
"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Optional[int]  						=			{
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""pegasus"""
   __lowerCAmelCase				=							["""past_key_values"""]
   __lowerCAmelCase				=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
   def __init__(     self      ,       _a=50_265      ,       _a=1_024      ,       _a=12      ,       _a=4_096      ,       _a=16      ,       _a=12      ,       _a=4_096      ,       _a=16      ,       _a=0.0      ,       _a=0.0      ,       _a=True      ,       _a=True      ,       _a="gelu"      ,       _a=1_024      ,       _a=0.1      ,       _a=0.0      ,       _a=0.0      ,       _a=0.0_2      ,       _a=0      ,       _a=False      ,       _a=0      ,       _a=1      ,       _a=1      ,       **_a      ,       ):
          lowercase       :							Optional[int]									=				vocab_size
          lowercase       :							str									=				max_position_embeddings
          lowercase       :							Tuple									=				d_model
          lowercase       :							int									=				encoder_ffn_dim
          lowercase       :							List[str]									=				encoder_layers
          lowercase       :							Union[str, Any]									=				encoder_attention_heads
          lowercase       :							Tuple									=				decoder_ffn_dim
          lowercase       :							Dict									=				decoder_layers
          lowercase       :							Any									=				decoder_attention_heads
          lowercase       :							Optional[Any]									=				dropout
          lowercase       :							Tuple									=				attention_dropout
          lowercase       :							str									=				activation_dropout
          lowercase       :							str									=				activation_function
          lowercase       :							Any									=				init_std
          lowercase       :							List[Any]									=				encoder_layerdrop
          lowercase       :							Dict									=				decoder_layerdrop
          lowercase       :							List[Any]									=				use_cache
          lowercase       :							Any									=				encoder_layers
          lowercase       :							Optional[int]									=				scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=_a      ,       eos_token_id=_a      ,       is_encoder_decoder=_a      ,       decoder_start_token_id=_a      ,       forced_eos_token_id=_a      ,       **_a      ,       )
   @property
   def      __magic_name__     (     self							):
          return self.encoder_attention_heads

   @property
   def      __magic_name__     (     self							):
          return self.d_model




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A     :			Optional[Any]  						=			{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			["""BridgeTowerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BridgeTowerForContrastiveLearning""",
       """BridgeTowerForImageAndTextRetrieval""",
       """BridgeTowerForMaskedLM""",
       """BridgeTowerModel""",
       """BridgeTowerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _A     :			Any  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


