code: 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 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=13					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=24					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=16					,	lowerCamelCase__=2					,	lowerCamelCase__=0.0_2					,	lowerCamelCase__=4					,	):
         lowerCAmelCase_:       List[str]         =  parent
         lowerCAmelCase_:       Union[str, Any]         =  batch_size
         lowerCAmelCase_:       str         =  seq_length
         lowerCAmelCase_:       Any         =  is_training
         lowerCAmelCase_:       str         =  use_attention_mask
         lowerCAmelCase_:       List[Any]         =  use_token_type_ids
         lowerCAmelCase_:       List[Any]         =  use_labels
         lowerCAmelCase_:       Any         =  vocab_size
         lowerCAmelCase_:       Any         =  hidden_size
         lowerCAmelCase_:       Union[str, Any]         =  embedding_size
         lowerCAmelCase_:       Dict         =  num_hidden_layers
         lowerCAmelCase_:       List[Any]         =  num_attention_heads
         lowerCAmelCase_:       Optional[int]         =  intermediate_size
         lowerCAmelCase_:       Union[str, Any]         =  hidden_act
         lowerCAmelCase_:       Dict         =  hidden_dropout_prob
         lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
         lowerCAmelCase_:       Dict         =  max_position_embeddings
         lowerCAmelCase_:       Optional[Any]         =  type_vocab_size
         lowerCAmelCase_:       Optional[int]         =  type_sequence_label_size
         lowerCAmelCase_:       str         =  initializer_range
         lowerCAmelCase_:       Dict         =  num_choices
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       List[str]         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )
  
         lowerCAmelCase_:       List[Any]         =  None
         if self.use_attention_mask:
            lowerCAmelCase_:       Dict         =  random_attention_mask([self.batch_size, self.seq_length]   )
  
         lowerCAmelCase_:       Tuple         =  None
         if self.use_token_type_ids:
            lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.type_vocab_size   )
  
         lowerCAmelCase_:       Any         =  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     _a						(						self   ):
         lowerCAmelCase_:       List[Any]         =  self.prepare_config_and_inputs()
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Any         =  config_and_inputs
         lowerCAmelCase_:       Optional[int]         =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict
  
  
  
  
  
  @require_flax
  class 				_lowercase						(    UpperCAmelCase__   ,     unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Optional[int]        =						True
  
      SCREAMING_SNAKE_CASE:							Any        =						(
          (
              FlaxElectraModel,
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForPreTraining,
              FlaxElectraForTokenClassification,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForMultipleChoice,
              FlaxElectraForSequenceClassification,
          )
          if is_flax_available()
          else ()
      )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       List[Any]         =  FlaxElectraModelTester(self   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            if model_class_name == FlaxElectraForMaskedLM:
               lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("google/electra-small-generator"   )
            else:
               lowerCAmelCase_:       Optional[int]         =  model_class_name.from_pretrained("google/electra-small-discriminator"   )
            lowerCAmelCase_:       List[Any]         =  model(np.ones((1, 1)   )   )
            self.assertIsNotNone(lowerCamelCase__   )
style_context: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)