






import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       ElectraForCausalLM,
       ElectraForMaskedLM,
       ElectraForMultipleChoice,
       ElectraForPreTraining,
       ElectraForQuestionAnswering,
       ElectraForSequenceClassification,
       ElectraForTokenClassification,
       ElectraModel,
   )
   from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST

class        snake_case_				:







   """simple docstring"""



   def __init__(     self:						Any ,							snake_case__:						Optional[Any] ,							snake_case__:						str=13 ,							snake_case__:						Tuple=7 ,							snake_case__:						Optional[int]=True ,							snake_case__:						Dict=True ,							snake_case__:						List[Any]=True ,							snake_case__:						Union[str, Any]=True ,							snake_case__:						Any=99 ,							snake_case__:						Tuple=32 ,							snake_case__:						Tuple=5 ,							snake_case__:						List[Any]=4 ,							snake_case__:						Dict=37 ,							snake_case__:						Union[str, Any]="gelu" ,							snake_case__:						Optional[int]=0.1 ,							snake_case__:						int=0.1 ,							snake_case__:						Optional[int]=512 ,							snake_case__:						List[str]=16 ,							snake_case__:						int=2 ,							snake_case__:						Dict=0.02 ,							snake_case__:						Optional[int]=3 ,							snake_case__:						Optional[int]=4 ,							snake_case__:						Optional[int]=None ,							)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						parent
       _lowercase     			=						batch_size
       _lowercase     			=						seq_length
       _lowercase     			=						is_training
       _lowercase     			=						use_input_mask
       _lowercase     			=						use_token_type_ids
       _lowercase     			=						use_labels
       _lowercase     			=						vocab_size
       _lowercase     			=						hidden_size
       _lowercase     			=						num_hidden_layers
       _lowercase     			=						num_attention_heads
       _lowercase     			=						intermediate_size
       _lowercase     			=						hidden_act
       _lowercase     			=						hidden_dropout_prob
       _lowercase     			=						attention_probs_dropout_prob
       _lowercase     			=						max_position_embeddings
       _lowercase     			=						type_vocab_size
       _lowercase     			=						type_sequence_label_size
       _lowercase     			=						initializer_range
       _lowercase     			=						num_labels
       _lowercase     			=						num_choices
       _lowercase     			=						scope

   def       SCREAMING_SNAKE_CASE__					(     self:						List[Any]						)	->		List[str]:


       """simple docstring"""



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

       _lowercase     			=						None
       if self.use_input_mask:
           _lowercase     			=						random_attention_mask([self.batch_size, self.seq_length]						)

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

       _lowercase     			=						None
       _lowercase     			=						None
       _lowercase     			=						None
       if self.use_labels:
           _lowercase     			=						ids_tensor([self.batch_size] ,							self.type_sequence_label_size						)
           _lowercase     			=						ids_tensor([self.batch_size, self.seq_length] ,							self.num_labels						)
           _lowercase     			=						ids_tensor([self.batch_size] ,							self.num_choices						)
           _lowercase     			=						ids_tensor([self.batch_size, self.seq_length] ,							1						)

       _lowercase     			=						self.get_config()

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           fake_token_labels,
       )

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Optional[int]:


       """simple docstring"""



       return ElectraConfig(
           vocab_size=self.vocab_size ,							hidden_size=self.hidden_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 ,							is_decoder=SCREAMING_SNAKE_CASE_ ,							initializer_range=self.initializer_range ,							)

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


       """simple docstring"""



       (
           _lowercase       
       )     			=						self.prepare_config_and_inputs()

       _lowercase     			=						True
       _lowercase     			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       _lowercase     			=						ids_tensor([self.batch_size, self.seq_length] ,							vocab_size=2						)

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							snake_case__:						int ,							snake_case__:						Any ,							snake_case__:						Any ,							snake_case__:						Optional[Any] ,							snake_case__:						str ,							snake_case__:						Tuple ,							snake_case__:						List[str] ,							snake_case__:						Optional[int] ,							)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						ElectraModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_						)
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_						)
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						str ,							snake_case__:						Dict ,							snake_case__:						Any ,							snake_case__:						Any ,							snake_case__:						List[Any] ,							snake_case__:						str ,							snake_case__:						Union[str, Any] ,							snake_case__:						str ,							snake_case__:						Optional[Any] ,							)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						True
       _lowercase     			=						ElectraModel(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(
           SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							)
       _lowercase     			=						model(
           SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							)
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						List[Any] ,							snake_case__:						Union[str, Any] ,							snake_case__:						Any ,							snake_case__:						Dict ,							snake_case__:						Union[str, Any] ,							snake_case__:						str ,							snake_case__:						List[Any] ,							snake_case__:						Tuple ,							)	->		Dict:


       """simple docstring"""



       _lowercase     			=						ElectraForMaskedLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						Optional[Any] ,							snake_case__:						Any ,							snake_case__:						str ,							snake_case__:						Optional[int] ,							snake_case__:						int ,							snake_case__:						Union[str, Any] ,							snake_case__:						Union[str, Any] ,							snake_case__:						Dict ,							snake_case__:						Optional[Any] ,							)	->		int:


       """simple docstring"""



