



"""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					:List[str]  ,    lowercase_					:Tuple  ,    lowercase_					:Union[str, Any]=13  ,    lowercase_					:int=7  ,    lowercase_					:Optional[int]=True  ,    lowercase_					:Any=True  ,    lowercase_					:Tuple=True  ,    lowercase_					:List[str]=True  ,    lowercase_					:Any=99  ,    lowercase_					:Optional[int]=24  ,    lowercase_					:Optional[int]=32  ,    lowercase_					:int=5  ,    lowercase_					:str=4  ,    lowercase_					:Tuple=37  ,    lowercase_					:Optional[int]="gelu"  ,    lowercase_					:Union[str, Any]=0.1  ,    lowercase_					:Dict=0.1  ,    lowercase_					:Optional[int]=5_12  ,    lowercase_					:Optional[int]=16  ,    lowercase_					:List[Any]=2  ,    lowercase_					:str=0.02  ,    lowercase_					:Optional[Any]=4  ,    )							->     Optional[int]:
												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[int]   )							->     List[str]:
												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					:Tuple   )							->     Union[str, Any]:
												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  A_     (	SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				True

						__UpperCamelCase													=				(
						    (
						        FlaxElectraModel,
						        FlaxElectraForCausalLM,
						        FlaxElectraForMaskedLM,
						        FlaxElectraForPreTraining,
						        FlaxElectraForTokenClassification,
						        FlaxElectraForQuestionAnswering,
						        FlaxElectraForMultipleChoice,
						        FlaxElectraForSequenceClassification,
						    )
						    if is_flax_available()
						    else ()
						)






						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												UpperCAmelCase			 =					FlaxElectraModelTester(self   )






						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[Any]:
												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(lowercase_   )






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				Optional[int]     			={
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'vit-hybrid'



      def __init__(       self  :					str     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Union[str, Any]=768     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					Optional[int]=3072     ,	_UpperCamelCase  :					Optional[int]="gelu"     ,	_UpperCamelCase  :					int=0.0     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					Dict=0.0_2     ,	_UpperCamelCase  :					Dict=1E-1_2     ,	_UpperCamelCase  :					Union[str, Any]=224     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Dict=[1, 1024, 24, 24]     ,	_UpperCamelCase  :					Optional[Any]=True     ,	**_UpperCamelCase  :					List[Any]     ,	)					->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone.""")
              _lowerCamelCase  :		Tuple			    =    {
                  """global_padding""": """same""",
                  """layer_type""": """bottleneck""",
                  """depths""": [3, 4, 9],
                  """out_features""": ["""stage3"""],
                  """embedding_dynamic_padding""": True,
              }

          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              if "model_type" in backbone_config:
                  _lowerCamelCase  :		int			    =    CONFIG_MAPPING[backbone_config["""model_type"""]]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""")
                  _lowerCamelCase  :		Optional[int]			    =    BitConfig
              _lowerCamelCase  :		List[Any]			    =    backbone_config_class(**_UpperCamelCase)

          _lowerCamelCase  :		Any			    =    backbone_featmap_shape
          _lowerCamelCase  :		Dict			    =    backbone_config
          _lowerCamelCase  :		Tuple			    =    hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_hidden_layers
          _lowerCamelCase  :		Optional[Any]			    =    num_attention_heads
          _lowerCamelCase  :		Optional[Any]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Dict			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Union[str, Any]			    =    initializer_range
          _lowerCamelCase  :		Any			    =    layer_norm_eps
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Any			    =    patch_size
          _lowerCamelCase  :		Optional[Any]			    =    num_channels
          _lowerCamelCase  :		List[str]			    =    qkv_bias




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict[str, any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.backbone_config.to_dict()
          _lowerCamelCase  :		List[str]			    =    self.__class__.model_type
          return output




