




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowerCamelCase__			:       str         =							{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "albert"



 def __init__(							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		str=30_000    ,    _lowerCAmelCase  :		Optional[Any]=128    ,    _lowerCAmelCase  :		Any=4_096    ,    _lowerCAmelCase  :		Optional[int]=12    ,    _lowerCAmelCase  :		Tuple=1    ,    _lowerCAmelCase  :		Optional[Any]=64    ,    _lowerCAmelCase  :		Dict=16_384    ,    _lowerCAmelCase  :		Union[str, Any]=1    ,    _lowerCAmelCase  :		Any="gelu_new"    ,    _lowerCAmelCase  :		int=0    ,    _lowerCAmelCase  :		List[Any]=0    ,    _lowerCAmelCase  :		Tuple=512    ,    _lowerCAmelCase  :		int=2    ,    _lowerCAmelCase  :		Dict=0.02    ,    _lowerCAmelCase  :		Union[str, Any]=1E-12    ,    _lowerCAmelCase  :		List[Any]=0.1    ,    _lowerCAmelCase  :		Union[str, Any]="absolute"    ,    _lowerCAmelCase  :		List[Any]=0    ,    _lowerCAmelCase  :		Optional[Any]=2    ,    _lowerCAmelCase  :		Tuple=3    ,    **_lowerCAmelCase  :		Optional[Any]    ,    ):
       super().__init__(pad_token_id=_lowerCAmelCase    ,    bos_token_id=_lowerCAmelCase    ,    eos_token_id=_lowerCAmelCase    ,    **_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      vocab_size
       SCREAMING_SNAKE_CASE_     						=      embedding_size
       SCREAMING_SNAKE_CASE_     						=      hidden_size
       SCREAMING_SNAKE_CASE_     						=      num_hidden_layers
       SCREAMING_SNAKE_CASE_     						=      num_hidden_groups
       SCREAMING_SNAKE_CASE_     						=      num_attention_heads
       SCREAMING_SNAKE_CASE_     						=      inner_group_num
       SCREAMING_SNAKE_CASE_     						=      hidden_act
       SCREAMING_SNAKE_CASE_     						=      intermediate_size
       SCREAMING_SNAKE_CASE_     						=      hidden_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      max_position_embeddings
       SCREAMING_SNAKE_CASE_     						=      type_vocab_size
       SCREAMING_SNAKE_CASE_     						=      initializer_range
       SCREAMING_SNAKE_CASE_     						=      layer_norm_eps
       SCREAMING_SNAKE_CASE_     						=      classifier_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      position_embedding_type





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @property
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_     						=      {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             SCREAMING_SNAKE_CASE_     						=      {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]						)





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

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


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Tuple=768						):
       super().__init__(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      proj_size

       SCREAMING_SNAKE_CASE_     						=      CLIPVisionModel(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      PaintByExampleMapper(_lowerCAmelCase						)
       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 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int=False						):
       SCREAMING_SNAKE_CASE_     						=      self.model(pixel_values=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      clip_output.pooler_output
       SCREAMING_SNAKE_CASE_     						=      self.mapper(latent_states[:, None]						)
       SCREAMING_SNAKE_CASE_     						=      self.final_layer_norm(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.proj_out(_lowerCAmelCase						)
       if return_uncond_vector:
             return latent_states, self.uncond_vector

       return latent_states





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		int    ,    _lowerCAmelCase  :		Optional[Any]						):
       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(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    activation_fn='gelu'    ,    attention_bias=_lowerCAmelCase						)
               for _ in range(_lowerCAmelCase						)
           ]						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[Any]						):
       for block in self.blocks:
             SCREAMING_SNAKE_CASE_     						=      block(_lowerCAmelCase						)

       return hidden_states