


'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE__	):
    """simple docstring"""
    a													=   'bert-generation'


    def __init__(  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Any=50358				,							SCREAMING_SNAKE_CASE      :							Dict=1024				,							SCREAMING_SNAKE_CASE      :							Tuple=24				,							SCREAMING_SNAKE_CASE      :							Tuple=16				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=4096				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]="gelu"				,							SCREAMING_SNAKE_CASE      :							List[Any]=0.1				,							SCREAMING_SNAKE_CASE      :							int=0.1				,							SCREAMING_SNAKE_CASE      :							Tuple=512				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=0.02				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=1e-12				,							SCREAMING_SNAKE_CASE      :							Any=0				,							SCREAMING_SNAKE_CASE      :							List[Any]=2				,							SCREAMING_SNAKE_CASE      :							Optional[int]=1				,							SCREAMING_SNAKE_CASE      :							Tuple="absolute"				,							SCREAMING_SNAKE_CASE      :							List[Any]=True				,							**SCREAMING_SNAKE_CASE      :							List[str]				,							):
           super().__init__(pad_token_id=a_				,							bos_token_id=a_				,							eos_token_id=a_				,							**a_)

           _A					:     Optional[Any]     = vocab_size
           _A					:     Optional[Any]     = hidden_size
           _A					:     List[str]     = num_hidden_layers
           _A					:     List[Any]     = num_attention_heads
           _A					:     List[str]     = hidden_act
           _A					:     Optional[Any]     = intermediate_size
           _A					:     str     = hidden_dropout_prob
           _A					:     List[Any]     = attention_probs_dropout_prob
           _A					:     int     = max_position_embeddings
           _A					:     Union[str, Any]     = initializer_range
           _A					:     Optional[Any]     = layer_norm_eps
           _A					:     Union[str, Any]     = position_embedding_type
           _A					:     Tuple     = use_cache



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlipModel''',
       '''BlipPreTrainedModel''',
       '''BlipForConditionalGeneration''',
       '''BlipForQuestionAnswering''',
       '''BlipVisionModel''',
       '''BlipTextModel''',
       '''BlipForImageTextRetrieval''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Union[str, Any]    						=			[
       '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFBlipModel''',
       '''TFBlipPreTrainedModel''',
       '''TFBlipForConditionalGeneration''',
       '''TFBlipForQuestionAnswering''',
       '''TFBlipVisionModel''',
       '''TFBlipTextModel''',
       '''TFBlipForImageTextRetrieval''',
   ]

if TYPE_CHECKING:
   from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
   from .processing_blip import BlipProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_blip import BlipImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip import (
          BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipForConditionalGeneration,
          BlipForImageTextRetrieval,
          BlipForQuestionAnswering,
          BlipModel,
          BlipPreTrainedModel,
          BlipTextModel,
          BlipVisionModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blip import (
          TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBlipForConditionalGeneration,
          TFBlipForImageTextRetrieval,
          TFBlipForQuestionAnswering,
          TFBlipModel,
          TFBlipPreTrainedModel,
          TFBlipTextModel,
          TFBlipVisionModel,
      )

else:
   import sys

   A				:					Optional[int]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