       _lowercase     			=						ElectraForCausalLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						List[str] ,							snake_case__:						Optional[int] ,							snake_case__:						Optional[Any] ,							snake_case__:						Any ,							snake_case__:						List[Any] ,							snake_case__:						Dict ,							snake_case__:						str ,							snake_case__:						Any ,							)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						self.num_labels
       _lowercase     			=						ElectraForTokenClassification(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						Union[str, Any] ,							snake_case__:						Tuple ,							snake_case__:						Any ,							snake_case__:						int ,							snake_case__:						Tuple ,							snake_case__:						Dict ,							snake_case__:						Tuple ,							snake_case__:						Optional[Any] ,							)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.num_labels
       _lowercase     			=						ElectraForPreTraining(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						List[str] ,							snake_case__:						Optional[Any] ,							snake_case__:						List[Any] ,							snake_case__:						Optional[int] ,							snake_case__:						str ,							snake_case__:						str ,							snake_case__:						int ,							snake_case__:						List[str] ,							)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						self.num_labels
       _lowercase     			=						ElectraForSequenceClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						Union[str, Any] ,							snake_case__:						int ,							snake_case__:						Optional[int] ,							snake_case__:						Any ,							snake_case__:						List[str] ,							snake_case__:						int ,							snake_case__:						List[str] ,							snake_case__:						int ,							)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						ElectraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						model(
           SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							start_positions=SCREAMING_SNAKE_CASE_ ,							end_positions=SCREAMING_SNAKE_CASE_ ,							)
       self.parent.assertEqual(result.start_logits.shape ,							(self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape ,							(self.batch_size, self.seq_length)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						int ,							snake_case__:						List[Any] ,							snake_case__:						Optional[Any] ,							snake_case__:						Tuple ,							snake_case__:						str ,							snake_case__:						int ,							snake_case__:						Tuple ,							snake_case__:						Optional[int] ,							)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.num_choices
       _lowercase     			=						ElectraForMultipleChoice(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       _lowercase     			=						input_ids.unsqueeze(1						).expand(-1 ,							self.num_choices ,							-1						).contiguous()
       _lowercase     			=						token_type_ids.unsqueeze(1						).expand(-1 ,							self.num_choices ,							-1						).contiguous()
       _lowercase     			=						input_mask.unsqueeze(1						).expand(-1 ,							self.num_choices ,							-1						).contiguous()
       _lowercase     			=						model(
           SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							token_type_ids=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_ ,							)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_choices)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		int:


       """simple docstring"""



       _lowercase     			=						self.prepare_config_and_inputs()
       (
           _lowercase       
       )     			=						config_and_inputs
       _lowercase     			=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class        snake_case_				(							lowercase_   ,      lowercase_   ,      unittest.TestCase   ):







   """simple docstring"""



   __lowerCAmelCase  =    (
       (
           ElectraModel,
           ElectraForPreTraining,
           ElectraForMaskedLM,
           ElectraForCausalLM,
           ElectraForMultipleChoice,
           ElectraForTokenClassification,
           ElectraForSequenceClassification,
           ElectraForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase  =    (
       {
           """feature-extraction""": ElectraModel,
           """fill-mask""": ElectraForMaskedLM,
           """question-answering""": ElectraForQuestionAnswering,
           """text-classification""": ElectraForSequenceClassification,
           """text-generation""": ElectraForCausalLM,
           """token-classification""": ElectraForTokenClassification,
           """zero-shot""": ElectraForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase  =    True

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						Union[str, Any] ,							snake_case__:						Tuple ,							snake_case__:						int=False						)	->		str:


       """simple docstring"""



       _lowercase     			=						super()._prepare_for_class(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							return_labels=SCREAMING_SNAKE_CASE_						)

       if return_labels:
           if model_class in get_values(SCREAMING_SNAKE_CASE_						):
               _lowercase     			=						torch.zeros(
                   (self.model_tester.batch_size, self.model_tester.seq_length) ,							dtype=torch.long ,							device=SCREAMING_SNAKE_CASE_						)
       return inputs_dict

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						ElectraModelTester(self						)
       _lowercase     			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_ ,							hidden_size=37						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		List[Any]:


       """simple docstring"""



       self.config_tester.run_common_tests()

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		Optional[Any]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_model(*SCREAMING_SNAKE_CASE_						)

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


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_electra_model_as_decoder(*SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Dict:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
           _lowercase     			=						type
           self.model_tester.create_and_check_electra_model(*SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		str:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_masked_lm(*SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		List[Any]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_token_classification(*SCREAMING_SNAKE_CASE_						)

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


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_pretraining(*SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_sequence_classification(*SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_question_answering(*SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Any:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_electra_for_multiple_choice(*SCREAMING_SNAKE_CASE_						)

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		Tuple:


       """simple docstring"""



       for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase     			=						ElectraModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_electra_for_causal_lm(*SCREAMING_SNAKE_CASE_						)






@require_torch
class        snake_case_				(							unittest.TestCase   ):







   """simple docstring"""



   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						ElectraModel.from_pretrained('google/electra-small-discriminator'						)
       _lowercase     			=						torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]						)
       _lowercase     			=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       _lowercase     			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_						)[0]
       _lowercase     			=						torch.Size((1, 11, 256)						)
       self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_						)
       _lowercase     			=						torch.tensor(
           [[[0.44_71, 0.68_21, -0.32_65], [0.46_27, 0.52_55, -0.36_68], [0.45_32, 0.33_13, -0.43_44]]]						)

       self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,							SCREAMING_SNAKE_CASE_ ,							atol=1E-4						)						)


'''simple docstring'''





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


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "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     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

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

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

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




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output

