






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  snake_case__      :
	def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=13    ,     lowerCAmelCase__=7    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=99    ,     lowerCAmelCase__=32    ,     lowerCAmelCase__=5    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__=37    ,     lowerCAmelCase__="gelu"    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=5_12    ,     lowerCAmelCase__=16    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=0.0_2    ,     lowerCAmelCase__=3    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__=None    ,     )							->   str:
							__magic_name__    :     Optional[int]     		=      parent
							__magic_name__    :     Tuple     		=      batch_size
							__magic_name__    :     Union[str, Any]     		=      seq_length
							__magic_name__    :     List[str]     		=      is_training
							__magic_name__    :     Dict     		=      use_input_mask
							__magic_name__    :     str     		=      use_token_type_ids
							__magic_name__    :     Optional[int]     		=      use_labels
							__magic_name__    :     List[Any]     		=      vocab_size
							__magic_name__    :     Optional[Any]     		=      hidden_size
							__magic_name__    :     Dict     		=      num_hidden_layers
							__magic_name__    :     Union[str, Any]     		=      num_attention_heads
							__magic_name__    :     Union[str, Any]     		=      intermediate_size
							__magic_name__    :     Optional[Any]     		=      hidden_act
							__magic_name__    :     Optional[Any]     		=      hidden_dropout_prob
							__magic_name__    :     str     		=      attention_probs_dropout_prob
							__magic_name__    :     Any     		=      max_position_embeddings
							__magic_name__    :     Tuple     		=      type_vocab_size
							__magic_name__    :     Union[str, Any]     		=      type_sequence_label_size
							__magic_name__    :     List[str]     		=      initializer_range
							__magic_name__    :     Dict     		=      num_labels
							__magic_name__    :     Tuple     		=      num_choices
							__magic_name__    :     int     		=      scope
	def     __magic_name__       (       self				)							->   Any:
							__magic_name__    :     Optional[Any]     		=      ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size				)

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

							__magic_name__    :     int     		=      None
							if self.use_token_type_ids:
													__magic_name__    :     Union[str, Any]     		=      ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size				)

							__magic_name__    :     Union[str, Any]     		=      None
							__magic_name__    :     Union[str, Any]     		=      None
							__magic_name__    :     Union[str, Any]     		=      None
							if self.use_labels:
													__magic_name__    :     Optional[int]     		=      ids_tensor([self.batch_size]    ,     self.type_sequence_label_size				)
													__magic_name__    :     Tuple     		=      ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels				)
													__magic_name__    :     str     		=      ids_tensor([self.batch_size]    ,     self.num_choices				)
													__magic_name__    :     List[Any]     		=      ids_tensor([self.batch_size, self.seq_length]    ,     1				)

							__magic_name__    :     Optional[Any]     		=      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				)							->   Dict:
							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=lowerCAmelCase__    ,     initializer_range=self.initializer_range    ,     )
	def     __magic_name__       (       self				)							->   List[Any]:
							(
							    (
							    __magic_name__
							)		,(
							    __magic_name__
							)		,(
							    __magic_name__
							)		,(
							    __magic_name__
							)		,(
							    __magic_name__
							)		,(
							    __magic_name__
							)		,(
							    __magic_name__
							)		,(
							    __magic_name__
							)		,
							)    :     int     		=      self.prepare_config_and_inputs()

