code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  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       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   @register_to_config
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A = False				,					)      ->  Tuple:
       super().__init__()
  
       a					=nn.Linear(__A				,					__A				,					bias=__A		)
  
       a					=nn.Embedding(__A				,					__A		)
       a					=False
  
       a					=nn.Dropout(p=__A		)
  
       a					=TaConfig(
           d_model=__A				,					num_heads=__A				,					d_kv=__A				,					d_ff=__A				,					feed_forward_proj=__A				,					dropout_rate=__A				,					is_decoder=__A				,					is_encoder_decoder=__A				,					)
       a					=nn.ModuleList()
       for lyr_num in range(__A		):
           a					=TaBlock(__A		)
           self.encoders.append(__A		)
  
       a					=TaLayerNorm(__A		)
       a					=nn.Dropout(p=__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  List[Any]:
       a					=self.input_proj(__A		)
  
       # terminal relative positional encodings
       a					=encoder_inputs.shape[1]
       a					=torch.arange(__A				,					device=encoder_inputs.device		)
  
       a					=encoder_inputs_mask.sum(-1		)
       a					=torch.roll(input_positions.unsqueeze(0		)				,					tuple(seq_lens.tolist()		)				,					dims=0		)
       x += self.position_encoding(__A		)
  
       a					=self.dropout_pre(__A		)
  
       # inverted the attention mask
       a					=encoder_inputs.size()
       a					=self.get_extended_attention_mask(__A				,					__A		)
  
       for lyr in self.encoders:
           a					=lyr(__A				,					__A		)[0]
       a					=self.layer_norm(__A		)
  
       return self.dropout_post(__A		), encoder_inputs_mask
  
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Tuple    		=						{
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"mobilevitv2"
  
  
   def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
       super().__init__(**__A		)
  
       a					=num_channels
       a					=image_size
       a					=patch_size
       a					=expand_ratio
       a					=hidden_act
       a					=conv_kernel_size
       a					=output_stride
       a					=initializer_range
       a					=layer_norm_eps
       a					=n_attn_blocks
       a					=base_attn_unit_dims
       a					=width_multiplier
       a					=ffn_multiplier
       a					=ffn_dropout
       a					=attn_dropout
       a					=classifier_dropout_prob
  
       # decode head attributes for semantic segmentation
       a					=aspp_out_channels
       a					=atrous_rates
       a					=aspp_dropout_prob
       a					=semantic_loss_ignore_index
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						version.parse("1.11"    )
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]		)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)
  
  
  
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
       return 1E-4