

'''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 			A_					(							unittest.TestCase				):






     '''simple docstring'''



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


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      Dict:
      UpperCAmelCase			:      Optional[Any]		   =					ids_tensor([self.batch_size, self.seq_length]	,     self.vocab_size			)

      UpperCAmelCase			:      Dict		   =					None
      if self.use_attention_mask:
       UpperCAmelCase			:      Optional[int]		   =					random_attention_mask([self.batch_size, self.seq_length]			)

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

      UpperCAmelCase			:      str		   =					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   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[Any]:
      UpperCAmelCase			:      Any		   =					self.prepare_config_and_inputs()
      UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					config_and_inputs
      UpperCAmelCase			:      Optional[int]		   =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict




@require_flax
class 			A_					(							lowercase__			,  unittest.TestCase				):






     '''simple docstring'''



     UpperCAmelCase_		:      str    						=    True

     UpperCAmelCase_		:      int    						=    (
         (
             FlaxElectraModel,
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,
             FlaxElectraForPreTraining,
             FlaxElectraForTokenClassification,
             FlaxElectraForQuestionAnswering,
             FlaxElectraForMultipleChoice,
             FlaxElectraForSequenceClassification,
         )
         if is_flax_available()
         else ()
     )


     def   UpperCAmelCase_			(						self					:	Any			)		->      int:
      UpperCAmelCase			:      int		   =					FlaxElectraModelTester(self			)




     @slow
     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[int]:
      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			:      Optional[int]		   =					model_class_name.from_pretrained('google/electra-small-discriminator'			)
       UpperCAmelCase			:      str		   =					model(np.ones((1, 1)			)			)
       self.assertIsNotNone(lowerCAmelCase__			)












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase      =				logging.get_logger(__name__)
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Union[str, Any]  					='''speech-encoder-decoder'''
     __snake_case		:				List[Any]  					=True





     def __init__(		self					:	Any  ,  **lowerCAmelCase__					:	List[Any])							->	Tuple:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      if "encoder" not in kwargs or "decoder" not in kwargs:
       raise ValueError(
           f'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
           f''' `decoder` sub-configurations are passed, but only {kwargs}''')

      SCREAMING_SNAKE_CASE				    =		kwargs.pop('encoder')
      SCREAMING_SNAKE_CASE				    =		encoder_config.pop('model_type')
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('decoder')
      SCREAMING_SNAKE_CASE				    =		decoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		True





     @classmethod
     def     __UpperCamelCase						(		cls					:	str  ,  lowerCAmelCase__					:	PretrainedConfig  ,  lowerCAmelCase__					:	PretrainedConfig  ,  **lowerCAmelCase__					:	List[str])							->	PretrainedConfig:

      """simple docstring"""
      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		True

      return cls(encoder=encoder_config.to_dict()  ,  decoder=decoder_config.to_dict()  ,  **lowerCAmelCase__)





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE				    =		self.encoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.decoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.__class__.model_type
      return output




