code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import unittest
  from dataclasses import dataclass
  from typing import List, Union
  
  import numpy as np
  import PIL.Image
  
  from diffusers.utils.outputs import BaseOutput
  
  
  
  
  
  
  @dataclass
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						42
  
  
  
  
  
  
  class    __A       ( unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       a					=CustomOutput(images=np.random.rand(1				,					3				,					4				,					4		)		)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,					np.ndarray		)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs['''images''']				,					np.ndarray		)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]				,					np.ndarray		)
       assert outputs[0].shape == (1, 3, 4, 4)
  
       # test with a non-tensor attribute
       a					=CustomOutput(images=[PIL.Image.new('''RGB'''				,					(4, 4)		)]		)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,					UpperCamelCase_		)
       assert isinstance(outputs.images[0]				,					PIL.Image.Image		)
       assert isinstance(outputs['''images''']				,					UpperCamelCase_		)
       assert isinstance(outputs['''images'''][0]				,					PIL.Image.Image		)
       assert isinstance(outputs[0]				,					UpperCamelCase_		)
       assert isinstance(outputs[0][0]				,					PIL.Image.Image		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       # test output reinitialization with a `dict` for compatibility with `accelerate`
       a					=CustomOutput({'''images''': np.random.rand(1				,					3				,					4				,					4		)}		)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,					np.ndarray		)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs['''images''']				,					np.ndarray		)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]				,					np.ndarray		)
       assert outputs[0].shape == (1, 3, 4, 4)
  
       # test with a non-tensor attribute
       a					=CustomOutput({'''images''': [PIL.Image.new('''RGB'''				,					(4, 4)		)]}		)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,					UpperCamelCase_		)
       assert isinstance(outputs.images[0]				,					PIL.Image.Image		)
       assert isinstance(outputs['''images''']				,					UpperCamelCase_		)
       assert isinstance(outputs['''images'''][0]				,					PIL.Image.Image		)
       assert isinstance(outputs[0]				,					UpperCamelCase_		)
       assert isinstance(outputs[0][0]				,					PIL.Image.Image		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images