code: 
  
  
  
  import unittest
  
  from transformers.image_processing_utils import get_size_dict
  
  
  
  class 		_snake_case      (			unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    {'''wrong_key''': 2_2_4}
     with self.assertRaises(a					):
       get_size_dict(a					)
  
     _A           =    {'''height''': 2_2_4}
     with self.assertRaises(a					):
       get_size_dict(a					)
  
     _A           =    {'''width''': 2_2_4, '''shortest_edge''': 2_2_4}
     with self.assertRaises(a					):
       get_size_dict(a					)
  
     # Test a dict with the correct keys is returned as is
     _A           =    {'''height''': 2_2_4, '''width''': 2_2_4}
     _A           =    get_size_dict(a					)
     self.assertEqual(a	,    a					)
  
     _A           =    {'''shortest_edge''': 2_2_4}
     _A           =    get_size_dict(a					)
     self.assertEqual(a	,    {'''shortest_edge''': 2_2_4}					)
  
     _A           =    {'''longest_edge''': 2_2_4, '''shortest_edge''': 2_2_4}
     _A           =    get_size_dict(a					)
     self.assertEqual(a	,    {'''longest_edge''': 2_2_4, '''shortest_edge''': 2_2_4}					)
  
     # Test a single int value which  represents (size, size)
     _A           =    get_size_dict(2_2_4					)
     self.assertEqual(a	,    {'''height''': 2_2_4, '''width''': 2_2_4}					)
  
     # Test a single int value which represents the shortest edge
     _A           =    get_size_dict(2_2_4	,    default_to_square=a					)
     self.assertEqual(a	,    {'''shortest_edge''': 2_2_4}					)
  
     # Test a tuple of ints which represents (height, width)
     _A           =    get_size_dict((1_5_0, 2_0_0)					)
     self.assertEqual(a	,    {'''height''': 1_5_0, '''width''': 2_0_0}					)
  
     # Test a tuple of ints which represents (width, height)
     _A           =    get_size_dict((1_5_0, 2_0_0)	,    height_width_order=a					)
     self.assertEqual(a	,    {'''height''': 2_0_0, '''width''': 1_5_0}					)
  
     # Test an int representing the shortest edge and max_size which represents the longest edge
     _A           =    get_size_dict(2_2_4	,    max_size=2_5_6	,    default_to_square=a					)
     self.assertEqual(a	,    {'''shortest_edge''': 2_2_4, '''longest_edge''': 2_5_6}					)
  
     # Test int with default_to_square=True and max_size fails
     with self.assertRaises(a					):
       get_size_dict(2_2_4	,    max_size=2_5_6	,    default_to_square=a					)
style_context: 
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{}
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt_decoder'''
   def __init__(						self	,    a=0	,    a=0					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
     _A           =    vocab_size
     _A           =    bos_token_id
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt'''
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    langs
     _A           =    src_vocab_size
     _A           =    tgt_vocab_size
     _A           =    d_model  # encoder_embed_dim and decoder_embed_dim
  
     _A           =    encoder_ffn_dim
     _A           =    _A           =    encoder_layers
     _A           =    encoder_attention_heads
     _A           =    encoder_layerdrop
     _A           =    decoder_layerdrop
     _A           =    decoder_ffn_dim
     _A           =    decoder_layers
     _A           =    decoder_attention_heads
     _A           =    max_position_embeddings
     _A           =    init_std  # Normal(0, this parameter)
     _A           =    activation_function
  
     _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
     if "decoder" in common_kwargs:
       del common_kwargs["decoder"]
  
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
     # 3 Types of Dropout
     _A           =    attention_dropout
     _A           =    activation_dropout
     _A           =    dropout
  
     _A           =    use_cache
     super().__init__(
         pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    copy.deepcopy(self.__dict__					)
     _A           =    self.decoder.to_dict()
     _A           =    self.__class__.model_type
     return output