






"""simple docstring"""







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase_		:			Union[str, Any]				   =					{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_		:			List[Any]				   =					[
     '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ConvNextV2ForImageClassification''',
     '''ConvNextV2Model''',
     '''ConvNextV2PreTrainedModel''',
     '''ConvNextV2Backbone''',
 ]


if TYPE_CHECKING:
 from .configuration_convnextva import (
     CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConvNextVaConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convnextva import (
      CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvNextVaBackbone,
      ConvNextVaForImageClassification,
      ConvNextVaModel,
      ConvNextVaPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_		:			int				   =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)








"""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      UpperCamelCase_       (				unittest.TestCase     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__=13       ,						snake_case__=7       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=99       ,						snake_case__=24       ,						snake_case__=32       ,						snake_case__=5       ,						snake_case__=4       ,						snake_case__=37       ,						snake_case__="gelu"       ,						snake_case__=0.1       ,						snake_case__=0.1       ,						snake_case__=5_12       ,						snake_case__=16       ,						snake_case__=2       ,						snake_case__=0.02       ,						snake_case__=4       ,						)	->	int:



         """simple docstring"""





         UpperCAmelCase					=	parent
         UpperCAmelCase					=	batch_size
         UpperCAmelCase					=	seq_length
         UpperCAmelCase					=	is_training
         UpperCAmelCase					=	use_attention_mask
         UpperCAmelCase					=	use_token_type_ids
         UpperCAmelCase					=	use_labels
         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	embedding_size
         UpperCAmelCase					=	num_hidden_layers
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	intermediate_size
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	type_vocab_size
         UpperCAmelCase					=	type_sequence_label_size
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	num_choices



  def 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size )

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

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

         UpperCAmelCase					=	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 )	->	List[str]:



         """simple docstring"""





         UpperCAmelCase					=	self.prepare_config_and_inputs()
         UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	config_and_inputs
         UpperCAmelCase					=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
         return config, inputs_dict





@require_flax
class      UpperCamelCase_       (				a_      ,    unittest.TestCase     ):
  _A				:    str =		True

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



  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	FlaxElectraModelTester(self )



  @slow
  def 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         for model_class_name in self.all_model_classes:
                if model_class_name == FlaxElectraForMaskedLM:
                       UpperCAmelCase					=	model_class_name.from_pretrained("""google/electra-small-generator""" )
                else:
                       UpperCAmelCase					=	model_class_name.from_pretrained("""google/electra-small-discriminator""" )
                UpperCAmelCase					=	model(np.ones((1, 1) ) )
                self.assertIsNotNone(snake_case__ )
