import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCamelCase      :			Union[str, Any]							       =		logging.get_logger(__name__)  # pylint: disable=invalid-name







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    Optional[int]	,     _lowercase      :    str	,     _lowercase      :    Tuple=7_68				):



     """simple docstring"""






     super().__init__(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				proj_size

     SCREAMING_SNAKE_CASE__      							=				CLIPVisionModel(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				PaintByExampleMapper(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				nn.LayerNorm(config.hidden_size				)
     SCREAMING_SNAKE_CASE__      							=				nn.Linear(config.hidden_size	,     self.proj_size				)

     # uncondition for scaling
     SCREAMING_SNAKE_CASE__      							=				nn.Parameter(torch.randn((1, 1, self.proj_size)				)				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    int=False				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model(pixel_values=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				clip_output.pooler_output
     SCREAMING_SNAKE_CASE__      							=				self.mapper(latent_states[:, None]				)
     SCREAMING_SNAKE_CASE__      							=				self.final_layer_norm(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.proj_out(_lowercase				)
     if return_uncond_vector:
       return latent_states, self.uncond_vector

     return latent_states







class __snake_case    (						nn.Module		):




   def __init__(   self      :    Tuple	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__      							=				(config.num_hidden_layers + 1) // 5
     SCREAMING_SNAKE_CASE__      							=				config.hidden_size
     SCREAMING_SNAKE_CASE__      							=				1
     SCREAMING_SNAKE_CASE__      							=				nn.ModuleList(
         [
             BasicTransformerBlock(_lowercase	,     _lowercase	,     _lowercase	,     activation_fn="""gelu"""	,     attention_bias=_lowercase				)
             for _ in range(_lowercase				)
         ]				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    List[str]				):



     """simple docstring"""






     for block in self.blocks:
       SCREAMING_SNAKE_CASE__      							=				block(_lowercase				)

     return hidden_states

from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
