





"""simple docstring"""


import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.electra.modeling_flax_electra import (
      FlaxElectraForCausalLM,
      FlaxElectraForMaskedLM,
      FlaxElectraForMultipleChoice,
      FlaxElectraForPreTraining,
      FlaxElectraForQuestionAnswering,
      FlaxElectraForSequenceClassification,
      FlaxElectraForTokenClassification,
      FlaxElectraModel,
  )



class    __SCREAMING_SNAKE_CASE			(  unittest.TestCase      ):







  '''simple docstring'''


  def __init__(						self      :				Optional[int]    ,      __a      :				List[str]    ,      __a      :				List[Any]=13    ,      __a      :				Tuple=7    ,      __a      :				str=True    ,      __a      :				Dict=True    ,      __a      :				int=True    ,      __a      :				Union[str, Any]=True    ,      __a      :				Optional[Any]=99    ,      __a      :				Union[str, Any]=24    ,      __a      :				Optional[Any]=32    ,      __a      :				List[Any]=5    ,      __a      :				str=4    ,      __a      :				Union[str, Any]=37    ,      __a      :				Union[str, Any]="gelu"    ,      __a      :				Tuple=0.1    ,      __a      :				Tuple=0.1    ,      __a      :				str=512    ,      __a      :				Any=16    ,      __a      :				int=2    ,      __a      :				int=0.02    ,      __a      :				str=4    ,      )						-> Any:
        _UpperCamelCase					:		Optional[Any]  =				parent
        _UpperCamelCase					:		Dict  =				batch_size
        _UpperCamelCase					:		Optional[int]  =				seq_length
        _UpperCamelCase					:		Optional[int]  =				is_training
        _UpperCamelCase					:		str  =				use_attention_mask
        _UpperCamelCase					:		Optional[Any]  =				use_token_type_ids
        _UpperCamelCase					:		Dict  =				use_labels
        _UpperCamelCase					:		Union[str, Any]  =				vocab_size
        _UpperCamelCase					:		Tuple  =				hidden_size
        _UpperCamelCase					:		List[str]  =				embedding_size
        _UpperCamelCase					:		Union[str, Any]  =				num_hidden_layers
        _UpperCamelCase					:		int  =				num_attention_heads
        _UpperCamelCase					:		Optional[Any]  =				intermediate_size
        _UpperCamelCase					:		Optional[Any]  =				hidden_act
        _UpperCamelCase					:		Tuple  =				hidden_dropout_prob
        _UpperCamelCase					:		List[Any]  =				attention_probs_dropout_prob
        _UpperCamelCase					:		str  =				max_position_embeddings
        _UpperCamelCase					:		List[str]  =				type_vocab_size
        _UpperCamelCase					:		List[str]  =				type_sequence_label_size
        _UpperCamelCase					:		Dict  =				initializer_range
        _UpperCamelCase					:		Dict  =				num_choices

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Union[str, Any]:
        _UpperCamelCase					:		Any  =				ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size		)

        _UpperCamelCase					:		Optional[Any]  =				None
        if self.use_attention_mask:
              _UpperCamelCase					:		List[Any]  =				random_attention_mask([self.batch_size, self.seq_length]		)

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

        _UpperCamelCase					:		Optional[int]  =				ElectraConfig(
            vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      embedding_size=self.embedding_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    ,      initializer_range=self.initializer_range    ,      )

        return config, input_ids, token_type_ids, attention_mask


  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> List[Any]:
        _UpperCamelCase					:		Dict  =				self.prepare_config_and_inputs()
        _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		str  =				config_and_inputs
        _UpperCamelCase					:		Dict  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict





@require_flax
class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     unittest.TestCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:int		   =    True

  SCREAMING_SNAKE_CASE__							:Optional[Any]		   =    (
      (
          FlaxElectraModel,
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForPreTraining,
          FlaxElectraForTokenClassification,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForMultipleChoice,
          FlaxElectraForSequenceClassification,
      )
      if is_flax_available()
      else ()
  )

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> List[Any]:
        _UpperCamelCase					:		Union[str, Any]  =				FlaxElectraModelTester(self		)


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> str:
        for model_class_name in self.all_model_classes:
              if model_class_name == FlaxElectraForMaskedLM:
                    _UpperCamelCase					:		int  =				model_class_name.from_pretrained("google/electra-small-generator"		)
              else:
                    _UpperCamelCase					:		List[Any]  =				model_class_name.from_pretrained("google/electra-small-discriminator"		)
              _UpperCamelCase					:		List[str]  =				model(np.ones((1, 1)		)		)
              self.assertIsNotNone(__a		)







"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
