




import logging

from transformers.configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__       :      List[str]  =   logging.getLogger(__name__)




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[Any]	     =      """masked_bert"""







    def __init__(    self     ,  UpperCamelCase__=3_0522     ,  UpperCamelCase__=768     ,  UpperCamelCase__=12     ,  UpperCamelCase__=12     ,  UpperCamelCase__=3072     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=512     ,  UpperCamelCase__=2     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=1e-12     ,  UpperCamelCase__=0     ,  UpperCamelCase__="topK"     ,  UpperCamelCase__="constant"     ,  UpperCamelCase__=0.0     ,  **UpperCamelCase__     ,  )     ->      List[Any]:
           super().__init__(pad_token_id=UpperCamelCase__     ,  **UpperCamelCase__     )

           lowerCamelCase   :      int			 =					vocab_size
           lowerCamelCase   :      Tuple			 =					hidden_size
           lowerCamelCase   :      Dict			 =					num_hidden_layers
           lowerCamelCase   :      Union[str, Any]			 =					num_attention_heads
           lowerCamelCase   :      List[str]			 =					hidden_act
           lowerCamelCase   :      List[str]			 =					intermediate_size
           lowerCamelCase   :      Dict			 =					hidden_dropout_prob
           lowerCamelCase   :      Tuple			 =					attention_probs_dropout_prob
           lowerCamelCase   :      Tuple			 =					max_position_embeddings
           lowerCamelCase   :      Any			 =					type_vocab_size
           lowerCamelCase   :      str			 =					initializer_range
           lowerCamelCase   :      Union[str, Any]			 =					layer_norm_eps
           lowerCamelCase   :      int			 =					pruning_method
           lowerCamelCase   :      Tuple			 =					mask_init
           lowerCamelCase   :      List[Any]			 =					mask_scale











'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__       :	Any    = {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__       :	Optional[Any]    = [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						UpperCamelCase__       :	str    = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)