from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE :	Dict							     =		{
    """configuration_blip_2""": [
        """BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Blip2Config""",
        """Blip2QFormerConfig""",
        """Blip2VisionConfig""",
    ],
    """processing_blip_2""": ["""Blip2Processor"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE :	Dict							     =		[
		    """BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Blip2Model""",
		    """Blip2QFormerModel""",
		    """Blip2PreTrainedModel""",
		    """Blip2ForConditionalGeneration""",
		    """Blip2VisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

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



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    A_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  TaConfig(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							d_model=__SCREAMING_SNAKE_CASE     ,							num_heads=__SCREAMING_SNAKE_CASE     ,							d_kv=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE     ,							feed_forward_proj=__SCREAMING_SNAKE_CASE     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  TaBlock(__SCREAMING_SNAKE_CASE       )
    self.encoders.append(__SCREAMING_SNAKE_CASE       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  self.token_embedder(__SCREAMING_SNAKE_CASE       )

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

   __a       =  self.dropout_pre(__SCREAMING_SNAKE_CASE       )

   # inverted the attention mask
   __a       =  encoder_input_tokens.size()
   __a       =  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   for lyr in self.encoders:
    __a       =  lyr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )[0]
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   return self.dropout_post(__SCREAMING_SNAKE_CASE       ), encoder_inputs_mask