							__magic_name__    :     Union[str, Any]     		=      True
							__magic_name__    :     Optional[Any]     		=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
							__magic_name__    :     int     		=      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    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Optional[Any]:
							__magic_name__    :     List[str]     		=      ElectraModel(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     List[Any]     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__				)
							__magic_name__    :     List[str]     		=      model(lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__				)
							__magic_name__    :     Union[str, Any]     		=      model(lowerCAmelCase__				)
							self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   int:
							__magic_name__    :     Union[str, Any]     		=      True
							__magic_name__    :     str     		=      ElectraModel(lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Optional[Any]     		=      model(
							    lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     encoder_hidden_states=lowerCAmelCase__    ,     encoder_attention_mask=lowerCAmelCase__    ,     )
							__magic_name__    :     Optional[Any]     		=      model(
							    lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     encoder_hidden_states=lowerCAmelCase__    ,     )
							__magic_name__    :     str     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__				)
							self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Tuple:
							__magic_name__    :     str     		=      ElectraForMaskedLM(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     int     		=      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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   List[str]:
							__magic_name__    :     List[str]     		=      ElectraForCausalLM(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     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.vocab_size)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Tuple:
							__magic_name__    :     Dict     		=      self.num_labels
							__magic_name__    :     Tuple     		=      ElectraForTokenClassification(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Optional[int]     		=      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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Optional[int]:
							__magic_name__    :     Any     		=      self.num_labels
							__magic_name__    :     int     		=      ElectraForPreTraining(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Optional[int]     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__				)
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   str:
							__magic_name__    :     List[str]     		=      self.num_labels
							__magic_name__    :     int     		=      ElectraForSequenceClassification(lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Union[str, Any]:
							__magic_name__    :     Optional[Any]     		=      ElectraForQuestionAnswering(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Tuple     		=      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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Dict:
							__magic_name__    :     List[str]     		=      self.num_choices
							__magic_name__    :     int     		=      ElectraForMultipleChoice(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     str     		=      input_ids.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
							__magic_name__    :     Union[str, Any]     		=      token_type_ids.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
							__magic_name__    :     List[Any]     		=      input_mask.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
							__magic_name__    :     List[Any]     		=      model(
							    lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__    ,     )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_choices)				)







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





@require_torch
class  snake_case__      (							_lowerCAmelCase		,						_lowerCAmelCase		,						unittest.TestCase   ):
	lowercase__		:  Any							   =					(
	    (
	        ElectraModel,
	        ElectraForPreTraining,
	        ElectraForMaskedLM,
	        ElectraForCausalLM,
	        ElectraForMultipleChoice,
	        ElectraForTokenClassification,
	        ElectraForSequenceClassification,
	        ElectraForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase__		:  Optional[int]							   =					(
	    {
	        '''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 {}
	)
	lowercase__		:  int							   =					True
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__=False				)							->   Tuple:
							__magic_name__    :     int     		=      super()._prepare_for_class(lowerCAmelCase__    ,     lowerCAmelCase__    ,     return_labels=lowerCAmelCase__				)

							if return_labels:
													if model_class in get_values(lowerCAmelCase__				):
																			__magic_name__    :     Dict     		=      torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.seq_length)    ,     dtype=torch.long    ,     device=lowerCAmelCase__				)
							return inputs_dict
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     str     		=      ElectraModelTester(self				)
							__magic_name__    :     List[str]     		=      ConfigTester(self    ,     config_class=lowerCAmelCase__    ,     hidden_size=37				)
	def     __magic_name__       (       self				)							->   List[Any]:
							self.config_tester.run_common_tests()
	def     __magic_name__       (       self				)							->   Any:
							__magic_name__    :     Tuple     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_model(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__    :     List[str]     		=      self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_electra_model_as_decoder(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     Optional[Any]     		=      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
													__magic_name__    :     Dict     		=      type
													self.model_tester.create_and_check_electra_model(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     List[Any]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_masked_lm(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     List[str]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_token_classification(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_pretraining(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     Dict     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_sequence_classification(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_question_answering(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Optional[int]:
							__magic_name__    :     str     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_multiple_choice(*lowerCAmelCase__				)
	@slow
	def     __magic_name__       (       self				)							->   List[str]:
							for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__    :     List[str]     		=      ElectraModel.from_pretrained(lowerCAmelCase__				)
													self.assertIsNotNone(lowerCAmelCase__				)







	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     str     		=      self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_electra_for_causal_lm(*lowerCAmelCase__				)





@require_torch
class  snake_case__      (							unittest.TestCase   ):
	@slow
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     int     		=      ElectraModel.from_pretrained("""google/electra-small-discriminator"""				)
							__magic_name__    :     Tuple     		=      torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]				)
							__magic_name__    :     List[Any]     		=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
							__magic_name__    :     Any     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__				)[0]
							__magic_name__    :     Tuple     		=      torch.Size((1, 11, 2_56)				)
							self.assertEqual(output.shape    ,     lowerCAmelCase__				)
							__magic_name__    :     Optional[int]     		=      torch.tensor(
							    [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]				)

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








def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						return 1 if input_a == 0 else 0

def 					UpperCamelCase		( ):






						"""simple docstring"""







						assert not_gate(0				) == 1
						assert not_gate(1				) == 0


if __name__ == "__main__":
		print(not_gate(0))
		print(not_gate(1))
