


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class       lowercase_					(lowercase__      ,					lowercase__      ,					lowercase__   ):





				@register_to_config
				def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = False       ,  )  -> str:
											super().__init__()

											a__      =nn.Embedding(lowercase_       ,  lowercase_)

											a__      =nn.Embedding(lowercase_       ,  lowercase_)
											a__      =False

											a__      =nn.Dropout(p=lowercase_)

											a__      =TaConfig(
											    vocab_size=lowercase_       ,  d_model=lowercase_       ,  num_heads=lowercase_       ,  d_kv=lowercase_       ,  d_ff=lowercase_       ,  dropout_rate=lowercase_       ,  feed_forward_proj=lowercase_       ,  is_decoder=lowercase_       ,  is_encoder_decoder=lowercase_       ,  )

											a__      =nn.ModuleList()
											for lyr_num in range(lowercase_):
																		a__      =TaBlock(lowercase_)
																		self.encoders.append(lowercase_)

											a__      =TaLayerNorm(lowercase_)
											a__      =nn.Dropout(p=lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> Tuple:
											a__      =self.token_embedder(lowercase_)

											a__      =encoder_input_tokens.shape[1]
											a__      =torch.arange(lowercase_       ,  device=encoder_input_tokens.device)
											x += self.position_encoding(lowercase_)

											a__      =self.dropout_pre(lowercase_)

											# inverted the attention mask
											a__      =encoder_input_tokens.size()
											a__      =self.get_extended_attention_mask(lowercase_       ,  lowercase_)

											for lyr in self.encoders:
																		a__      =lyr(lowercase_       ,  lowercase_)[0]
											a__      =self.layer_norm(lowercase_)

											return self.dropout_post(lowercase_), encoder_inputs_mask


from typing import TYPE_CHECKING

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


a  :      int            =    {
    """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  :      str            =    [
     """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  :      List[Any]            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)