =  True
       a_		    =  ElectraModel(lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(
           lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		encoder_hidden_states=lowercase__      ,		encoder_attention_mask=lowercase__      ,		)
       a_		    =  model(
           lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		encoder_hidden_states=lowercase__      ,		)
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__       )
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)       )



     def 				__magic_name__		(  self   :							str      ,		lowercase__   :							List[Any]      ,		lowercase__   :							Any      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							Tuple      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							Any      ,		lowercase__   :							Dict      ,		lowercase__   :							Any      ,		):
       a_		    =  ElectraForMaskedLM(config=lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		labels=lowercase__       )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)       )



     def 				__magic_name__		(  self   :							Optional[int]      ,		lowercase__   :							Dict      ,		lowercase__   :							Dict      ,		lowercase__   :							Dict      ,		lowercase__   :							str      ,		lowercase__   :							List[Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							int      ,		lowercase__   :							str      ,		):
       a_		    =  ElectraForCausalLM(config=lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		labels=lowercase__       )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)       )



     def 				__magic_name__		(  self   :							str      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							List[Any]      ,		lowercase__   :							str      ,		lowercase__   :							List[str]      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							Dict      ,		lowercase__   :							Dict      ,		lowercase__   :							List[Any]      ,		):
       a_		    =  self.num_labels
       a_		    =  ElectraForTokenClassification(config=lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		labels=lowercase__       )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.num_labels)       )



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							Any      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Any      ,		lowercase__   :							Dict      ,		lowercase__   :							List[str]      ,		lowercase__   :							List[str]      ,		lowercase__   :							List[str]      ,		):
       a_		    =  self.num_labels
       a_		    =  ElectraForPreTraining(config=lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		labels=lowercase__       )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length)       )



     def 				__magic_name__		(  self   :							Optional[int]      ,		lowercase__   :							Dict      ,		lowercase__   :							Tuple      ,		lowercase__   :							Any      ,		lowercase__   :							Any      ,		lowercase__   :							int      ,		lowercase__   :							List[str]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							int      ,		):
       a_		    =  self.num_labels
       a_		    =  ElectraForSequenceClassification(lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		labels=lowercase__       )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_labels)       )



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							int      ,		lowercase__   :							Any      ,		lowercase__   :							List[Any]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Optional[Any]      ,		):
       a_		    =  ElectraForQuestionAnswering(config=lowercase__       )
       model.to(lowercase__       )
       model.eval()
       a_		    =  model(
           lowercase__      ,		attention_mask=lowercase__      ,		token_type_ids=lowercase__      ,		start_positions=lowercase__      ,		end_positions=lowercase__      ,		)
       self.parent.assertEqual(result.start_logits.shape      ,		(self.batch_size, self.seq_length)       )
       self.parent.assertEqual(result.end_logits.shape      ,		(self.batch_size, self.seq_length)       )



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



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



@require_torch
class 							__lowercase   (			a__	,		a__	,		unittest.TestCase    ):
     _lowerCAmelCase		       =						(
         (
             ElectraModel,
             ElectraForPreTraining,
             ElectraForMaskedLM,
             ElectraForCausalLM,
             ElectraForMultipleChoice,
             ElectraForTokenClassification,
             ElectraForSequenceClassification,
             ElectraForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     _lowerCAmelCase		       =						(
         {
             "feature-extraction": ElectraModel,
             "fill-mask": ElectraForMaskedLM,
             "question-answering": ElectraForQuestionAnswering,
             "text-classification": ElectraForSequenceClassification,
             "text-generation": ElectraForCausalLM,
             "token-classification": ElectraForTokenClassification,
             "zero-shot": ElectraForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _lowerCAmelCase		       =						True



     def 				__magic_name__		(  self   :							Tuple      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							List[Any]      ,		lowercase__   :							Dict=False       ):
       a_		    =  super()._prepare_for_class(lowercase__      ,		lowercase__      ,		return_labels=lowercase__       )

       if return_labels:
         if model_class in get_values(lowercase__       ):
           a_		    =  torch.zeros(
               (self.model_tester.batch_size, self.model_tester.seq_length)      ,		dtype=torch.long      ,		device=lowercase__       )
       return inputs_dict



     def 				__magic_name__		(  self   :							List[str]       ):
       a_		    =  ElectraModelTester(self       )
       a_		    =  ConfigTester(self      ,		config_class=lowercase__      ,		hidden_size=3_7       )



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



     def 				__magic_name__		(  self   :							Any       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_model(*lowercase__       )



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       a_		    =  self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_electra_model_as_decoder(*lowercase__       )



     def 				__magic_name__		(  self   :							int       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         a_		    =  type
         self.model_tester.create_and_check_electra_model(*lowercase__       )



     def 				__magic_name__		(  self   :							List[str]       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_masked_lm(*lowercase__       )



     def 				__magic_name__		(  self   :							Optional[int]       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_token_classification(*lowercase__       )



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_pretraining(*lowercase__       )



     def 				__magic_name__		(  self   :							List[Any]       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_sequence_classification(*lowercase__       )



     def 				__magic_name__		(  self   :							List[str]       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_question_answering(*lowercase__       )



     def 				__magic_name__		(  self   :							str       ):
       a_		    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_multiple_choice(*lowercase__       )



     @slow
     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_		    =  ElectraModel.from_pretrained(lowercase__       )
         self.assertIsNotNone(lowercase__       )



     def 				__magic_name__		(  self   :							Dict       ):
       a_		    =  self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_electra_for_causal_lm(*lowercase__       )



@require_torch
class 							__lowercase   (			unittest.TestCase    ):



     @slow
     def 				__magic_name__		(  self   :							Tuple       ):
       a_		    =  ElectraModel.from_pretrained('''google/electra-small-discriminator'''       )
       a_		    =  torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]       )
       a_		    =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
       a_		    =  model(lowercase__      ,		attention_mask=lowercase__       )[0]
       a_		    =  torch.Size((1, 1_1, 2_5_6)       )
       self.assertEqual(output.shape      ,		lowercase__       )
       a_		    =  torch.tensor(
           [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]       )

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